diff --git a/Agriculture/Analysis of the movement and activity of free-ranging cattle/notebook.ipynb b/Agriculture/Analysis of the movement and activity of free-ranging cattle/notebook.ipynb
index 73183df361c50f0966960fcb9af297b47d952b62..1ae0f8518a063381dd47fd58fec2ed8ba4708352 100644
--- a/Agriculture/Analysis of the movement and activity of free-ranging cattle/notebook.ipynb	
+++ b/Agriculture/Analysis of the movement and activity of free-ranging cattle/notebook.ipynb	
@@ -1,29561 +1,29568 @@
 {
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Analyse der Bewegung und Aktivit\u00e4t von freilaufenden Rindern"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# 1. Business Understanding\n",
-                "Landwirte wollen analysieren, wie sich ihr Vieh auf der Weide bewegt und verh\u00e4lt. (siehe Readme f\u00fcr weitere Informationen)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# 2. Daten und Datenverst\u00e4ndnis\n",
-                "## 2.1. Import von relevanten Daten "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 34,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import numpy as np # standard for data processing\n",
-                "import pandas as pd # standard for data processing\n",
-                "\n",
-                "import plotly.graph_objects as go # creates plots\n",
-                "import seaborn as sns\n",
-                "from matplotlib import pyplot as plt\n",
-                "import plotly.express as px\n",
-                "from mpl_toolkits.mplot3d import Axes3D\n",
-                "\n",
-                "from sklearn.decomposition import PCA\n",
-                "from sklearn.preprocessing import StandardScaler\n",
-                "from imblearn.over_sampling import RandomOverSampler\n",
-                "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
-                "from sklearn.ensemble import RandomForestClassifier\n",
-                "\n",
-                "from sklearn.multiclass import OneVsRestClassifier\n",
-                "from sklearn.linear_model import LogisticRegression\n",
-                "from sklearn.neighbors import KNeighborsClassifier\n",
-                "from sklearn import svm\n",
-                "from sklearn.tree import DecisionTreeClassifier\n",
-                "from sklearn.pipeline import Pipeline\n",
-                "from sklearn.model_selection import GridSearchCV\n",
-                "from sklearn.preprocessing import StandardScaler\n",
-                "from sklearn.metrics import confusion_matrix, classification_report"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Daten lesen"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Quelle der Daten:\n",
-                "\n",
-                "Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra und Jorge L. Reyes-Ortiz. Ein \u00f6ffentlich zug\u00e4nglicher Datensatz f\u00fcr die Erkennung menschlicher Aktivit\u00e4ten mit Smartphones. 21. Europ\u00e4isches Symposium \u00fcber K\u00fcnstliche Neuronale Netze, Computational Intelligence und Maschinelles Lernen, ESANN 2013. Br\u00fcgge, Belgien 24-26 April 2013.\n",
-                "Heruntergeladen von Kaggle: https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones\n"
-            ]
-        },
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyse der Bewegung und AktivitÃ¤t von freilaufenden Rindern"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Business Understanding\n",
+    "Landwirte wollen analysieren, wie sich ihr Vieh auf der Weide bewegt und verhÃ¤lt. (siehe Readme fÃ¼r weitere Informationen)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Daten und DatenverstÃ¤ndnis\n",
+    "## 2.1. Import von relevanten Daten "
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": ""
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np # standard for data processing\n",
+    "import pandas as pd # standard for data processing\n",
+    "\n",
+    "import plotly.graph_objects as go # creates plots\n",
+    "import seaborn as sns\n",
+    "from matplotlib import pyplot as plt\n",
+    "import plotly.express as px\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "from sklearn.multiclass import OneVsRestClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn import svm\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import confusion_matrix, classification_report"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Daten lesen"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quelle der Daten:\n",
+    "\n",
+    "Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra und Jorge L. Reyes-Ortiz. Ein Ã¶ffentlich zugÃ¤nglicher Datensatz fÃ¼r die Erkennung menschlicher AktivitÃ¤ten mit Smartphones. 21. EuropÃ¤isches Symposium Ã¼ber KÃ¼nstliche Neuronale Netze, Computational Intelligence und Maschinelles Lernen, ESANN 2013. BrÃ¼gge, Belgien 24-26 April 2013.\n",
+    "Heruntergeladen von Kaggle: https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Es gibt zwei getrennte DatensÃ¤tze aus der Testgruppe und der Trainingsgruppe. Diese werden fÃ¼r die weitere Analyse zunÃ¤chst noch einmal zusammengefasst. Die Daten werden identifiziert, ob sie aus dem Trainings- oder Testdatensatz stammen, damit sie spÃ¤ter wieder aufgeteilt werden kÃ¶nnen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Analysis_of_the_movement_and_activity_of_free-ranging_cattle_test.csv', delimiter=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Analysis_of_the_movement_and_activity_of_free-ranging_cattle_test.csv', delimiter=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train['type']='train'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test['type']='test'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat([df_train, df_test], axis=0).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tBodyAcc-mean()-X</th>\n",
+       "      <th>tBodyAcc-mean()-Y</th>\n",
+       "      <th>tBodyAcc-mean()-Z</th>\n",
+       "      <th>tBodyAcc-std()-X</th>\n",
+       "      <th>tBodyAcc-std()-Y</th>\n",
+       "      <th>tBodyAcc-std()-Z</th>\n",
+       "      <th>tBodyAcc-mad()-X</th>\n",
+       "      <th>tBodyAcc-mad()-Y</th>\n",
+       "      <th>tBodyAcc-mad()-Z</th>\n",
+       "      <th>tBodyAcc-max()-X</th>\n",
+       "      <th>...</th>\n",
+       "      <th>angle(tBodyAccMean,gravity)</th>\n",
+       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
+       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
+       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
+       "      <th>angle(X,gravityMean)</th>\n",
+       "      <th>angle(Y,gravityMean)</th>\n",
+       "      <th>angle(Z,gravityMean)</th>\n",
+       "      <th>subject</th>\n",
+       "      <th>Activity</th>\n",
+       "      <th>type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.288585</td>\n",
+       "      <td>-0.020294</td>\n",
+       "      <td>-0.132905</td>\n",
+       "      <td>-0.995279</td>\n",
+       "      <td>-0.983111</td>\n",
+       "      <td>-0.913526</td>\n",
+       "      <td>-0.995112</td>\n",
+       "      <td>-0.983185</td>\n",
+       "      <td>-0.923527</td>\n",
+       "      <td>-0.934724</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.112754</td>\n",
+       "      <td>0.030400</td>\n",
+       "      <td>-0.464761</td>\n",
+       "      <td>-0.018446</td>\n",
+       "      <td>-0.841247</td>\n",
+       "      <td>0.179941</td>\n",
+       "      <td>-0.058627</td>\n",
+       "      <td>1</td>\n",
+       "      <td>STANDING</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.278419</td>\n",
+       "      <td>-0.016411</td>\n",
+       "      <td>-0.123520</td>\n",
+       "      <td>-0.998245</td>\n",
+       "      <td>-0.975300</td>\n",
+       "      <td>-0.960322</td>\n",
+       "      <td>-0.998807</td>\n",
+       "      <td>-0.974914</td>\n",
+       "      <td>-0.957686</td>\n",
+       "      <td>-0.943068</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.053477</td>\n",
+       "      <td>-0.007435</td>\n",
+       "      <td>-0.732626</td>\n",
+       "      <td>0.703511</td>\n",
+       "      <td>-0.844788</td>\n",
+       "      <td>0.180289</td>\n",
+       "      <td>-0.054317</td>\n",
+       "      <td>1</td>\n",
+       "      <td>STANDING</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.279653</td>\n",
+       "      <td>-0.019467</td>\n",
+       "      <td>-0.113462</td>\n",
+       "      <td>-0.995380</td>\n",
+       "      <td>-0.967187</td>\n",
+       "      <td>-0.978944</td>\n",
+       "      <td>-0.996520</td>\n",
+       "      <td>-0.963668</td>\n",
+       "      <td>-0.977469</td>\n",
+       "      <td>-0.938692</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.118559</td>\n",
+       "      <td>0.177899</td>\n",
+       "      <td>0.100699</td>\n",
+       "      <td>0.808529</td>\n",
+       "      <td>-0.848933</td>\n",
+       "      <td>0.180637</td>\n",
+       "      <td>-0.049118</td>\n",
+       "      <td>1</td>\n",
+       "      <td>STANDING</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.279174</td>\n",
+       "      <td>-0.026201</td>\n",
+       "      <td>-0.123283</td>\n",
+       "      <td>-0.996091</td>\n",
+       "      <td>-0.983403</td>\n",
+       "      <td>-0.990675</td>\n",
+       "      <td>-0.997099</td>\n",
+       "      <td>-0.982750</td>\n",
+       "      <td>-0.989302</td>\n",
+       "      <td>-0.938692</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.036788</td>\n",
+       "      <td>-0.012892</td>\n",
+       "      <td>0.640011</td>\n",
+       "      <td>-0.485366</td>\n",
+       "      <td>-0.848649</td>\n",
+       "      <td>0.181935</td>\n",
+       "      <td>-0.047663</td>\n",
+       "      <td>1</td>\n",
+       "      <td>STANDING</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.276629</td>\n",
+       "      <td>-0.016570</td>\n",
+       "      <td>-0.115362</td>\n",
+       "      <td>-0.998139</td>\n",
+       "      <td>-0.980817</td>\n",
+       "      <td>-0.990482</td>\n",
+       "      <td>-0.998321</td>\n",
+       "      <td>-0.979672</td>\n",
+       "      <td>-0.990441</td>\n",
+       "      <td>-0.942469</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.123320</td>\n",
+       "      <td>0.122542</td>\n",
+       "      <td>0.693578</td>\n",
+       "      <td>-0.615971</td>\n",
+       "      <td>-0.847865</td>\n",
+       "      <td>0.185151</td>\n",
+       "      <td>-0.043892</td>\n",
+       "      <td>1</td>\n",
+       "      <td>STANDING</td>\n",
+       "      <td>train</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 564 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
+       "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
+       "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
+       "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
+       "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
+       "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
+       "\n",
+       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
+       "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
+       "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
+       "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
+       "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
+       "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
+       "\n",
+       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  angle(tBodyAccMean,gravity)  \\\n",
+       "0         -0.923527         -0.934724  ...                    -0.112754   \n",
+       "1         -0.957686         -0.943068  ...                     0.053477   \n",
+       "2         -0.977469         -0.938692  ...                    -0.118559   \n",
+       "3         -0.989302         -0.938692  ...                    -0.036788   \n",
+       "4         -0.990441         -0.942469  ...                     0.123320   \n",
+       "\n",
+       "   angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
+       "0                              0.030400                         -0.464761   \n",
+       "1                             -0.007435                         -0.732626   \n",
+       "2                              0.177899                          0.100699   \n",
+       "3                             -0.012892                          0.640011   \n",
+       "4                              0.122542                          0.693578   \n",
+       "\n",
+       "   angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
+       "0                             -0.018446             -0.841247   \n",
+       "1                              0.703511             -0.844788   \n",
+       "2                              0.808529             -0.848933   \n",
+       "3                             -0.485366             -0.848649   \n",
+       "4                             -0.615971             -0.847865   \n",
+       "\n",
+       "   angle(Y,gravityMean)  angle(Z,gravityMean)  subject  Activity   type  \n",
+       "0              0.179941             -0.058627        1  STANDING  train  \n",
+       "1              0.180289             -0.054317        1  STANDING  train  \n",
+       "2              0.180637             -0.049118        1  STANDING  train  \n",
+       "3              0.181935             -0.047663        1  STANDING  train  \n",
+       "4              0.185151             -0.043892        1  STANDING  train  \n",
+       "\n",
+       "[5 rows x 564 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. Datenexploration und -aufbereitung"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of observations: 10299\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Total number of observations: ' + str(df.shape[0]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Auf fehlende Werte prÃ¼fen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tBodyAcc-mean()-X       0\n",
+       "tBodyAcc-mean()-Y       0\n",
+       "tBodyAcc-mean()-Z       0\n",
+       "tBodyAcc-std()-X        0\n",
+       "tBodyAcc-std()-Y        0\n",
+       "                       ..\n",
+       "angle(Y,gravityMean)    0\n",
+       "angle(Z,gravityMean)    0\n",
+       "subject                 0\n",
+       "Activity                0\n",
+       "type                    0\n",
+       "Length: 564, dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isna().sum().sum()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "--> **Keine fehlenden Werte im Data Frame"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Auf Duplikate prÃ¼fen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tBodyAcc-mean()-X</th>\n",
+       "      <th>tBodyAcc-mean()-Y</th>\n",
+       "      <th>tBodyAcc-mean()-Z</th>\n",
+       "      <th>tBodyAcc-std()-X</th>\n",
+       "      <th>tBodyAcc-std()-Y</th>\n",
+       "      <th>tBodyAcc-std()-Z</th>\n",
+       "      <th>tBodyAcc-mad()-X</th>\n",
+       "      <th>tBodyAcc-mad()-Y</th>\n",
+       "      <th>tBodyAcc-mad()-Z</th>\n",
+       "      <th>tBodyAcc-max()-X</th>\n",
+       "      <th>...</th>\n",
+       "      <th>angle(tBodyAccMean,gravity)</th>\n",
+       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
+       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
+       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
+       "      <th>angle(X,gravityMean)</th>\n",
+       "      <th>angle(Y,gravityMean)</th>\n",
+       "      <th>angle(Z,gravityMean)</th>\n",
+       "      <th>subject</th>\n",
+       "      <th>Activity</th>\n",
+       "      <th>type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows Ã— 564 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [tBodyAcc-mean()-X, tBodyAcc-mean()-Y, tBodyAcc-mean()-Z, tBodyAcc-std()-X, tBodyAcc-std()-Y, tBodyAcc-std()-Z, tBodyAcc-mad()-X, tBodyAcc-mad()-Y, tBodyAcc-mad()-Z, tBodyAcc-max()-X, tBodyAcc-max()-Y, tBodyAcc-max()-Z, tBodyAcc-min()-X, tBodyAcc-min()-Y, tBodyAcc-min()-Z, tBodyAcc-sma(), tBodyAcc-energy()-X, tBodyAcc-energy()-Y, tBodyAcc-energy()-Z, tBodyAcc-iqr()-X, tBodyAcc-iqr()-Y, tBodyAcc-iqr()-Z, tBodyAcc-entropy()-X, tBodyAcc-entropy()-Y, tBodyAcc-entropy()-Z, tBodyAcc-arCoeff()-X,1, tBodyAcc-arCoeff()-X,2, tBodyAcc-arCoeff()-X,3, tBodyAcc-arCoeff()-X,4, tBodyAcc-arCoeff()-Y,1, tBodyAcc-arCoeff()-Y,2, tBodyAcc-arCoeff()-Y,3, tBodyAcc-arCoeff()-Y,4, tBodyAcc-arCoeff()-Z,1, tBodyAcc-arCoeff()-Z,2, tBodyAcc-arCoeff()-Z,3, tBodyAcc-arCoeff()-Z,4, tBodyAcc-correlation()-X,Y, tBodyAcc-correlation()-X,Z, tBodyAcc-correlation()-Y,Z, tGravityAcc-mean()-X, tGravityAcc-mean()-Y, tGravityAcc-mean()-Z, tGravityAcc-std()-X, tGravityAcc-std()-Y, tGravityAcc-std()-Z, tGravityAcc-mad()-X, tGravityAcc-mad()-Y, tGravityAcc-mad()-Z, tGravityAcc-max()-X, tGravityAcc-max()-Y, tGravityAcc-max()-Z, tGravityAcc-min()-X, tGravityAcc-min()-Y, tGravityAcc-min()-Z, tGravityAcc-sma(), tGravityAcc-energy()-X, tGravityAcc-energy()-Y, tGravityAcc-energy()-Z, tGravityAcc-iqr()-X, tGravityAcc-iqr()-Y, tGravityAcc-iqr()-Z, tGravityAcc-entropy()-X, tGravityAcc-entropy()-Y, tGravityAcc-entropy()-Z, tGravityAcc-arCoeff()-X,1, tGravityAcc-arCoeff()-X,2, tGravityAcc-arCoeff()-X,3, tGravityAcc-arCoeff()-X,4, tGravityAcc-arCoeff()-Y,1, tGravityAcc-arCoeff()-Y,2, tGravityAcc-arCoeff()-Y,3, tGravityAcc-arCoeff()-Y,4, tGravityAcc-arCoeff()-Z,1, tGravityAcc-arCoeff()-Z,2, tGravityAcc-arCoeff()-Z,3, tGravityAcc-arCoeff()-Z,4, tGravityAcc-correlation()-X,Y, tGravityAcc-correlation()-X,Z, tGravityAcc-correlation()-Y,Z, tBodyAccJerk-mean()-X, tBodyAccJerk-mean()-Y, tBodyAccJerk-mean()-Z, tBodyAccJerk-std()-X, tBodyAccJerk-std()-Y, tBodyAccJerk-std()-Z, tBodyAccJerk-mad()-X, tBodyAccJerk-mad()-Y, tBodyAccJerk-mad()-Z, tBodyAccJerk-max()-X, tBodyAccJerk-max()-Y, tBodyAccJerk-max()-Z, tBodyAccJerk-min()-X, tBodyAccJerk-min()-Y, tBodyAccJerk-min()-Z, tBodyAccJerk-sma(), tBodyAccJerk-energy()-X, tBodyAccJerk-energy()-Y, tBodyAccJerk-energy()-Z, tBodyAccJerk-iqr()-X, ...]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 564 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.duplicated()]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "--> **keine Duplikatzeilen** in den Daten"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Ziel Variable"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wir haben ein Klassifizierungsproblem und die Zielspalte ist die Spalte \"AktivitÃ¤t\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['STANDING', 'SITTING', 'LAYING', 'WALKING', 'WALKING_DOWNSTAIRS',\n",
+       "       'WALKING_UPSTAIRS'], dtype=object)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Possible classes/labels\n",
+    "df['Activity'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Activity'].value_counts().plot.bar()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Im Smart Farming Kontext dieser Aufgabe wÃ¼rde ein Rind normalerweise nicht auf einer Treppe laufen. Daher werden die Zeilen mit den Bezeichnungen \"WALKING_DOWNSTAIRS\" und \"WALKING_UPSTAIRS\" entfernt:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "indexNames = df[(df['Activity'] == 'WALKING_DOWNSTAIRS') | (df['Activity'] == 'WALKING_UPSTAIRS')].index\n",
+    "df.drop(indexNames , inplace=True)\n",
+    "df = df.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Activity'].value_counts().plot.bar()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- FÃ¼r jede AktivitÃ¤t etwa die gleiche Anzahl von Beobachtungen. Wir kÃ¶nnten vor der Modellierung eine Ãœberstichprobe aller Minderheitenklassen nehmen, um einen perfekt ausgewogenen Datensatz zu erhalten."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Wie viele Beobachtungen gibt es von jedem Testobjekt??"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD7CAYAAACRxdTpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVXUlEQVR4nO3df7DddX3n8efLBCk0iIFcIEIwDBtroV3C9pp2lu7CFkdS3W7ojuwEZ2x03E13FoVaO1uo3UXdpmKn0u2uhZ0oatQizda2pF2tTVNc110kXBCBEDKm/EwT4dYfVccOLfGzf5xvpsdvzrn3nHvvSc798HzMfOd8v5/z/n7P5/vjvr7f+73nnJtSCpKkurzoeHdAkrTwDHdJqpDhLkkVMtwlqUKGuyRVyHCXpAotPd4dAFixYkVZvXr18e6GJC0q991331+XUiZ6PTcW4b569WqmpqaOdzckaVFJ8mS/57wtI0kVMtwlqUKGuyRVyHCXpAoZ7pJUIcNdkipkuEtShQx3SarQWHyIqdvq6/9Xz/YnbnrdMe6JJC1eXrlLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekCs0a7kl+IMnuJF9OsifJu5v205LsTPKV5nF51zw3JNmfZF+SK0a5ApKkow1y5f4c8FOllIuAtcD6JD8BXA/sKqWsAXY10yS5ANgIXAisB25JsmQEfZck9TFruJeO7zSTJzRDATYA25r2bcCVzfgG4I5SynOllMeB/cC6hey0JGlmA91zT7IkyQPAs8DOUso9wJmllEMAzeMZTfnZwNNdsx9o2trL3JxkKsnU9PT0PFZBktQ2ULiXUg6XUtYC5wDrkvzIDOXptYgey9xaSpkspUxOTPT8/66SpDka6t0ypZRvAp+jcy/9mSQrAZrHZ5uyA8CqrtnOAQ7Ot6OSpMEN8m6ZiSQvbcZPAl4NPArsADY1ZZuAO5vxHcDGJCcmOQ9YA+xe4H5LkmYwyLdCrgS2Ne94eRGwvZTyJ0nuBrYneQvwFHAVQCllT5LtwCPA88A1pZTDo+m+JKmXWcO9lPIgcHGP9q8Bl/eZZwuwZd69kyTNiZ9QlaQKGe6SVCHDXZIqNHb/Zm8Y/ks+SeptUYf7MIY9EfSq96QhabHwtowkVchwl6QKvWBuy4zSMLdwvN0j6Vjwyl2SKmS4S1KFDHdJqpD33MeY7+OXNFdeuUtShQx3SaqQ4S5JFfKeeyW8Py+pm+H+AuT37Ej187aMJFXIcJekChnuklQhw12SKmS4S1KFfLeMFpTvrJHGg+Gu48YTgTQ6s96WSbIqyV1J9ibZk+S6pv1dSf4qyQPN8NqueW5Isj/JviRXjHIFJElHG+TK/XngHaWU+5OcAtyXZGfz3G+VUn6zuzjJBcBG4ELgZcCfJ3lFKeXwQnZcktTfrFfupZRDpZT7m/FvA3uBs2eYZQNwRynluVLK48B+YN1CdFaSNJih7rknWQ1cDNwDXAK8NcnPAVN0ru6/QSf4v9g12wF6nAySbAY2A5x77rlz6bteQPzuHGk4A78VMsky4FPAL5RSvgXcCpwPrAUOAe8/Utpj9nJUQylbSymTpZTJiYmJYfstSZrBQFfuSU6gE+y/W0r5A4BSyjNdz38Q+JNm8gCwqmv2c4CDC9JbaQBe5UsDhHuSALcBe0spN3e1ryylHGomfxZ4uBnfAdye5GY6f1BdA+xe0F5LC8QTgWo1yJX7JcAbgYeSPNC0/QpwdZK1dG65PAH8PEApZU+S7cAjdN5pc43vlJGkY2vWcC+lfIHe99E/PcM8W4At8+iXNJb84JUWC79bRpIqZLhLUoX8bhlpRLyFo+PJK3dJqpDhLkkV8raMNAa8haOF5pW7JFXIcJekChnuklQhw12SKuQfVKVFxi870yAMd6liw5wIPGnUxdsyklQhr9wlDc2r/PFnuEsaOT+kdex5W0aSKuSVu6Sx4lX+wvDKXZIqZLhLUoUMd0mqkPfcJS1aviWzP8Nd0gvCC+1EYLhLUksNX9vgPXdJqtCs4Z5kVZK7kuxNsifJdU37aUl2JvlK87i8a54bkuxPsi/JFaNcAUnS0Qa5cn8eeEcp5YeBnwCuSXIBcD2wq5SyBtjVTNM8txG4EFgP3JJkySg6L0nqbdZ77qWUQ8ChZvzbSfYCZwMbgMuasm3A54BfbtrvKKU8BzyeZD+wDrh7oTsvSYvNsfoE7lD33JOsBi4G7gHObIL/yAngjKbsbODprtkONG3tZW1OMpVkanp6eg5dlyT1M3C4J1kGfAr4hVLKt2Yq7dFWjmooZWspZbKUMjkxMTFoNyRJAxjorZBJTqAT7L9bSvmDpvmZJCtLKYeSrASebdoPAKu6Zj8HOLhQHZakF4r53MIZ5N0yAW4D9pZSbu56agewqRnfBNzZ1b4xyYlJzgPWALsH6o0kaUEMcuV+CfBG4KEkDzRtvwLcBGxP8hbgKeAqgFLKniTbgUfovNPmmlLK4YXuuCSpv0HeLfMFet9HB7i8zzxbgC3z6JckaR78hKokVchwl6QKGe6SVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqpDhLkkVMtwlqUKGuyRVyHCXpAoZ7pJUIcNdkipkuEtShQx3SaqQ4S5JFZo13JN8OMmzSR7uantXkr9K8kAzvLbruRuS7E+yL8kVo+q4JKm/Qa7cPwqs79H+W6WUtc3waYAkFwAbgQubeW5JsmShOitJGsys4V5K+Tzw9QGXtwG4o5TyXCnlcWA/sG4e/ZMkzcF87rm/NcmDzW2b5U3b2cDTXTUHmjZJ0jE013C/FTgfWAscAt7ftKdHbem1gCSbk0wlmZqenp5jNyRJvcwp3Espz5RSDpdSvgd8kH+49XIAWNVVeg5wsM8ytpZSJkspkxMTE3PphiSpjzmFe5KVXZM/Cxx5J80OYGOSE5OcB6wBds+vi5KkYS2drSDJJ4HLgBVJDgA3ApclWUvnlssTwM8DlFL2JNkOPAI8D1xTSjk8kp5LkvqaNdxLKVf3aL5thvotwJb5dEqSND9+QlWSKmS4S1KFDHdJqpDhLkkVMtwlqUKGuyRVyHCXpAoZ7pJUIcNdkipkuEtShQx3SaqQ4S5JFTLcJalChrskVchwl6QKGe6SVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekChnuklShWcM9yYeTPJvk4a6205LsTPKV5nF513M3JNmfZF+SK0bVcUlSf4NcuX8UWN9qux7YVUpZA+xqpklyAbARuLCZ55YkSxast5Kkgcwa7qWUzwNfbzVvALY149uAK7va7yilPFdKeRzYD6xbmK5KkgY113vuZ5ZSDgE0j2c07WcDT3fVHWjaJEnH0EL/QTU92krPwmRzkqkkU9PT0wvcDUl6YZtruD+TZCVA8/hs034AWNVVdw5wsNcCSilbSymTpZTJiYmJOXZDktTLXMN9B7CpGd8E3NnVvjHJiUnOA9YAu+fXRUnSsJbOVpDkk8BlwIokB4AbgZuA7UneAjwFXAVQStmTZDvwCPA8cE0p5fCI+i5J6mPWcC+lXN3nqcv71G8BtsynU5Kk+fETqpJUIcNdkipkuEtShQx3SaqQ4S5JFTLcJalChrskVchwl6QKGe6SVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqpDhLkkVMtwlqUKGuyRVyHCXpAotnc/MSZ4Avg0cBp4vpUwmOQ34PWA18ATwb0op35hfNyVJw1iIK/d/UUpZW0qZbKavB3aVUtYAu5ppSdIxNIrbMhuAbc34NuDKEbyGJGkG8w33AvxZkvuSbG7aziylHAJoHs/oNWOSzUmmkkxNT0/PsxuSpG7zuucOXFJKOZjkDGBnkkcHnbGUshXYCjA5OVnm2Q9JUpd5XbmXUg42j88CfwisA55JshKgeXx2vp2UJA1nzuGe5AeTnHJkHHgN8DCwA9jUlG0C7pxvJyVJw5nPbZkzgT9McmQ5t5dS/jTJvcD2JG8BngKumn83JUnDmHO4l1IeAy7q0f414PL5dEqSND9+QlWSKmS4S1KFDHdJqpDhLkkVMtwlqUKGuyRVyHCXpAoZ7pJUIcNdkipkuEtShQx3SaqQ4S5JFTLcJalChrskVchwl6QKGe6SVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekChnuklShkYV7kvVJ9iXZn+T6Ub2OJOloIwn3JEuA3wF+GrgAuDrJBaN4LUnS0UZ15b4O2F9KeayU8nfAHcCGEb2WJKklpZSFX2jyemB9KeXfNtNvBH68lPLWrprNwOZm8oeAfT0WtQL46wFfdhxqx6Uf41A7Lv1YbLXj0o9xqB2XfoxDbb/6l5dSJnpWl1IWfACuAj7UNf1G4L/PYTlTi6l2XPoxDrXj0o/FVjsu/RiH2nHpxzjUzqV+VLdlDgCruqbPAQ6O6LUkSS2jCvd7gTVJzkvyYmAjsGNEryVJalk6ioWWUp5P8lbgs8AS4MOllD1zWNTWRVY7Lv0Yh9px6cdiqx2XfoxD7bj0Yxxqh64fyR9UJUnHl59QlaQKGe6SVCHDXZIqVE24Jzn9ePehdknOON59qE2SdUle1YxfkOQXk7y2T+2PJ3lJM35Skncn+eMk70ty6rHs91wl+dgCLefaJKtmr5z36/xks09e0+f5Vya5PMmyVvv61vSLk/xcklc3029I8oEk1yQ5ocdyz0/yS0l+O8n7k/z7Yffxogj3JJ9pTd+UZEUzPpnkMeCeJE8mubRVu75r/NQktyV5MMntSc5s1U4muSvJJ5KsSrIzyd8kuTfJxa3a+5P8apLzF3yF56nfiS7JWUluTfI7SU5P8q4kDyXZnmRlq/a01nA6sDvJ8iSntWpPbfbJo0m+1gx7m7aXzrV2Duu9LMl7kuxp9tt0ki8medOQy3lza/olSd6b5ONJ3tB67pZ59PdG4L8BtyZ5L/ABYBlwfZJ39pjlw8B3m/HfBk4F3te0fWSu/ejTt3mfyJPsaA1/DPzrI9PzXPx/ofMz/3+S/IckvT+lOaQku7vG/x2dfXIKcGNaX4CY5FrgTuBtwMNJur9i5ddbi/4I8DrguiQfp/NBz3uAVwEf6rHc/wH8QPP8SXQ+N3R3kssGXplhPvE0ygH4J32GHwMOtWof6hq/C3hVM/4KWp/iAu7vGv8Q8GvAy4G3A3/Uqt1N58vOrgaeBl7ftF8O3N2qfRz4TeCpZr63Ay+bYf2WAe8B9gB/A0wDXwTe1KP2fuBXgfMH2G43ASua8UngMWA/8CRwaav2T+kciNcDDwK/DJzbtN3Zqv1es47dw983j4+1aj/bLOusrrazmradc63tWqe7gE/QOcB3NtvvXuDiVu2dwJvofGjuF4H/BKwBtgG/PsSx+FRr+lPNdr6Szuc1PgWc2D6+uurXd42fCtzWbO/bgTO7j2M6bxU+GfgW8JKm/STgwR7L3dvruG6mH2hNnwXcSucL/E4H3tW83nZgZav2tNZwOvAEsBw4rVX7EuC9wMeBN7Seu6XHcfwJ4DLg0ubxUDN+aXv9Ztknn2lNf4nOxelrmu07Tef43gSc0qo9tdl/jwJfa4a9TdtL28vtGr8XmGjGf5Cu3Onaf8ua8dXAFHBdeznN9IPN41LgGWBJM532vj5yXDTjJwOfa8bPbS93xm02zAYe5QAcBv6Czg9ye/jbVu2jwNJm/IvtDdM+wGb4AWhPd+/Y9g94e2d1L/efAbcAX236u7nH+g0cPAxx4mC4E91M69feFr/U/LD8aHe/+vRh3wz7dd9ca5u2YU64X25N39s8vgh4tPXcg32Gh4DnZtk27wT+L50Q7BXuA11QtPZH+/h6oMdy/yfw5mb8I8Bk176+t1U7qhP5wCe6Zru/nc4JeW3T9lh7vbrqh7nAa7/WCcC/Aj4JTLeeG+bi48t0TmqnM8PPTzP9SGt6WbPdb+5xzDwMvLhZ9rdpTpp0rs73tmof6tqmy4H7upfTb/sdtT0HLRz10Kz8mj7PPd2afhvwZ8BP0bki+a/APwfeDXy8VXuATpi+g85Vbbqea58x76ZzJXAVnSvfK5v2S3vs6F4/1EuA9cBHejw3TPAMfOJguBPdl7vGf22m2qbtHDqBcjOdX017/mA2++I/8v1XpWc2Pzx/Ptfa5rkvdY3PdsL9f8BPNuM/A3y267n2SeYZYC2d0O0eVgMHW7V7gRe12jbR+S3syR59HuiCgs6v5ScfOQ662k/tc3ydCnwU+Mtm3r9vjun/DVw0xHZr92mYE3l73hlPdK3j6APtvrTqhrnA+9IMyzmpNT3MxccTzTZ9vHk8q2lf1mPd/4LmpNXVthT4GHC41f72ZnlPAtcCu4AP0gnyG1u119E5IW+l8/N95IQ+AXy+37octW6DFo56AF4P/FCf567s0XYZ8Ht0fj17CPg0nW+ZPKFVd2NrOPJr1lnAx1q1F9E5y38GeCWd+5rfpPND/E9btXcMuX7DBM/AJw6GO9G9h+bXyFb7PwJ+f4a+/wydW0hf7fP8cjr3fh8FvgF8nU4gvo+jf60fuLapH+aEexGdK/1vAl84cjw1PxTXtmpvO7I/erzm7a3p3wBe3aNuPfCVHu0DXVDQXJ31mH8FXUHb4/lTmnX9MbpOkq2aUZ3IhzrRtepexwy3xxjuAu8VM71Wq3aoC4o+yzgZOK/HNjurT/0lPdpeRvMbOPBSOpm3rs/8FzbPv3LQ9TxqGXOdcRQDnUC9nFYA0XUP8xjU/vCIlvuPW8Hziqa9V/AMe+K4jN4nuqULtd3o3Af+kVlqXz3gctfxD7ePLqQTgq/ts24Dn3C79t9A/VigY/One9QOfEExqoHRnciHOtEN2eehLvCGWG73BcXX+f4LiuXHYn8cj+G4d6BrB1xL5zvd/4jOr0Ybup5r318bpvZtQy730YVe7gDr/uZjUTuHbTGK2hub8Jii84e5XcB/Bj4PvHM+222Y/Tfk6xyXfT2qYbY+8P0n8pEcmwvd53Fb7jgMx70DXRt5mL88L6raAda9733Ihawdh23BkO8QGdX6jerYXMj9N6rhWB1vx6vP47DccRhG8q2Qc7SklPIdgFLKE837OX8/ycvpvF1oMdeS5ME+6x069/9GXjtkn0dV+3wp5TDw3SR/WUr5VjPf3yb53lErMbr1G8bI9vWojMnxNpRRLXsc9sfxME7h/tUka0spDwCUUr6T5F/S+eDGjy7yWugcRFfQ+SNit9D5Y+uxqB2HbfF3SU4upXyXzh8EO53tfPruqHAf4foNY5T7elTG4Xgb1qiWPQ7749g73r86HBkY4i/Pi622aRvm3Rmjqj3u24Ih3yEyqvUb1bE5bJ9HNYzD8TbKPo/Dcsd98PvcJalCi+K7ZSRJwzHcJalChrskVchwl6QKGe6SVKH/D3A7WxVdhPmIAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['subject'].value_counts().plot.bar()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die obige Abbildung ist interessant. Normalerweise haben alle Versuchspersonen die gleiche Versuchsreihe durchgefÃ¼hrt. Daher wÃ¼rde man annehmen, dass die Anzahl der Beobachtungen von jedem Probanden nahezu gleich sein muss. Es gibt jedoch eine Spanne von 200 bis 300 Beobachtungen Ã¼ber alle Versuchspersonen hinweg. Ein Grund dafÃ¼r kÃ¶nnte sein, dass der Wechsel von einer AktivitÃ¤t zur nÃ¤chsten in der Sequenz nicht scharf oder deutlich genug war und die Beobachter die unklaren Beobachtungen in diesen unstabilen Phasen nachtrÃ¤glich gelÃ¶scht haben.  "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gesamtzahl der Beobachtungen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of observations: 7349\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Number of observations: ' + str(df.shape[0]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PCA zur Visualisierung\n",
+    "- **Die PCA ist eine einfache Methode zur Visualisierung hochdimensionaler Daten in einem niedrigdimensionalen Raum. (Vorsicht: wir bezahlen dafÃ¼r mit einem Informationsverlust-->aber fÃ¼r Visualisierungszwecke ist es OK)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_visualisation = df.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = StandardScaler()\n",
+    "data_visualisation = s.fit_transform(data_visualisation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#We want 3 \n",
+    "p = PCA(n_components = 3)\n",
+    "data_visualisation_transformed = p.fit_transform(data_visualisation)\n",
+    "#data_visualisation_transformed = p.transform(data_visualisation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features before PCA: 561\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Features before PCA: ' + str(data_visualisation.shape[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features after PCA: 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Features after PCA: ' + str(data_visualisation_transformed.shape[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.619964301867613"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p.explained_variance_ratio_.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1c5212bc790>]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcXElEQVR4nO3de3RdZZ3/8fenaZNCk4I0IZVeLJS2UBVQQ0FwBFR+g4MzFcUBdI2Dt9oZ8bqcn6xZs7wM8/st0ZmfOoDTVRUYHbXijJeKBXRQhCUiLQy3Ir1QuQRom5RbEtqkbb6/P/ZOepqepDtp9jlJ9ue11lk5e+9n73xPd/p8z3728zxbEYGZmRXXpGoHYGZm1eVEYGZWcE4EZmYF50RgZlZwTgRmZgU3udoBDFdjY2PMmzev2mGYmY0r99xzT3tENJXbNu4Swbx581i3bl21wzAzG1ckPT7YNjcNmZkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVnBOBmRVSRNCxa3e1wxgTxt2AMjOzwfT2Bs/v3E17ZzftHd20dXazo7MnWe7spj19v6Ozh7bObnr29PLIFecxdUpNtUOvKicCMxvTdu/t5dmuHto6utnR1UN7R3d/xd5XofdV8M929bC398CHbU2eJGbU19JYX0djfR0Ljm6gMV32s7mcCMysCnbt3rvvG3pase9IK/vSSr69s5vnXirffDN1yiQa6+uYUV/HrCOncvLsI9KKvpYZaYXf1JBU9tOnTmHSJFX4U44fTgRmdsgigs7uPbR39rAjrcjbSiv5Ac0znd17yh6noW4yjQ1JZX780fWcdtxR/d/i+yr5xvo6GhvqmFZbg+TKfTTkmggknQd8DagBvhkRXyxT5mzgq8AUoD0izsozJjPLprc3eCFtb+9rfumr5Ns7Dmx3797TW/Y4R02rZca0pAJ/9ewj91XmJU01jQ11zJhWW/i2+mrJLRFIqgGuAc4FWoG1klZHxMMlZY4Evg6cFxFPSDo6r3jMDPak7e3t+31D7y5Z3vct/tmuHvaUaW+vmaT+ir2xoY75TfX93+L7mmoa62tpqq/jqGm1TK5x58SxLs8rgiXA5ojYAiBpFbAUeLikzLuBH0XEEwARsT3HeMwmpF279+53E3XfDdR9FfuOruT9cy/1lL05Wjt5Ek1pBf7yI6by6llH7Lu52rCvYp9RX8eRh7m9faLJMxHMAp4sWW4FThtQZiEwRdJtQAPwtYj49sADSVoGLAOYO3duLsGajRURQVfP3v4KvK2kGeaArpAd3XQM0t5eXze5/8bpsY3TOHXeUfsq9mm1aQWfVPL1dZPd3l5geSaCcn9VA7+LTAZeB7wZOAz4naS7ImLjfjtFrARWArS0tLizl407ESXt7R09yTf0ju4BTTT73u/aXb69/cjDp/RX3q88Zvqgbe1NDXVub7fM8kwErcCckuXZwNNlyrRHRBfQJel24GRgI2bjxK7de3m0rZPN2zvZ/mI37V0Db6Ym3+QHa2/vu5na1JB8cx/Y1p50g0za26e4vd1ykGciWAsskHQs8BRwMck9gVI/Ba6WNBmoJWk6+kqOMZmN2O69vfyxvYuN2zrYuLWDjds62bitg8d2dFFax9fWTEoq8IY6mqdPZfHLp+/XDNNUUsm/7PBat7db1eWWCCJij6TLgFtIuo9eGxHrJS1Pt6+IiD9Iuhl4AOgl6WL6UF4xmWWxtzd44tmX2LC1g03bOtiwrYON2zr4Y3sXu/cmNf4kwbzGaSxsbuBtJx/DwuZ6FhzdwMwjpjJ9qtvbbXxRjLPx1S0tLeGH19to6O0Nnnp+J5u2d7Bha/LtfuO2DjZv79yvT/ycow5jUXMDC5ob0p/1zG+qdxu8jSuS7omIlnLbPLLYJryIYNuL3f0V/cZtHWzY1snmbR109eztL/fyI6ayoLmBM+bP6K/0jz+6nml1/m9iE5v/wm1C2dHZzYZtHWza1pn+7GDD1g5e3LWvi2VjfS0Lmxt4V8scFjY3sGhmPccf3cARh02pYuRm1eNEYOPSCzt397ffb9rWyYatyTf9HV09/WWOOGwKi5ob+POTj2HRzAYWHN3AwuZ6ZtTXVTFys7HHicDGtK7uPWza3pn20tlX8W99cVd/mWm1NSxobuAtJzazoLmeRTMbWNjcwNENdb5pa5aBE4GNCbt272Xz9s7+G7d93/Zbn9vZX6Zu8iQWNNdzxvwZLJyZfLtf2NzArCMPc4VvdgicCKyi+vril3bN3LStc7+++FNqxHGN9bxm7su4qGUOC2cmN27nHHU4Ne5zbzbqnAgsF3t7g8d3dPUPuup7bWnr6h9hW64v/qLmBuY1TvMIWrMKciKwQ9LXF3/jgBu3j7aV74v/5hObWdSctOEf1zTNffHNxgAnAstkYF/8DVs72Lg9act/qUxf/DOPn8HCtMJ3X3yzsc3/O+0A7Z3d++bTKemxs39f/DoWNtfzl+6LbzbuOREU2Asv7Wbj9o4DJlFzX3yzYnEiKIByffE3butg24vd/WXK9cVf1NxAk/vim014TgQTSF9f/KQdf19vnXJ98c+c39jfLXNBc7374psVmBPBONSzp2Re/P5XJ48P0hf/4lPn9E+i5r74ZjaQE8EYtq8vflLR902iVq4v/gkz03b85qQN333xzSwrJ4Ix6t3fuIt1jz9HT0lf/LlHHc7C5nrecmJzf9dM98U3s0NVqEQw7/KfVzuEQ/LEsy/xxLMv8d9/2F7tUA7w2BfPr3YIZjZCbjswMys4JwIzs4JzIjAzKzgnAjOzgnMiMDMrOCcCM7OCcyIwMys4JwIzs4LLNRFIOk/SBkmbJV1eZvvZkl6QdF/6+mye8ZiZ2YFyG1ksqQa4BjgXaAXWSlodEQ8PKHpHRLwtrzjMzGxoeV4RLAE2R8SWiOgBVgFLc/x9ZmY2AnkmglnAkyXLrem6gV4v6X5JN0l6ZbkDSVomaZ2kdW1tbXnEamZWWHkmgnKT3seA5XuBV0TEycBVwE/KHSgiVkZES0S0NDU1jW6UZmYFl2ciaAXmlCzPBp4uLRARL0ZEZ/p+DTBFUmOOMZmZ2QB5JoK1wAJJx0qqBS4GVpcWkDRT6fMRJS1J49mRY0xmZjZAbr2GImKPpMuAW4Aa4NqIWC9pebp9BXAh8DeS9gA7gYsjYmDzkZmZ5SjXB9OkzT1rBqxbUfL+auDqPGMwM7OheWSxmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVnBOBmVnBDTqOQNLPOHBuoH4R8Re5RGRmZhU11ICyf05/vgOYCfxHunwJ8FiOMZmZWQUNmggi4jcAkq6IiDeWbPqZpNtzj8zMzCoiyz2CJknH9S1IOhbwXNBmZhNElrmGPgncJmlLujwP+HBuEZmZWUUdNBFExM2SFgAnpKseiYjufMMyM7NKOWjTkKTDgb8DLouI+4G5kvyweTOzCSLLPYLrgB7g9elyK/BPuUVkZmYVlSURzI+ILwG7ASJiJ+WfR2xmZuNQlkTQI+kw0sFlkuYDvkdgZjZBZOk19DngZmCOpO8CZwKX5hmUmZlVTpZeQ7+UdC9wOkmT0Mcjoj33yMzMrCKyPrN4KvBcWn6xJCLCo4vNzCaAgyYCSVcCFwHrgd50dQBOBGZmE0CWK4K3A4s8iMzMbGLK0mtoCzAl70DMzKw6slwRvATcJ+lWSrqNRsTHcovKzMwqJssVwWrgCuBO4J6S10FJOk/SBkmbJV0+RLlTJe2VdGGW45qZ2ejJ0n3030dyYEk1wDXAuSTTUqyVtDoiHi5T7krglpH8HjMzOzRDParyhoj4S0kPUuaRlRFx0kGOvQTYHBFb0uOtApYCDw8o91Hgv4BThxO4mZmNjqGuCD6e/hzpTKOzgCdLlluB00oLSJoFXAC8iSESgaRlwDKAuXPnjjAcMzMrZ6hHVT6T/nx8hMcuNzHdwCuLrwKfiYi90uDz2EXESmAlQEtLywFXJ2ZmNnJZBpSdDlwFnAjUAjVAV0RMP8iurcCckuXZwNMDyrQAq9Ik0Aj8maQ9EfGTTNGbmdkhy9J99GrgYuCHJBX3e4HjM+y3FliQPuP4qfQY7y4tEBHH9r2XdD1wo5OAmVllZZprKCI2S6qJiL3AdZLuzLDPHkmXkfQGqgGujYj1kpan21ccSuBmZjY6Mg0ok1RLMqjsS8AzwLQsB4+INcCaAevKJoCIuDTLMc3MbHRlGVD2VyTf6C8Dukja/d+ZZ1BmZlY5WQaU9fUa2gl8Id9wzMys0oYaUFZ2IFmfDAPKzMxsHBjqimCkA8nMzGwcGWpAWf9AMkkzSaaMCGBtRGytQGxmZlYBB71ZLOmDwN3AO4ALgbskvT/vwMzMrDKydB/9O+A1EbEDQNIMkimpr80zMDMzq4ws3UdbgY6S5Q72n0zOzMzGsSxXBE8Bv5f0U5J7BEuBuyV9CiAi/l+O8ZmZWc6yJIJH01efn6Y/G0Y/HDMzq7QsieDKiNhVukJSY0S05xSTmZlVUJZ7BHenU1EDIOmdJDeLzcxsAshyRfAe4FpJtwHHADNInihmZmYTQJa5hh6U9H+A75D0GHpjRLTmHpmZmVVElieUfQuYD5wELAR+JunqiLgm7+DMzCx/We4RPAScExF/jIhbgNOB1+YblpmZVcqgiUDSdICI+EpE9M9CGhEv4OmozcwmjKGuCG7reyPp1gHbfpJHMGZmVnlDJQKVvD9qiG1mZjaODZUIYpD35ZbNzGycGqrX0NHpfEIqeU+63JR7ZGZmVhFDJYJvsG8+odL3AN/MLSIzM6uooZ5Q5p5BZmYFkGUcgZmZTWC5JgJJ50naIGmzpMvLbF8q6QFJ90laJ+kNecZjZmYHyjLp3IhIqgGuAc4lecrZWkmrI+LhkmK3AqsjIiSdBNwAnJBXTGZmdqAsD69vlvQtSTely4slfSDDsZcAmyNiS0T0AKtInm7WLyI6S0YtT8PdUs3MKi5L09D1wC0kU1ADbAQ+kWG/Wez/bOPWdN1+JF0g6RHg58D7MxzXzMxGUZZE0BgRNwC9ABGxB9ibYb9yo48P+MYfET+OiBOAtwNXlD2QtCy9h7Cura0tw682M7OssiSCLkkzSCvx9GllL2TYrxWYU7I8G3h6sMIRcTswX1JjmW0rI6IlIlqamjyWzcxsNGW5WfwpYDVJJf1bklHFF2bYby2wQNKxwFPAxcC7SwtIOh54NL1Z/FqgFtgxjPjNzOwQZXlC2b2SzgIWkTT3bIiI3Rn22yPpMpL7CzXAtRGxXtLydPsK4J3AeyXtBnYCF5VOeW1mZvnL8oSyjwDfjYj16fLLJF0SEV8/2L4RsQZYM2DdipL3VwJXDjtqMzMbNVnuEXwoIp7vW4iI54AP5RaRmZlVVJZEMElSfw+gdKBYbX4hmZlZJWW5WXwLcIOkFSQ9h5YDN+calZmZVUyWRPAZ4MPA35DcLP4FnobazGzCyNJrqBf4t/RlZmYTTJZeQ2cCnwdekZYXEBFxXL6hmZlZJWRpGvoW8EngHrJNLWFmZuNIlkTwQkTclHskZmZWFVkSwa8lfRn4EdDdtzIi7s0tKjMzq5gsieC09GdLyboA3jT64ZiZWaVl6TV0TiUCMTOz6sj0qEpJ5wOvBKb2rYuIf8wrKDMzq5wsj6pcAVwEfJSk6+i7SLqSmpnZBJBlrqEzIuK9wHMR8QXg9ez/wBkzMxvHsiSCnenPlyQdA+wGjs0vJDMzq6Qs9whulHQk8GXgXpIeQ55ryMxsgsjSa6jvgfL/JelGYGpEZHlmsZmZjQODJgJJb4qIX0l6R5ltRMSP8g3NzMwqYagrgrOAXwF/XmZbkIw0NjOzcW7QRBARn5M0CbgpIm6oYExmZlZBQ/YaSp9FcFmFYjEzsyrI0n30l5I+LWmOpKP6XrlHZmZmFZGl++j7058fKVkXgB9MY2Y2AWTpPurBY2ZmE1jWSedeBSxm/0nnvp1XUGZmVjlZJp37HHBV+joH+BLwF1kOLuk8SRskbZZ0eZnt75H0QPq6U9LJw4zfzMwOUZabxRcCbwa2RsT7gJOBuoPtJKkGuAZ4K8nVxCWSFg8o9kfgrIg4CbgCWDmM2M3MbBRkmnQu7Ua6R9J0YDvZbhQvATZHxJaI6AFWAUtLC0TEnRHxXLp4FzA7e+hmZjYasiSCdemkc98A7iGZeO7uDPvNAp4sWW5N1w3mA8BN5TZIWiZpnaR1bW1tGX61mZllNdRcQ1cD34uIv01XrZB0MzA9Ih7IcGyVWReD/K5zSBLBG8ptj4iVpM1GLS0tZY9hZmYjM1SvoU3Av0h6OfAD4PsRcd8wjt3K/g+wmQ08PbCQpJNIprV+a0TsGMbxzcxsFAzaNBQRX4uI15NMPvcscJ2kP0j6rKSFGY69Flgg6VhJtcDFwOrSApLmkkxe91cRsXHEn8LMzEbsoPcIIuLxiLgyIl4DvBu4APhDhv32kMxTdEta/oaIWC9puaTlabHPAjOAr0u6T9K6kX4QMzMbmYMOKJM0BTiP5Bv9m4HfAF/IcvCIWAOsGbBuRcn7DwIfHEa8ZmY2yoa6WXwucAlwPkkvoVXAsojoqlBsZmZWAUNdEfw98D3g0xHxbIXiMTOzChvqwTTnVDIQMzOrjiwDyszMbAJzIjAzKzgnAjOzgnMiMDMrOCcCM7OCcyIwMys4JwIzs4JzIjAzKzgnAjOzgnMiMDMrOCcCM7OCcyIwMys4JwIzs4JzIjAzKzgnAjOzgnMiMDMrOCcCM7OCcyIwMys4JwIzs4JzIjAzKzgnAjOzgss1EUg6T9IGSZslXV5m+wmSfiepW9Kn84zFzMzKm5zXgSXVANcA5wKtwFpJqyPi4ZJizwIfA96eVxxmZja0PK8IlgCbI2JLRPQAq4ClpQUiYntErAV25xiHmZkNIc9EMAt4smS5NV03bJKWSVonaV1bW9uoBGdmZok8E4HKrIuRHCgiVkZES0S0NDU1HWJYZmZWKs9E0ArMKVmeDTyd4+8zM7MRyDMRrAUWSDpWUi1wMbA6x99nZmYjkFuvoYjYI+ky4BagBrg2ItZLWp5uXyFpJrAOmA70SvoEsDgiXswrLjMz219uiQAgItYAawasW1HyfitJk5GZmVWJRxabmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFVyuI4vNDtW8y39e7RAmrMe+eH61Q7AxwlcEZmYF50RgZlZwTgRmZgXnRGBmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZwHlBmZqPKgwDzk9cgQF8RmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZweWaCCSdJ2mDpM2SLi+zXZL+Nd3+gKTX5hmPmZkdKLdEIKkGuAZ4K7AYuETS4gHF3gosSF/LgH/LKx4zMysvzyuCJcDmiNgSET3AKmDpgDJLgW9H4i7gSEkvzzEmMzMbIM+RxbOAJ0uWW4HTMpSZBTxTWkjSMpIrBoBOSRtGN9QxqxFor3YQWejKakcwZvicjS/j5nzBIZ+zVwy2Ic9EoDLrYgRliIiVwMrRCGo8kbQuIlqqHYdl53M2vvh8JfJsGmoF5pQszwaeHkEZMzPLUZ6JYC2wQNKxkmqBi4HVA8qsBt6b9h46HXghIp4ZeCAzM8tPbk1DEbFH0mXALUANcG1ErJe0PN2+AlgD/BmwGXgJeF9e8YxThWsOmwB8zsYXny9AEQc0yZuZWYF4ZLGZWcE5EZiZFZwTQZVI2ivpPkkPSfqhpMPT9TMlrZL0qKSHJa2RtDDddrOk5yXdWN3oi2m450zSKZJ+J2l9OoXKRdX+DEUzgnP2Ckn3pPv039Oc6HyPoEokdUZEffr+u8A9wFeAO4F/T2+mI+kUoCEi7pD0ZuBw4MMR8bbqRF5cwz1nwDYgImKTpGPS8idGxPNVCL+QRnDOfk9SL3ZLqgceAs6IiAndrd3PLB4b7gBOAs4Bdvf9cQJExH0l72+VdHalg7OyMp2zknVPS9oONAHPVyhG29+wzhlQR0FaTQrxIccySZNJJt97EHgVyTcWG8NGcs4kLQFqgUfzjc7KGc45kzRH0gMk099cOdGvBsCJoJoOk3QfsA54AvhWdcOxDEZ0ztKJFL8DvC8ievMLz8oY9jmLiCcj4iTgeOCvJTXnG2L1uWmoenZGxCmlKyStBy6sTjiWwbDPmaTpwM+Bf0hn2LXKGvH/s7Q5bz3wJ8B/5hPe2OArgrHlV0CdpA/1rZB0qqSzqhiTDW3Qc5ZOrfJjkqnWf1i1CG2goc7ZbEmHpeteBpwJTPjZjt1rqEpKezMMWH8M8FXgdcAu4DHgE2nPkzuAE4B6YAfwgYi4pWJBF9xwzxnJtOvXAetLil86yI1Jy8EIztk84F9IZkEWcHU6+/GE5kRgZlZwbhoyMys4JwIzs4JzIjAzKzgnAjOzgnMiMDMrOCcCq4rBZoUsU+7OER6/RdK/HkJ8nYOsH3R22PFK0tmSzqh2HFY9TgRWLTsj4pSIeBXQA+w33a+kGoCIGFEFFRHrIuJjhx7mfjGJZIDYbRExPyIWA38PjPcpCM4GnAgKzInAxoI7gOPTb6a/lvQ9ksnB+r+Zp9tuk/Sfkh6R9N20Yu4bFXqnpPsl3S2pIS1/Y7r985K+I+lXkjb1jSiVVC/pVkn3SnpQ0tKDxFl21sp0inBJ+nJ6hfNg37MH0jh+I+kGSRslfVHSe9I4H5Q0Py13vaQVku5Iy70tXT9V0nVp2f+RdE66/lJJP1LyjIpNkr7UF5Ok/6XkOQj3pldbfdMwPybpCyWf9wRJ80iS8CfTK7Q/kfSu9HPcL+n2Qzy3Ng54riGrKu2bFfLmdNUS4FUR8ccyxV8DvBJ4GvgtcKaku4EfABdFxNp0bp+dZfY9CTgdmAb8j6SfA9uBCyLiRUmNwF2SVsfgoyyHmrXyHcApwMlAI7C2pBI9GTgReBbYAnwzIpZI+jjwUZIRrZCMaj0LmA/8WtLxwEcAIuLVkk4AflHSFHVK+m/SDWyQdFX62f8BeEtEdEn6DPAp4B/Tfdoj4rWS/hb4dER8UNIKoDMi/hlA0oPAn0bEU5KOHOTz2gTiKwKrlsFmhbx7kCTQt601ncHzPpKKcxHwTESsBYiIFyNiT5l9fxoROyOiHfg1ScIR8H+VTDn838AsRt7M8wbg+xGxNyK2Ab8BTk23rY2IZyKim2Qa6l+k6x9MP0OfGyKiNyI2kSSME9Ljfif9bI8AjwN9ieDWiHghInYBDwOvIEl2i4Hfpv++f52u7/Oj9Oc9A353qd8C16dXTjXD+Uew8clXBFYt5WaFBOgaYp/ukvd7Sf5+RTIvzMEMLBPAe0geFPO6iNgt6TFg6hDHGGrWSg2xX2ncvSXLvez/f7BcjFmPW/rv8cuIuOQg+/SVP0BELJd0GnA+cJ+kUyJixxBx2DjnKwIb7x4BjpF0KkB6f6BcBbc0bW+fQXJzdC1wBLA9TQLnsP8353KGmh32duAiSTWSmoA3AncP87O8S9Kk9L7BcSSzXt5OkrBIm4TmMvRsmHeRNJkdn+5zuA7eq6mD5DGNfZ9pfkT8PiI+C7QDc4b5OWyccSKwcS0ieoCLgKsk3Q/8kvLf6u8meS7AXcAV6VOnvgu0SFpHUtk+cpDfFcAFwLlKuo+uBz5Pcs/ix8ADwP0kCeN/R8TWYX6cDSRNSjcBy9Mmn68DNWm7/Q9IZi/tHuwAEdEGXAp8P23yuoukiWkoPwMu6LtZDHw5vZn8EEkiun+Yn8PGGc8+ahOepM9TcjN0LJJ0PXBjREzoB6DY2OQrAjOzgvMVgZlZwfmKwMys4JwIzMwKzonAzKzgnAjMzArOicDMrOD+P9xjPyVa4fGfAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = []\n",
+    "for i in range(0, len(p.explained_variance_ratio_)):\n",
+    "    x.append('PC' + str(i + 1))\n",
+    "y = np.array(p.explained_variance_ratio_)\n",
+    "z = np.cumsum(p.explained_variance_ratio_)\n",
+    "plt.xlabel('Principal Components')\n",
+    "plt.ylabel('Variance Explained')\n",
+    "plt.bar(x, y)\n",
+    "plt.plot(x, z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#labels 'STANDING', 'SITTING', 'LAYING', 'WALKING'\n",
+    "\n",
+    "#fig, ax = plt.subplots()\n",
+    "plt.figure(figsize=(15,6))\n",
+    "for activity in df['Activity'].unique():\n",
+    "    filtered_val = data_visualisation_transformed[df['Activity']==activity,:]\n",
+    "    plt.scatter(filtered_val[:,0], filtered_val[:,1], label=activity, s=1.5)\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize = (15, 6))\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "for activity in df['Activity'].unique():\n",
+    "    filtered_val = data_visualisation_transformed[df['Activity'] == activity, :]\n",
+    "    ax.scatter(\n",
+    "        filtered_val[:, 0], \n",
+    "        filtered_val[:, 1], \n",
+    "        filtered_val[:, 2], \n",
+    "        label = activity, \n",
+    "        s = 4\n",
+    "    )\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
         {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Es gibt zwei getrennte Datens\u00e4tze aus der Testgruppe und der Trainingsgruppe. Diese werden f\u00fcr die weitere Analyse zun\u00e4chst noch einmal zusammengefasst. Die Daten werden identifiziert, ob sie aus dem Trainings- oder Testdatensatz stammen, damit sie sp\u00e4ter wieder aufgeteilt werden k\u00f6nnen."
-            ]
+         "hovertemplate": "color=STANDING<br>x=%{x}<br>y=%{y}<br>z=%{z}<extra></extra>",
+         "legendgroup": "STANDING",
+         "marker": {
+          "color": "#636efa",
+          "line": {
+           "color": "azure",
+           "width": 0.05
+          },
+          "size": 2.5,
+          "symbol": "circle"
+         },
+         "mode": "markers",
+         "name": "STANDING",
+         "scene": "scene",
+         "showlegend": true,
+         "type": "scatter3d",
+         "x": [
+          -11.646608782317305,
+          -10.735523666780427,
+          -10.493257762047435,
+          -10.713837435187976,
+          -10.900113176058632,
+          -10.78327592205203,
+          -10.310643888654335,
+          -10.595253596142921,
+          -10.824502028190786,
+          -9.430757317366792,
+          -9.653739749909265,
+          -11.542945986321808,
+          -11.439638417968158,
+          -11.325376672536082,
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+          -10.71499451224212,
+          -10.960280145316958,
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+          -10.945501598202457,
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+          -10.894676108848351,
+          -10.607719984712029,
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+          -10.371570189051653,
+          -6.561288910439135,
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-            "metadata": {},
-            "outputs": [],
-            "source": [
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-                            "      <th>tBodyAcc-mean()-X</th>\n",
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-                            "      <th>subject</th>\n",
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-                            "      <td>1</td>\n",
-                            "      <td>STANDING</td>\n",
-                            "      <td>train</td>\n",
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-                            "      <th>1</th>\n",
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-                            "      <td>...</td>\n",
-                            "      <td>0.053477</td>\n",
-                            "      <td>-0.007435</td>\n",
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-                            "      <td>0.703511</td>\n",
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-                            "      <td>0.180289</td>\n",
-                            "      <td>-0.054317</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>STANDING</td>\n",
-                            "      <td>train</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>0.279653</td>\n",
-                            "      <td>-0.019467</td>\n",
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-                            "      <td>1</td>\n",
-                            "      <td>STANDING</td>\n",
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-                            "      <td>1</td>\n",
-                            "      <td>STANDING</td>\n",
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-                            "<p>5 rows \u00d7 564 columns</p>\n",
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-                        "text/plain": [
-                            "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
-                            "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
-                            "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
-                            "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
-                            "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
-                            "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
-                            "\n",
-                            "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
-                            "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
-                            "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
-                            "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
-                            "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
-                            "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
-                            "\n",
-                            "   tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  angle(tBodyAccMean,gravity)  \\\n",
-                            "0         -0.923527         -0.934724  ...                    -0.112754   \n",
-                            "1         -0.957686         -0.943068  ...                     0.053477   \n",
-                            "2         -0.977469         -0.938692  ...                    -0.118559   \n",
-                            "3         -0.989302         -0.938692  ...                    -0.036788   \n",
-                            "4         -0.990441         -0.942469  ...                     0.123320   \n",
-                            "\n",
-                            "   angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
-                            "0                              0.030400                         -0.464761   \n",
-                            "1                             -0.007435                         -0.732626   \n",
-                            "2                              0.177899                          0.100699   \n",
-                            "3                             -0.012892                          0.640011   \n",
-                            "4                              0.122542                          0.693578   \n",
-                            "\n",
-                            "   angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
-                            "0                             -0.018446             -0.841247   \n",
-                            "1                              0.703511             -0.844788   \n",
-                            "2                              0.808529             -0.848933   \n",
-                            "3                             -0.485366             -0.848649   \n",
-                            "4                             -0.615971             -0.847865   \n",
-                            "\n",
-                            "   angle(Y,gravityMean)  angle(Z,gravityMean)  subject  Activity   type  \n",
-                            "0              0.179941             -0.058627        1  STANDING  train  \n",
-                            "1              0.180289             -0.054317        1  STANDING  train  \n",
-                            "2              0.180637             -0.049118        1  STANDING  train  \n",
-                            "3              0.181935             -0.047663        1  STANDING  train  \n",
-                            "4              0.185151             -0.043892        1  STANDING  train  \n",
-                            "\n",
-                            "[5 rows x 564 columns]"
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-                    "output_type": "stream",
-                    "text": [
-                        "Total number of observations: 10299\n"
-                    ]
-                }
+            "type": "contour"
+           }
+          ],
+          "contourcarpet": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "contourcarpet"
+           }
+          ],
+          "heatmap": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
             ],
-            "source": [
-                "print('Total number of observations: ' + str(df.shape[0]))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Auf fehlende Werte pr\u00fcfen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 42,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "tBodyAcc-mean()-X       0\n",
-                            "tBodyAcc-mean()-Y       0\n",
-                            "tBodyAcc-mean()-Z       0\n",
-                            "tBodyAcc-std()-X        0\n",
-                            "tBodyAcc-std()-Y        0\n",
-                            "                       ..\n",
-                            "angle(Y,gravityMean)    0\n",
-                            "angle(Z,gravityMean)    0\n",
-                            "subject                 0\n",
-                            "Activity                0\n",
-                            "type                    0\n",
-                            "Length: 564, dtype: int64"
-                        ]
-                    },
-                    "execution_count": 42,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
+            "type": "heatmap"
+           }
+          ],
+          "heatmapgl": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
             ],
-            "source": [
-                "df.isna().sum()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 43,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "0"
-                        ]
-                    },
-                    "execution_count": 43,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
+            "type": "heatmapgl"
+           }
+          ],
+          "histogram": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "histogram"
+           }
+          ],
+          "histogram2d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
             ],
-            "source": [
-                "df.isna().sum().sum()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "--> **Keine fehlenden Werte im Data Frame"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Auf Duplikate pr\u00fcfen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 44,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>tBodyAcc-mean()-X</th>\n",
-                            "      <th>tBodyAcc-mean()-Y</th>\n",
-                            "      <th>tBodyAcc-mean()-Z</th>\n",
-                            "      <th>tBodyAcc-std()-X</th>\n",
-                            "      <th>tBodyAcc-std()-Y</th>\n",
-                            "      <th>tBodyAcc-std()-Z</th>\n",
-                            "      <th>tBodyAcc-mad()-X</th>\n",
-                            "      <th>tBodyAcc-mad()-Y</th>\n",
-                            "      <th>tBodyAcc-mad()-Z</th>\n",
-                            "      <th>tBodyAcc-max()-X</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>angle(tBodyAccMean,gravity)</th>\n",
-                            "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
-                            "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
-                            "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
-                            "      <th>angle(X,gravityMean)</th>\n",
-                            "      <th>angle(Y,gravityMean)</th>\n",
-                            "      <th>angle(Z,gravityMean)</th>\n",
-                            "      <th>subject</th>\n",
-                            "      <th>Activity</th>\n",
-                            "      <th>type</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>0 rows \u00d7 564 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "Empty DataFrame\n",
-                            "Columns: [tBodyAcc-mean()-X, tBodyAcc-mean()-Y, tBodyAcc-mean()-Z, tBodyAcc-std()-X, tBodyAcc-std()-Y, tBodyAcc-std()-Z, tBodyAcc-mad()-X, tBodyAcc-mad()-Y, tBodyAcc-mad()-Z, tBodyAcc-max()-X, tBodyAcc-max()-Y, tBodyAcc-max()-Z, tBodyAcc-min()-X, tBodyAcc-min()-Y, tBodyAcc-min()-Z, tBodyAcc-sma(), tBodyAcc-energy()-X, tBodyAcc-energy()-Y, tBodyAcc-energy()-Z, tBodyAcc-iqr()-X, tBodyAcc-iqr()-Y, tBodyAcc-iqr()-Z, tBodyAcc-entropy()-X, tBodyAcc-entropy()-Y, tBodyAcc-entropy()-Z, tBodyAcc-arCoeff()-X,1, tBodyAcc-arCoeff()-X,2, tBodyAcc-arCoeff()-X,3, tBodyAcc-arCoeff()-X,4, tBodyAcc-arCoeff()-Y,1, tBodyAcc-arCoeff()-Y,2, tBodyAcc-arCoeff()-Y,3, tBodyAcc-arCoeff()-Y,4, tBodyAcc-arCoeff()-Z,1, tBodyAcc-arCoeff()-Z,2, tBodyAcc-arCoeff()-Z,3, tBodyAcc-arCoeff()-Z,4, tBodyAcc-correlation()-X,Y, tBodyAcc-correlation()-X,Z, tBodyAcc-correlation()-Y,Z, tGravityAcc-mean()-X, tGravityAcc-mean()-Y, tGravityAcc-mean()-Z, tGravityAcc-std()-X, tGravityAcc-std()-Y, tGravityAcc-std()-Z, tGravityAcc-mad()-X, tGravityAcc-mad()-Y, tGravityAcc-mad()-Z, tGravityAcc-max()-X, tGravityAcc-max()-Y, tGravityAcc-max()-Z, tGravityAcc-min()-X, tGravityAcc-min()-Y, tGravityAcc-min()-Z, tGravityAcc-sma(), tGravityAcc-energy()-X, tGravityAcc-energy()-Y, tGravityAcc-energy()-Z, tGravityAcc-iqr()-X, tGravityAcc-iqr()-Y, tGravityAcc-iqr()-Z, tGravityAcc-entropy()-X, tGravityAcc-entropy()-Y, tGravityAcc-entropy()-Z, tGravityAcc-arCoeff()-X,1, tGravityAcc-arCoeff()-X,2, tGravityAcc-arCoeff()-X,3, tGravityAcc-arCoeff()-X,4, tGravityAcc-arCoeff()-Y,1, tGravityAcc-arCoeff()-Y,2, tGravityAcc-arCoeff()-Y,3, tGravityAcc-arCoeff()-Y,4, tGravityAcc-arCoeff()-Z,1, tGravityAcc-arCoeff()-Z,2, tGravityAcc-arCoeff()-Z,3, tGravityAcc-arCoeff()-Z,4, tGravityAcc-correlation()-X,Y, tGravityAcc-correlation()-X,Z, tGravityAcc-correlation()-Y,Z, tBodyAccJerk-mean()-X, tBodyAccJerk-mean()-Y, tBodyAccJerk-mean()-Z, tBodyAccJerk-std()-X, tBodyAccJerk-std()-Y, tBodyAccJerk-std()-Z, tBodyAccJerk-mad()-X, tBodyAccJerk-mad()-Y, tBodyAccJerk-mad()-Z, tBodyAccJerk-max()-X, tBodyAccJerk-max()-Y, tBodyAccJerk-max()-Z, tBodyAccJerk-min()-X, tBodyAccJerk-min()-Y, tBodyAccJerk-min()-Z, tBodyAccJerk-sma(), tBodyAccJerk-energy()-X, tBodyAccJerk-energy()-Y, tBodyAccJerk-energy()-Z, tBodyAccJerk-iqr()-X, ...]\n",
-                            "Index: []\n",
-                            "\n",
-                            "[0 rows x 564 columns]"
-                        ]
-                    },
-                    "execution_count": 44,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
+            "type": "histogram2d"
+           }
+          ],
+          "histogram2dcontour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
             ],
-            "source": [
-                "df[df.duplicated()]"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "--> **keine Duplikatzeilen** in den Daten"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Ziel Variable"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Wir haben ein Klassifizierungsproblem und die Zielspalte ist die Spalte \"Aktivit\u00e4t\"."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 45,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "array(['STANDING', 'SITTING', 'LAYING', 'WALKING', 'WALKING_DOWNSTAIRS',\n",
-                            "       'WALKING_UPSTAIRS'], dtype=object)"
-                        ]
-                    },
-                    "execution_count": 45,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
+            "type": "histogram2dcontour"
+           }
+          ],
+          "mesh3d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "mesh3d"
+           }
+          ],
+          "parcoords": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "parcoords"
+           }
+          ],
+          "pie": [
+           {
+            "automargin": true,
+            "type": "pie"
+           }
+          ],
+          "scatter": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter"
+           }
+          ],
+          "scatter3d": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter3d"
+           }
+          ],
+          "scattercarpet": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattercarpet"
+           }
+          ],
+          "scattergeo": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergeo"
+           }
+          ],
+          "scattergl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergl"
+           }
+          ],
+          "scattermapbox": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermapbox"
+           }
+          ],
+          "scatterpolar": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolar"
+           }
+          ],
+          "scatterpolargl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolargl"
+           }
+          ],
+          "scatterternary": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterternary"
+           }
+          ],
+          "surface": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
             ],
-            "source": [
-                "#Possible classes/labels\n",
-                "df['Activity'].unique()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 46,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:>"
-                        ]
-                    },
-                    "execution_count": 46,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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t24DlwD6T/f4REbHhejKmL2kusBdwVWk6VtK1ks6QtFVpmwXc1fayFUz8SyIiInqs69CXtBlwPnC87V8ApwE7AHsCK4GTW0/t8HKP854LJC2WtHjVqlXdlhgREUVXoS9pY6rAP9v2VwFs32t7je1Hgc/x+BDOCmC7tpfPBu7p9L62F9ketT06MjLSTYkREdGmm9k7Ak4HbrD9qbb2bdue9hrgunL/QmC+pE0lbQ/sCFw92e8fEREbrpvZO/sBbwaWSVpa2t4PHCFpT6qhm9uBtwPYvl7SecCPqWb+HJOZOxER/TXp0Lf9PTqP0188wWsWAgsn+z0jIqI7uSI3IqJBEvoREQ2S0I+IaJCEfkREgyT0IyIaJKEfEdEgCf2IiAZJ6EdENEhCPyKiQRL6ERENktCPiGiQhH5ERIMk9CMiGiShHxHRIAn9iIgGSehHRDRIQj8iokES+hERDdL30Jc0T9JNkpZLel+/v39ERJP1NfQlzQD+AXg5sBvVJuq79bOGiIgm63dPfx9gue1bbf8GOBc4tM81REQ0lmz375tJrwfm2f7Tcvxm4PdtHzvmeQuABeVwZ+CmvhUJ2wA/7eP366dh/myQzzfo8vl663dsj4xt3KiPBQCoQ9tav3VsLwIWTX05a5O02PZoHd97qg3zZ4N8vkGXz9cf/R7eWQFs13Y8G7inzzVERDRWv0P/P4EdJW0vaRNgPnBhn2uIiGisvg7v2F4t6VjgG8AM4Azb1/ezhvVQy7BSnwzzZ4N8vkGXz9cHfT2RGxER9coVuRERDZLQj4hokIR+RESDJPQjIqaYpK0kdbpOqe8S+m0kzZI0p9z6feFaT0k6VNIxbcdXSbq13F5fZ229IGmGpM3ajveVtH+5bV5nbd2SNFvSH7Qd/7mkD5fbc+usrVck/Y6kp7Udv0TSKeWzblJnbd0q/067lPubSvo2cAtwr6SX1ltdw0Nf0gmSPtzWdAVwEfBN4C/qqapn/pInXgOxKfB7wAHAO+soqMf+D/BnbcfnUP2bfQj4YC0V9c4ngC3bjt8O/Irq6vW/rqOgKXAe8FQASXsCXwbuBJ4PfLa+snricB5fOuao8nUE+EPgo7VU1Gage7M98AbgxW3H99veq6wG+h3gY/WU1ROb2L6r7fh7tu8H7pf01LqK6qGDqH6JtfzM9iHlT+jv1lRTr+xs+6K244dtnwwgadA/W8tM262r8f+Y6pqdkyU9CVhaX1k98Rs/Phf+YOBc22uAG6bDCEKje/oAtn/VdnhKaVsDzKynop7Zqv1gzKJ2ay3CNICeZHt12/FfAZQfts06v2RgPHnM8UFt95/ez0KmUPv49oHAZQC2H62nnJ56RNIekkaAl1CNHLQ8paaaHtP00N9M0satA9tfgGocDtiirqJ65CpJbxvbKOntwNU11NNrm7SP3dv+JkAZJx4bmoPmIUk7tQ5sPwBQxol/WVtVvfUtSedJOoWqg/ItAEnbAr+ptbLuvRv4CnAj8GnbtwFIegXwwzoLg4ZfkSvpo8CzgGNtP1zangr8PfAT2yfUWV83JD0D+BfgEeCa0rw31dj+Ybbvram0npD058BLgXfYvrO0/Q5wGnBZazhkEEmaB5wKLOSJ/3bvB95t+5K6auuVMgx3OLAtcJ7tu0v7XsAzbH+jzvqGWdNDfwbVD9afAndQ/cm5HXA68MExwwcDSdKBwO7l8Hrb36qznl6S9A6qIGydo/gl8HHbp9VXVW9I2oPqZHzr3+464BO2r6uvqqlXfibn2z677lq6UT7HVrZ/Wo43Ad4CvMf2rrXW1uTQb5E0E2hNhVtu+9d11hMbpkzdlO2H6q4l1o+kLYBjgFlUs8wuBY4F/jew1PbA7qgnaT7w/6hmXN0MnAT8E9Uqw39j+5rxXz31Gh36kvaf6HHbl/erll6T9BAdNqihmrG1ie3aZxF0Q9KREz1u+6x+1dJrkj5P5387qM5VH93PeqaCpAuAB6mmSR9ENa6/CdXw1dIaS+uapOuohlCXS3oB1Wecb/trNZcGJPT/tUOzqeYKz7Y9o88lTZly0vPPqOZ8f832e2suqSuS/m+nZuAQYNYg/1KT9LoOzXOA44EZtmf3t6Lek7TM9u+W+zOothGcMwx/rUm6xvYL2o5vtL1LnTW1G9gfjF6wfUj7cbkK8gPASqo/NQeepC2pwuJI4IvA75X5+gPN9nGt++Wk4B9RTdu8kuo8zcCyfX7rvqTnUJ232B/4ONX5pmHw29Yd22sk3TYMgV88o0w0aNms/dj2p2qo6TGNDv0WSQdRXclp4KO2L625pK5J2gZ4L9UMiTOAvWz/vN6qeqtc6PIWqs95FfB62zdN+KIBIWlXqg7IXlRX6L5jGCYWtHm+pF+U+wJmlmNRDWEN8pTpzwGbT3Bcq6YP77yS6gfr58Df2v5+zSX1jKRfAauAzwNr9aDq7m10q6wr9G6qi3o+bvuOmkvqGUlfBkaBT1ItV7Cm/fHWvP2IyWh66D9KtVn7j+hw4sz2q/teVI9IOonxTwZie6DXcCn/dvdR/WJr/5ytnuLzaimsByTdzuOfyTzx6lXbfk7fi+oxSVtP9Pgg/2KTdOpEj9t+V79q6aTpwzsvqbuAqWL7pLprmGLb113AVLE9t+4a+mAJa/9CazEwyL/YltRdwEQa3dMfZtO9txHjK9P8xlX3PO+YHElPBg6x/eU662h0T1/SMiYeAhnYIQKmeW+jWxNchzAMJwInWkLCVAuUDR1JOwDzgSNs71F3Pb1QpqO+DDiCasXN71ItI11fTU3u6Ze1WsY1TCcHh42kjW3/dt3PHDySNrHdcdExSdu3FvAaBmWBtcOBNwHPo1rO/Ku2l9VaWJfKhZ9vAl5JtcDhfsBzWmt81anpq2z+FfCg7Ts63eourluSjpJ0jaRfldvidV3JOkCuqruAKXRBp92jJD0P+HYN9fScpLdJ+hbVvhXbUK1/tdL2Xw9B4K+guqbi+8Butl8H/Ho6BD4k9G8Hlkh6U92F9FoJ9+Op5rA/m2qNk78E3j0kwT8t9hudIkuASyQ9tva6pAOAi4G1lsseUP8AzADeZPuDtq9lgqHWAXM+1c/b4cAhZeXeafPZGj28A9W+uMCnqHobpwGPbeJg+6t11dUtSVdSrfdx+5j2uVQ7+exbR129UnpT415rMATXIXwAmAe8nGos+NPAa20vrrWwHikbjLyeaqz7mVTXI7zF9na1FtYj5Srxl1B9vldQ7c9xNHCx7Vr3RGj0iVwA23dL+jeqS/cP4fHQNzCwoQ9sMTbwAWzfXlY4HHQzqHbIGsoev+2Fkn5N1esXcKDt5TWX1UsPliWwT5M0m+oE7n2SbqBaG+r99ZY3eZKOtf33VBvDfKts1DSP6hfAZ6k6mPXV1+SevqTdqXr391Ctc72y5pJ6RtIS23tv6GODYuyiVsOkLATYmsO+H7Ac+Enr8UG+aLBlvH8/STtT/YU6sBcPTvR/U9LMupdub3ro3wAcP3aXnukyn7Ybkh6mCou1HqKaRTDQm6NLesD2hFd1DipJfzjR47a/069apoqkH9req+46psJ075A0PfQ3tf1Iub/WfFrbr6+zvm4M+3RUSdcO+HUUkyLpS7YPr7uObg3zORlJq4FOM3WmxTUkjR7Tt/3IOPNpt58u06sma9BDfT0M04qTG+KFdRfQI8N8TmbZdP4rptGhX3obd1KN6/+F7YfKut4DHfgAkm6jw0Jk5b5t79D/qnpq9kRLTWSZiWlvpe2P1F1EEzU69Knm0x5GNZ92TdnCbVjGu0bHHD8JeCPVHqQ/7H85Pdea2TJ0Jlh7R8DG/axlCg1jD79lWp8LbPSYPkzv+bS9IOlJwJuBvwCWUm0S8+Nai+qB6X6yrBuSJrzq1vbArw4raQ5Vb/+35Xhnqp+/Owb5+hiorjYG/sP2zSVfzgBeR3Ux6FvqXjCv8aHfbsx82pfZrnU+bTfKZ3kr8B7ge8DHbN9Sb1W9I+nKQb/AbDKGZc0hSZcDR5dgfC7V+bSzgd2Aq22fUGuBXSgbo+9l+7flav/3Uk0S2Qs40faLa60vod+ZpBNsf6zuOiarnK9YDXyG6rzFEwxBb2pvJl4hdWiWH277a/RNVFOJn1lzSV0bszH63wBb2z6mrDm0pPXYIJK01Pae5f4Xgatsn1KOa/8Ltelj+hN5J9WKf4Pq36lC8fnl1m7QrzaGaivB9k04xv4CGPjlhyX9PlXQvwbYGjiGaphuGLT/ex1ItQ8wtn9TdkUbZI+W1UMfBA6iutq/ZWY9JT0uoT++gT7RZPst4z0maeB7ilQrpN7Vuopa0lE8Pm56Un1ldU/SQqqT7ncC5wAfARbbPrPWwnrrWkmfpLoa/rnANwEkbVlnUT3yYWAx1bTUC21fD49ddHdrnYVBhnfGJelO23PqrqNXJD2NKhTfBOxqe1bNJXVF0jXAS20/UK61OBc4DtiT6vMN8oV1q4CbqIbmLrL935Ju9RDsjdsiaSbVxvbPAj5v+0el/UXADrb/qc76uiVpI2Bz2w+2tT2VKnNrnSDS6NBfx+5LM20P9F9C5Qfr1VRB/wJgc6opqpfbHug/oSX9yPbzy/1/AFa57AvcPqY6iMZcHX4g1Rr6LwW2sz00F6VJ2gvYAbje9g1119MrpRMyLtuX96uWTgY61Lple/O6a5gqks4G9qf6s7m14t9y2/9RZ109NEPSRiUEDwIWtD026P+vj6PagOOtVJ/lVcBTgLslXWZ74Pd/kPRh4I+prrX4O0kfs/25msvqlU7nXVrn12ZTDfvUZtB/OGJ8e1CdSLoBuNH2GknD9GfdOcB3JP2U6kKt7wKU6X8/r7OwHpgNnALsAlwL/IBqrvfxVLN4hsHhwJ62H5b0dODrwFCEvu1D2o8l/QHwAWAlcGwtRbVp9PDOsJO0C9XQzuHAfVQh8ru2fzLhCweEpH2BbYFv2v5VadsJ2GwYpmyW6YujwIuo1tx5IfBz27vWWlgPjF3eexiW+x5L0kHAh6h6+R+1fWnNJQEJ/aElaV/bV7Ydj1KNEb8BWGH7RbUVF+ulnHx/IdUigC8EtqRazOtP6qyrFyT9DGiNbQt4cdvxQO8ZIOmVVD37nwN/a/v7NZf0BAn9ITXBJhUC9h+GNdmHlaRFwO7AQ1QbwF8JXNk+E2TQDfOeAeU6gxXAj+gwUaTuX2gZ028YV7/lB/YHqiHmAJsCNwN3UwXIz+osqNcGOdTXw7Q+75Ke/pAa8+fzWurubcTEyl9ku1ON57+I6sT8A8AVtk+ss7ZekLSMJ/aCDfyUanrqJ23/dy2F9YCkw4Af2L6v7lo6SegPKUk3A3863uND3tMaGmXT8P2ogv9VwNNtb1lrUT0wzs5uWwNHAU+1/bY+l9Qzkr5CdQ7mYaqptz8Avt+6MrduCf0hNcx7kA47Se+iCvn9gN9SBccV5euyQb+wbl2G5f+upLk8/pfaC6mG7f7T9ivqrCtj+sPrQUnPak3PlHQk1TIMdwAn2X6g1upiInOBrwDvaa0t1DBPqruAXrB9u6QnUy2yNhNo3a9VevpDapjXponBN87uYFtRXaX7S9vH9bmknpH0fqqe/QjVGkpXltu1ttfUWRsk9IfWmDW9h2ptmhh8HXYHM3A/8B/AokHeKEbSjcAvgYuoxvOvsj1trhLP8M7w2miI16aJAbe+Wz5KOmrQlpS2vYukranG8g8A3idpM6p5+z+w/fk660tPf0hJ+gDVnqM/pTqB9ALbLmvTnGl7v1oLjFgP02GnqW6UJZb3plr88O3A9rZrXXAtoT/Ehn1tmhh+gziTR9KreXz21e7A9VTDPD+g6umvqrG8hH5ETF+D2NOX9FXK3Hyq/X5/U3NJT5DQj4hpaxB7+i2Stqfq6Ru4wXbtWyVCTuhFxPQ2rVaoXB+SNgdOpxrL/xHVKqLPl7QEONr2L2qtLz39iOi3srzEXNvfK8d/DmxWHv6i7eW1FdclSV8Abgc+0rp6uqyl9CHgubaPrK+6hH5E1EDSOcDZti8qxzcBi6i2hdzF9h/VWV83JN1se8cNfaxfMrwTEXXYuRX4xcO2TwaQ9N2aauoV1V3ARIZijYuIGDhPHnN8UNv9p/ezkCnwfUkfLkM6j5H0IarlGGqVnn5E1OEhSTvZ/i+A1gKAZV/nX9ZaWfeOozqRu1zSUqrZOy8ArgGOrrEuIGP6EVEDSfOAU4GFVGEI1WyX9wPvtn1JXbX1iqQdgN2ohnuut31LzSUBCf2IqImkPYC/pJrLDnAd8Anb19VXVW+U5RdeDuxSmm4Avl7WwqpVQj8ioockPZtq28eVwA+pevp7Ac8CXmL7nhrLS+hHRP9J+jxP3CO3nW3XPvY9WWWe/lLbnxnT/i5gb9tH1VHXY3Uk9COi3yS9rkPzHOB4YIbt2f2tqHck3Wh7l3Eeu8n2zv2uqV1m70RE39k+v3Vf0nOoTuDuD3ycaubLIPv1BI893LcqxpHQj4haSNoV+ADVePcngHdMhxOdPfA0Sa/t0C5gi34Xs1YRGd6JiH6T9GVgFPgkcB7whL1jW/P2B1E5XzEu23/Sr1o6SehHRN9Jup3HT+SaJy5dYNvP6XtRfVbXVpAJ/YiIGtS1QUzG9COi7yRNGHYN2c6zloXZEvoRUYeTJ3jMwIH9KqRGtQyzJPQjog4Hj7d3bNlmsAlq6elnaeWIqMMFkjYZ2yjpeVRLGDRBLVtBpqcfEXVYAlwi6RDbDwNIOgD4Z6DWKY3dWt+tIG0fW0d96elHRN/Z/iDwLeAbkjYryzKcBRxm+9J6q+vaJ4At247fDvyKagz/r+soqF16+hFRC9sLJf2aqtcv4MBB3hC9zbTeCjKhHxF9J+lfefyirBFgOfCp1g6Dtl9dX3Vdm9ZbQSb0I6IOnxzn/jCY1ltBJvQjou9sf2e8xyR9CRj38QFwInCRpI5bQdZWVZFlGCJiWpF0p+05ddfRjem8FWRCPyKmlWEI/ekswzsR0XcTrL0jYON+1tJr030ryIR+RNRhorV3buxbFVPjog5tj20F2d9S1pbhnYiYViRtbPu3ddfRC2O2gvw0cPp4aw71S67IjYjaqXKgpP8PrKi7nm5J2lXSPwP/CnwP2M32aXUHPiT0I6JGkn5f0inAHcCFwHeBXeqtqjtlK8iLgSuAA6g+1xaStpa0dZ21QYZ3IqIGZQ77G4E7gXOArwGLbQ/8ssrTfSvIhH5E9J2kVcBNwGeAi2z/t6Rb6w7EJsjsnYiow7OAlwFHAJ+R9G1gpqSNbK+ut7TuTPetIBP6EVGH46g2EXkrVQ69CngKcLeky2y/qc7iujStt4JM6EdEHWYDp1CdtL0W+AFwBtVc9pfUV1ZPTOutIDOmHxG1KVsmjgIvAl5Ybj+3vWuthXVB0iXAoWODv2wFeaHtubUUVmTKZkTUaSawBfC0crsHuLLWirrX2gryKa2GshXkxcDbaqrpMenpR0TfSVpEtQLlQ8BVVEF/pe0Hay2sRyR9AJgHvBw4mOpq3NfaXlxrYWRMPyLqMQfYFLgZuJvqKtyf1VlQL03nrSDT04+IWqjaG3F3qvH8FwF7AA8AV9g+sc7aujFmK8j9qLaC/Enr8bq3gkzoR0StJM2mCscXUU3dfLrtLWstqguS/nCixyfaNawfEvoR0XeS3kUV8vsBv6Was39F+brM9qM1ljdlJH3J9uF11pAx/Yiow1zgK8B7bK+suZZ+emHdBaSnHxHRJ9NhK8j09CMiemi6bwWZnn5ERA+VxePGZbvWZSYS+hERfTIdtoLMMgwREVNoum0FmdCPiJgC03UryAzvRET00HTfCjKzdyIiemsB1VaQp/H4VpDTpned4Z2IiN56FrAQeDWwXNI/UbaCrLesSkI/IqK3jgN+SrUV5HOBC6h2Brtb0hfrLAwS+hERvdbaCvI+4OvAC6i2ghwtx7XKidyIiCkwXbeCnBZjTBERQ6jTVpDLaq2I9PQjInpqum8FmTH9iIjeam0F+ROm4VaQ6elHRPTYdN4KMqEfETFFpuNWkAn9iIgemu5bQWb2TkREb81lGm8FmZ5+RESDZPZORESDJPQjIhokoR8R0SAJ/YiIBknoR0Q0yP8A5RIGBUaUGGkAAAAASUVORK5CYII=",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
+            "type": "surface"
+           }
+          ],
+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
+           }
+          ]
+         },
+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
+          },
+          "coloraxis": {
+           "colorbar": {
+            "outlinewidth": 0,
+            "ticks": ""
+           }
+          },
+          "colorscale": {
+           "diverging": [
+            [
+             0,
+             "#8e0152"
             ],
-            "source": [
-                "df['Activity'].value_counts().plot.bar()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "* Im Smart Farming Kontext dieser Aufgabe w\u00fcrde ein Rind normalerweise nicht auf einer Treppe laufen. Daher werden die Zeilen mit den Bezeichnungen \"WALKING_DOWNSTAIRS\" und \"WALKING_UPSTAIRS\" entfernt:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 47,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "indexNames = df[(df['Activity'] == 'WALKING_DOWNSTAIRS') | (df['Activity'] == 'WALKING_UPSTAIRS')].index\n",
-                "df.drop(indexNames , inplace=True)\n",
-                "df = df.reset_index(drop=True)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 48,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:>"
-                        ]
-                    },
-                    "execution_count": 48,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": "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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
+            [
+             0.1,
+             "#c51b7d"
             ],
-            "source": [
-                "df['Activity'].value_counts().plot.bar()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "- F\u00fcr jede Aktivit\u00e4t etwa die gleiche Anzahl von Beobachtungen. Wir k\u00f6nnten vor der Modellierung eine \u00dcberstichprobe aller Minderheitenklassen nehmen, um einen perfekt ausgewogenen Datensatz zu erhalten."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Wie viele Beobachtungen gibt es von jedem Testobjekt??"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 49,
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:>"
-                        ]
-                    },
-                    "execution_count": 49,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": "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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
+            [
+             0.2,
+             "#de77ae"
             ],
-            "source": [
-                "df['subject'].value_counts().plot.bar()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Die obige Abbildung ist interessant. Normalerweise haben alle Versuchspersonen die gleiche Versuchsreihe durchgef\u00fchrt. Daher w\u00fcrde man annehmen, dass die Anzahl der Beobachtungen von jedem Probanden nahezu gleich sein muss. Es gibt jedoch eine Spanne von 200 bis 300 Beobachtungen \u00fcber alle Versuchspersonen hinweg. Ein Grund daf\u00fcr k\u00f6nnte sein, dass der Wechsel von einer Aktivit\u00e4t zur n\u00e4chsten in der Sequenz nicht scharf oder deutlich genug war und die Beobachter die unklaren Beobachtungen in diesen unstabilen Phasen nachtr\u00e4glich gel\u00f6scht haben.  "
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Gesamtzahl der Beobachtungen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 50,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Number of observations: 7349\n"
-                    ]
-                }
+            [
+             0.3,
+             "#f1b6da"
             ],
-            "source": [
-                "print('Number of observations: ' + str(df.shape[0]))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### PCA zur Visualisierung\n",
-                "- **Die PCA ist eine einfache Methode zur Visualisierung hochdimensionaler Daten in einem niedrigdimensionalen Raum. (Vorsicht: wir bezahlen daf\u00fcr mit einem Informationsverlust-->aber f\u00fcr Visualisierungszwecke ist es OK)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 51,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "data_visualisation = df.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 52,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "s = StandardScaler()\n",
-                "data_visualisation = s.fit_transform(data_visualisation)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 53,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "#We want 3 \n",
-                "p = PCA(n_components = 3)\n",
-                "data_visualisation_transformed = p.fit_transform(data_visualisation)\n",
-                "#data_visualisation_transformed = p.transform(data_visualisation)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 54,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Features before PCA: 561\n"
-                    ]
-                }
+            [
+             0.4,
+             "#fde0ef"
             ],
-            "source": [
-                "print('Features before PCA: ' + str(data_visualisation.shape[1]))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 55,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Features after PCA: 3\n"
-                    ]
-                }
+            [
+             0.5,
+             "#f7f7f7"
             ],
-            "source": [
-                "print('Features after PCA: ' + str(data_visualisation_transformed.shape[1]))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 56,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "0.619964301867613"
-                        ]
-                    },
-                    "execution_count": 56,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
+            [
+             0.6,
+             "#e6f5d0"
             ],
-            "source": [
-                "p.explained_variance_ratio_.sum()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 57,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[<matplotlib.lines.Line2D at 0x1c5212bc790>]"
-                        ]
-                    },
-                    "execution_count": 57,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": "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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
+            [
+             0.7,
+             "#b8e186"
             ],
-            "source": [
-                "x = []\n",
-                "for i in range(0, len(p.explained_variance_ratio_)):\n",
-                "    x.append('PC' + str(i + 1))\n",
-                "y = np.array(p.explained_variance_ratio_)\n",
-                "z = np.cumsum(p.explained_variance_ratio_)\n",
-                "plt.xlabel('Principal Components')\n",
-                "plt.ylabel('Variance Explained')\n",
-                "plt.bar(x, y)\n",
-                "plt.plot(x, z)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 58,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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c6ss4K4V6bxVF2aaq6thQjzedlKsS4nSmzWY7Adpfl06KEwyb2vKFvlzGIIQQQgghep+ENyHONNoyT+hR6DypQVMIIYQQQvQaCW9CdEUvLK3sNePnBn4VQgghhBDnBAlvQnTFCVa7elUvLPMUQgghhBBnHuk2KUQXlFw8lYUjLqfk4qkndhzp9CiEEEIIIXpIwpsQXZB75ANWO4+Qe+SDE5oLdyaNFBBCCCGEEKcXCW9CdEHAoMkTmAt3MgZWCiGEEEKcTR544AH+9Kc/6d9fd9113Hnnnfr3Dz30EM888wxut5vExETmz58f8PxrrrmG4HFkH3zwATfddJP+/a9//Wuuu+46WlpaAh6fnp7O9773Pf1xq1evZubMmfr3b7/9NuPGjeP8889n1KhR3HbbbRw6dKg3XnZIEt6E6IKAQZPj58KYmT1qGJIem86C4T8k/cNne1S5E0IIIYQ414wfP578/HwAvF4vVVVV7Nq1S78/Pz+fK6+8ko0bNzJixAhWrVpFd2ZZP/HEE3z88cf873//IywsrM39BQUFAefTfPnll8ydO5fc3Fy++uorduzYwQ9/+ENKSkq6/yK7SMKbOD2cwFLEk05rGNLTrpMnULkTQgghhDjXXHnllXp427VrFxdddBHR0dHU1NTQ0tLCnj17GD16NCtXruRnP/sZ5513Hlu2bOnSsZ9++mneeust3njjDSIiIkI+5uGHH+b3v/99m9uffPJJfvnLXwYM2b755puZMGFCD15l10h4E6eHjgLNmRTsgoRsUHIClbtOncHvlRBCCCFEKAMGDMBkMnHo0CHy8/O54ooruOyyy/jkk08oKCjg4osvxuPx8O6773LTTTcxY8YMVq5c2elxP/74Y/7617+yfv16oqKi2n3c9OnT2b59O/v3B36+2rVrF2PGjDnh19cdEt7E6aGjQHMGV6pCNig50cpdR87g90oIIYQQZ4+W4mLKHl1AS3FxrxxPq75p4e2KK67Qvx8/fjzr1q3jm9/8JpGRkXzve99j7dq1eDyeDo85dOhQVFVl48aNHT7OaDTy//7f/2PRokXtPubYsWOMGjWK4cOH88c//rFHr7ErJLyJ00NHgaYvK1V97KQ3KDmD3yshhBBCnD2ql6+gdtUqqpev6JXjafveCgsLueiii7j88sv55JNP9P1uK1euZNOmTaSnp3PppZdy7Ngx3n///Q6P2b9/f9566y0eeOCBTh97++23s3nz5oBmJCNHjmT79u0A9OvXjx07dnDXXXfR0NBw4i+4HRLexOmvLytVfSyg0cnJcAa/V0IIIYQ4eyTMmknc9OkkzJrZK8e78sorWbduHQkJCRiNRhISEqitreWTTz7hkksu4aOPPuLQoUOUlJRQUlLCCy+80KWlk8OHD2fNmjX86Ec/YseOHe0+zmw2t+l6+cgjj/DEE0+wZ88e/bbGxsYTeZmdkvAmzmindOi13/6yE72OosoG5q/ZSVFl3/2lRgghhBDiZAnLyCD1sYWEZWT0yvGysrKoqqri8ssvD7gtNjaW9957j2uvvTagU+R3vvMdXn/9dVpaWgC48cYbSUtLIy0tjVtvvTXg2N/4xjdYvnw5N998MwcOHGj3Gn7yk5/gdrsDzv/cc8/x4x//mPPPP58rr7ySPXv28IMf/KBXXnMoSnfaaPa1sWPHqsEzGIToyML8hazet5ppw6axYPyCDh9bUldC7q5cckbm9E4l7PWf+faXjZnJwsSELl9HKPPX7GTl1lJmjBvEoqkXn/i1CSGEEEL0kj179gR0VBS9J9R7qyjKNlVVx4Z6vOmkXJUQfUTbS9aVPWVa8xCgRwGrDW1f2fi55JhNXb6OUGZnZwZ8FUIIIYQQItgJV94URRkE/B1IAbzAS6qqPqcoSgLwCpAOlADTVVWt6ehYUnkTfanXK29CCCGEEOcAqbz1ne5W3npjz5sbeEhV1QuAy4H7FEW5EJgHvKuq6jDg3dbvhThlTnrzECGEEEIIIXrRCYc3VVXLVFXd3vpvO7AHGAh8B9CGW+UC3z3RcwkhhBBCCCFOvtOpT8bZoifvaa92m1QUJR0YDXwK9FdVtaz1wsqA5Haec5eiKAWKohRUVlb25uWIM8Qp7RgphBBCCCE6FB4ezrFjxyTA9SJVVTl27Bjh4eHdel6vNSxRFCUKeBX4uaqq9YqidOl5qqq+BLwEvj1vvXU94sxxQo1EqvZD/hJf85BQs806u18IIYQQQnQoLS0Nm82GFFp6V3h4OGlpad16Tq+EN0VRzPiC279UVV3TenOFoiipqqqWKYqSChztjXOJs093Oka2kb/E164ffMOpu3u/EEIIIYTokNlsJqOX5rWJE3PCyyYVX4nt/4A9qqo+43fX64D2aTwHeO1EzyXOTumx6SwY/kPSP3zWVynT+A3Bbtf4uTBm5vG2/d29v7u6ck2dkGWiQgghhBCiJ3pjz9uVwO3AtYqi7Gj9zw3AYmCSoij7gEmt3wsRmlYhy1/S8W3BEof6KmrtLYkMur+osoH5a3ZSVNkA9CBIdeWaOqEtE83dldv5g4UQQgghhGh1wssmVVX9CGhvg9u3TvT44hzhN/C6w9tC6ca+tmV5RazcWgrAoqkXd3+/XUfX1MXrOKFloiKAzO4TQgghxLmk1xqWCHFCtApZR7e1F466sa9tdnZmwNduByn/awq+ni5ehzZvTpy4E2p2I4QQQghxhpHwJk6KosoGluUVMTs7k8ykqJ4dpL1w1NUKHZCZFMWiqRfr359QkAq+nm5cx7mirytjUsUUQgghxLlEwps4KYKXK/ZIe+EoVNXuZAi+nlN1HR041csK+7oyJlVMIYQQQpxLJLyJkyJ4uWKPdCcc9dF8t4AwdBqGtWCnelmhVMaEEEIIIXpPb3SbFKJT2nJFfclkV1vu97Q1fy90hQzlTOsUmTMyh2nDpp2y8KRVxqSZiBBCCCHEiZPKmzg1/PeLjZ/bfpWsp0O2+2j/2ZlWSZJlhUIIIYQQZw+pvIk+F3KWmv/w7I6qZO0N2e6sIpc49Pixq/Z3r4LXwWOlkiSEEEIIIU4VqbyJPhdy35X/frHxc6G5DlrqfIHJv/rW3r6yrlTk/B/TUge71vq+3rqi4wvuabVPCCGEEEKIPiThTfS5TpcaJg6F8FhfYAqL7Vpg6sqySP/HvPe4799qFy64l5ZcnupOj6GcjtckhBBCCCG6RsKb6HP6vittOWKovW3dDUxd6fTo/5hrf+0LiF05fi91kTzVnR5DOR2vSQghhBBCdI2EN3HydLQcsa/b7vsdv1cGhnfB6djc5HS8JiGEEEII0TWKqnZlHdnJMXbsWLWgoOBUX4boK12cvdbXS/vmr9nJyq2lzBg3qOcDw4UQQgghhOgDiqJsU1V1bKj7pPImTp7Oqmut4S43ysTq0o3A8aV9vRnoemVguBBCCCGEECeZhDdx+mhdVplzyXRoHSythTa7086GgxuAru3V6mhppDYwXAghhBBCiDOJzHkTfS/U3LRQt7XOdEvP/oU+S82/wca01kDXFcvyili5tZRleUW9+Up6TcjZd0IIIYQQQnRAKm8iQLsVqy7uVwspVKOSULeFWFbp32CjO8slT/XSyM6WeUrXRyGEEEII0V0S3kQArWIFBC4tPJHB1aHGAHRxNIA+ZoDu7Xs71Usj2wtnWji+YfStgHR9FEIIIYQQXSfhTQTwr1gFhCW/sNXt5iGJQym64gmWbS5idnaDr6LXxeYl/pW+M6la1V5L/uPheBCLpp7er0EIIYQQQpxeZM+bCKBVrDKTovSwlLsr93jYShwaeHsXlO77gpIXpzP+8//H2k0fdO1CtEpf/hL9ppyROUwbNo1rU29l/pqdFFU2dP8F9pZQe/b8aBXD4HA7OzuTGeMGSadLIYQQQgjRbVJ5E+1qr3oU8vYO9sQdWf9HMvmU3ORopiqrgZs6P3mIZZVaINLmtAGnbmlkD5eRnurlnEIIIYQQ4swl4U20y3+/WYe3V+2HlTPg2Ne+74PCzIApD/OH94p4P7wekvuR1ZWTd7CsslebkfS0EUsX9+wJIYQQQgjRWyS8ifZ1NdjkL/EFt37DQ4YZT3IslhFXcB2QM3rOCV9Wr1avetqIpbM9e0IIIYQQQvQy2fMm2tcabEryngyYSdZmRlnrfDZmrDwe8vz2hOXuymXDwQ1EW6ID9oD5H6eosqH9fWyd7C87Idq1d6OCdq7NaDvXXq8QQgghxOlKKm+ifePnUuJp5H5nEcX7tkDRByy4fhm5X/8rsOtjqCqUX0Ur5+KpcDCfnAHXBDzEv3tkc/kt7e9jO5ExBZ3pQQXtTOp62RvOtdcrhBBCCHG6kvAm2pc4lNz+aRTv20KG00lOxU7IX0LO1Q8AxxuWBAz2Vsp9YeuCm33HGD+X9PwlLNi7BaxrIP1q/fDa8ycOnsia5lX8+OIhPOR+E6oeDFym2Yv7y7o95iCE9hq5nK3Otdd7JumN32chhBBCnDlk2eS5ppMliMHLF3MGXMM0YyLPG9NIj0qDC25u0wZfm122LK/oeJVsz+v6aAEuuNm3H04LdK2042w6uImNpa9hVP5D7O5/+Zqf+F+f35iCjnRleV93xxyE0t4YgLPVafN6+3L57BmqN36fhRBCCHHmkMrbuaaTJYjL8or4z47t7Gx+mRdufJD0nWtYsH87hMVAS70vlA37VsBzAro/KiGqZHtep6SuiNyCp8hJHkK6y328Orfndd+ySnxBkaqHfc1P8pf0yXLG3q4iSeXj5CipKyH3nfvIOVBAOkizmFZSFRVCCCHOLRLezjWdLEGcnZ3JzuaXOeR6n9xd/Vgwfi4tRR8RVrsfZ8x5WJrrfJUPvypYYPfHEHvIxs8l1/EVq51HYFcuC6qqfQHyYD4c+5p0YIH2nBkr4f3HIcR5OtOVD7LtjT/oqe7sB5Og13O5u3J9vz9DxrJAxjPoevv3WQghhBCnNwlv55r2GnS0jgXIHD+XF258kNxd/XwhKDadpf0XklL1f1ykwCW710J4bLfb6ufc8CK0Bhdcbt/trZW3gCCZOBTCYn3hrpvn6fEH2Z7OeqN7lQ9p/NFzAe+zBF8hhBBCnKMUVVVP9TXoxo4dqxYUFJzqyzi7tRdUXv+ZLzCNmdkmMBVVNvDMxq9Jch7i59aNxF7b2lCki6Gns4pTQMOTpKiA4xapKYH39SL9uipspH+xKuRr75PzSeVNCCGEEEK0Q1GUbaqqjg11nzQsOddoe97ylwTe3sG8s8ykKKIjTKworuUHbhMlZlPgsV6cAP+c1m4jCb2pwrYlIRtOBDQ8gYAGJW3u645OGlzo1xUb0+1Zbz1x2jT+EEIIIYQQZyRZNnmuGT8XWura7ikLsZzSvyJ2b5ZKfdUKNruOte6FW+A71q41vkYm+9+Bf+6DH72qH1OrNE0cPBGAnAobfLHKd3C/c2kNT+7NUn1hy6+SF9AMpRNtKnidNGeRpXhCCCGEEOJMIpW3s1FHFSdtT9nutW2qb8FjAvyrXoP2/I3/d7SQaZYBx/d3JQ6FaSvAbPV9X1sScMylny9l9b7VrN231ldxyv4FjJlJ6QV3BJxHa3gyaM/f2lQFtfu6smTS/3pL6kpYGGWi5JLp7VbU0l1uFlRV+7pf9hZpZy+EEEIIIfqIVN7ORkEVJ/8K2KaDm8i5eKqv3XpQqNHCD8CiqRe3GQGQDr5Of35VqpLkIeReMYOco4dJN4YHHLOhxR3wVavu/XnNzoDz6FqfW3rBHfx5zc5u73Pzv97cXU+zunQjDJvGgvb243VSmeuRvjimEEIIIYQQSHg7OwWNA9D2dm2r2EZxfTHbKrbx/LXPt9l7df3IFLYWV3P9yJS2SxAZStEVT7BscxGzsxv0UJW7K5fVpRupH/QdLLXTucHu5L2vF5IzMgezfQrOGjtm65SA87S7FLKzcNcJ/5EFOZYudIHsZGxCj/TFMYUQQgghhEC6TZ412oat47fdMNrEe8X/x8TqCp7kGMUOG9OGTfPtW6vaT0nek+TGxlBVPZk3CtzMGDcIgJVbS5kxbpAeiOav2cmWz7ayOPVDLvvBAkgcqlf1qo5czhsFbkZevIFDrveZNmwatw97qEedIkO9FiGEEEIIIc4FHXWblMrbWSJ4yWPgbYNYZHLDl2/w/CXTyR12+fGKVP4Scm3vsLo+msmDIph7yeXc6f4r9aPvBgbp1bGiygbqm1z8Lul9+te/zcJ3jpJzw4t6B8WiygaiDEXcMPqnvFfWr7UdflS3KmeawKHfQoj2yPgJIYQQ4twi4e0sEWopYvCeNYD08XMD94CNn0uOpxFiY8i59E7iNzxF7O5/AXD3dY+Qu+tpciw5PLOxmt1ffk5EvwZy0y9mtfMI9dtexlI7Xa+QaYErO0MGUAtxMsjgdyGEEOLcIuHtDNLRX9lDVasylXIWmf4PlNZh1+6fMFtNwX+nWZGawjJ1LrOzMvE64f6DVzLZXUZL02h2v/kTNriOYnfaUZnBncY3ubThA/pZp0P/a6g6cjmvFnRvb5r/awCkaiDECQgYdyGEEEKIs56EtzNIt//Knr+Ekp3/JNfxFVXmB3mjwE2Dt4zEAVv0wOS/3BLgw2NxlPSfwVXGZThqS8AaCUc+Z96421ll+AlH3Em82nwztw+7BoZBlKGoTeORjvas+b8GQKoGQpwAbdmyEEIIIc4NEt7OIN35K3tJXQm5USbsmaPY4DzC5P55zBg3HWfcKlbve42tJcdYMun3AUsrDzccYmfzBpIjj/F6XTlXRaUwTYWcoh1UV/yWW370D5bkncfKraVUhRXp4wT0pihl/yVnZA7L8urb7RYZ6jWcLlWD7uwfOtuaqrQUF1O9fAUJs2YSlpFxqi9HCCGEEEKEIOHtDOL/V/bOgobWwv+6wdcxzRKtP66kLoadtjp2fzWGp9W9vDCulkWH50HtYl4ofZ9Drvdx14UBYFUMLKiuB7ebN6obeXvjXgBuykpldnYmpfu+YNeqx9nScB07m3dyyPU+ANePvEcfOaDxDzv+lYIF4xeQV7yb+968n3lX/JTsjAtDvr42s+r6YKlldyqboRrEnMmql6+gdtUqAFIfW3iKr0YIIYQQQoQi4e0MpQeNg/ksmPSCb0YaQNV+yF9CzsVTAQJCjhaABhmmsM+pstNWR0vlI4TV7qdk4yMUxo3GWZdFhPcCbk7czGxzP6jdiccSQ+XAaSgorCssY8a4QWQmRfHpyj/ybddGxoR/QZ3nEv47aDITB0/k9x8+TlHdGN7elcDVI5IBePbDj9loW0XDh9NZMu064Hig29awjDL1AxZ/AtkZzwe+PnxBSvt+/f6Pcahl+u29qTuVzXZn1Z2hEmbNDPgqhBBCCCFOP4ZTfQGiZ3JG5jDNmEjOgQJ4//Hjd+Qvge0rSN+5hgXjF/iCW9V+eP1n5G5bwup9q0nlWa7uV0tpTRMvRc6mImwwfxl0CWXuT7nUXIKN3dSHP0z6mNkQFoPRWc8d8V9y69g0hiRZ9YragCkPUxE2mIFqBRce2MiCBjeb9q7hkOt9xgzbzA2jTSzMX0hJXQmWhDws8VuxJOT5rrNqP5Ur72HLZ1sZaLie88zfZN4VPw18fcOm6UFq4uCJWJVUKg9fwXnmb3Z56ah2/q7QKptdqehpDWLOhiWTAGEZGaQ+tlCWTAohhBBCnMak8naGSo9NZ4FlMLi3g/+c9fFzobkOWup8oS1xqN64xJ45iuvMycwu2k788GEsHvJTttUm8XTdIr5ZD9Msh7E3fE1hvJ0I65vUfe4mtqWelrihhI2fy9ubyzlQ6eDtXeVcPSKZQcMugdlr4L3HQfGdOyfvSai3k5NmJLfsv3r1bO6ldxITbg6YL3dZ9essToWk6/5CZtKNbV9fa2WtqLKB33/4Cg61jPMH17Bk0u9Jj+08NEkbdSGEEEIIcTaR8HYmu/bXEB7rC2yaxKHYW9xEH3gDe7Ob6Nv/CePnsqx+Nxtc5UyIm0BFzACMo+9m0bCLmfOv7QBYlVQWTHqBklduI7r+GDkxTbzs+Q4p7jLK+/+EhxKHMju7gQZvGc64VZTUxfgqVIlDYfqK43vURv+IHCA3NoaJgycCx5duBgSo1mu+bPxcSOw4iC3LK2L3V2O48Hx44cYHuxTcwFet21axTb+OUIL31p1tjUi6Q5qWCCGEEEKc3iS8nckSh8LNz7W5uaiqgUv8vgKMq41jgzuRL2ov4sdKHoO3LuWl5F/x4OThREeYuDdLhfwlhF2+kOs/fgXjRXO4JW4IT3tTUbwKRZUNZCZFEZb4DhsObkAp/4Tpw37PQ+85SI0NpzZyJWXqB76T9U9j9b7VvGnbwZj+Wd269lCO7y+bhtcJ89fs7FK42nRwE8X1xWw6uIkrB14Z8jHB1bmzrRFJd7TXtERCnRBCCCHE6UH2vJ1liiobeC1uFh/F3ETCTQspqmzg038v5DtVG3nBYOKS9C+xxBZS5t5C7q5cfe/WoD1/g+0rOPTxK9x25Pv8uVAhMymKmAgz6wrLWJZXFHiimiIaNiyktKaJrSU17Nt/qb4XLWdkDlYllSb1KB+Xv0vurty2F9q6D4+q/Z2+Jv/9ZVq40q+ng+ME75sLJfgxs7MzmTFu0FnTiKQ7EmbNJG769DZNS7RQV718xSm5LiGEEEII4SOVt9NBa4dIxs893jWyh5blFbFyr4mmcb9g0bCLmb9mJ1vKrmZxKlz2gwW4P/gtRU4n6UoYOVHD4fWfUXrBHbxTcxE/jBtKRNZ3GeI63pQkuKvinNFziLZtI6fmCBGpsQyqiiAu0kxtYwSPXHoD6bG+7pJ/yP4Tv8t/lsxEa+jw1NpYBehaBa71Pbo36w7AL1yFOI7/UsjO9roFL+fUgmJv684MuVNFa1oSTDpRCiGEEEKcHnolvCmK8jfgJuCoqqoXtd6WALwCpAMlwHRVVWt643xnne4GmQ5ooebmoUdYuPL/cfPIB2jwDmJVwmCcdid/arJgs1i4vL6e9A+fgWNfc6SkmrCjDYSZ9qPu+Ad31Ci8u+1mXj98AGd1Ng9cfaW+RDE9Np07LvsTR6r/iPcbP2RiQx5VRy7njQI3b+8qZ1BCpL5nbOMPl7V/oa173kounkpu/sI2oabN3rPW92gQsGjqc22O47/v73RsVHI6XlNXtRfqhBBCCCHEydVblbcVwFLg7363zQPeVVV1saIo81q//0Uvne/sEiKAhNKV6o1WOVq48v+x2nkEdj1LYtxgVpd+QlXJl1xT6iIsfSQ5adFw0fdhz+sMuOAOPioopc6Yiqm5kv2mz6jz1PJeaRXOmjKi8lIDqlF/LlRYeeT7jPzyLQ653mfyIBczxk1ndnZml/aMldSVkPv1v8i5+gGWbHuZjaWvUd/s4ulrj488aHOc9t6jEHvnujOv7WQ5Ha9JCCGEEEKcWRRVVTt/VFcOpCjpwDq/ytte4BpVVcsURUkFPlBVdURHxxg7dqxaUFDQK9dzNlqYv5DV+1YzedB3sNRO16tsobojlpR8SO4nvydn5Ex4ex65EQq3N6lkNtZSd+EPiZ3+55DneHjDT9lQ/jFXJY4hMmIQ1UeuwKqkcuvYNN7eVR5wzhtGm3iv7L8BYVKrmF0/MkV/fHBjEe11TBs2jaojl7PRtorJaceHd3f1OAHaWXp6LnePPNWk0YkQQgghRPcpirJNVdWxoe7ryz1v/VVVLQNoDXDJ7VzcXcBdAOedd14fXs6ZL2dkDvXNLj7ZfhFHqkqpb3IRE2Fmy2dbubHkSf4Sfy07jDt4NPseBiUMhsHj4cB7pDfW8mt3DGuT76Ffuo29GTnMf/oDHr3pQq4e0fpj0cKPwQGA1RzBvVnzmP15AQcqyyg8up9yZQMNH07noUkjCE9Zy6CEHBZkBC4B1Cp/89fsDKyc+YUr/yqUd1giUXmpbRqEtHuc9rSz9FSr4G0trmbZj8dKgDuJ2uteKYQQQggheuaUNyxRVfUl4CXwVd5O8eWc1tJj07HUTudIlS/MKCjMzs7ku7Y/cFn1Ol40lVAeW8/iTxTGpffz7bEaNJkFY2byJ8cklnyhMGPcTWzdXM2BSgePrdvNu1p4aw0/c5KGEu21kxPj4ekPP8ZmXEVa6ljMKW9gaS7FkpBK7q4tne7fCm50oh2/rtnFi6afMjv7IbzO0FXDDo/TnnaWVc7OzmRrse/1LssrChkAz4RmImciaXQihBBCCNG7+jK8VSiKkuq3bPJoH57rnDE7O5P6JhcKCg9OHk5mUhSZP1jAq/9sYjderE2JDDUUMjHuHtBa4Memc0tlA0Xevdib3NyVnclLeUU8etOF+rLCe7PuwONpJDdcIadZxXjRHL765GUs8VuJtlZgc5SSEZPB3MxJ8Pk/YdDkdvdvldSV8I99udz9rZzjA7VbQ9WrNRdx0b4F/LNuJh9Wx3Kg0lfpa6+q1uXuj+3MjctMimLZj8fqSzBDzYg7k5uJnM6k0YkQQgghRO/qy/D2OpADLG79+lofnuucoAWteeNMvrlsylxgKEVqCi/Ex+JkK3GYec/rwrxtCX+8PY+SuhIWvvcQOXX1XOoeR9i+t3C4b+DdYV9S1tDA1lW52JrGsH3Xdj4em8r6o+/DsGk0Fyrs338pF56v8MsrbmPTwU2+ytSHz8IXq/j5hT9k8bv1zM5uaFM1C9mEpDVcfW/VvcSa3uOjmkj+VvkDhiRZ9apaX1XAOluCKc1EhBBCCCHEmaC3RgWsBK4BEhVFsQEL8IW2VYqi/AQ4BNzaG+c6l2n7t8bvWcEg10ZKPI0sjTRSe3AH8S4XlVFgx9X6aAXQgtRGqLcz37WFI+FHeL7lAA+X2pnx1Xt8x3OIC01fMsxzBA5MxnPhdzhkG4I5+t/cdPE3eeDq32OwVOnXUHrBHRwpqeaz2ou56NAC1rb8hIdm3HT8Iqv2M2H3Xj50X4TTmt3mNcRe+yCEmxl8wR3MKFQCqmB9XQHTQmJwBS541ps0OTk1eqPBiTRJEUIIIcTZrFfCm6qqM9q561u9cfxzQjvdEkv3fcGR9X9kwJSHmZ09hCMHCrHWV3A0LJVf1Bxmd30phMF1LgctJHKQJjIMkczJ/h2l+77gos8KaYowklNvp8oQz4r4frwTpkJYFM7+Yzi0ewTvKuP4UXge34oLY3/F5ayrWIslfiuTB5lYlpeKM24VG0t9hdPm8ltYeeT7/DP531xleo86Yyq+EX++11Dyym3sUI/xG9M3yLr6yravs7UCNwhYNCzwLq3yNXHwRBaGmP12orraBKUr4w5O1Jm0z66vA5F2fI/djn39eqDnDU60Jikeux1jdLSEOCGEEEKcVU55wxLRyr9b4vi5epA7sv6PXFb9Op+uh6QZf+Fm81reSinlLRTCmw5ClIGBbpjoPp8ZA69hXfG/GJF4DY+9+Su+5zAyq2kHU5r60WyCP0fZmWKvp8HcH6KSiXRewwONyWQoZWRYDhK1/yAzhkTyunodFyQn4azO5tWCUr550RWcZ67n2tRbGTjM1xF0cNYjsCeRWK1BSNV+WDmDXI6xOiYaBp3Hd0JUrToKLVoFTBslAH1bgWuvCUqXm6ScgDNpn92JdI3sSvDTjh89ZQpx06efUIMT7bkeu106XQohhBDirCPh7TRQVNnAWsck7rzQ5QtDWpDbtYaM8Y/y6RfgveaH3Lnhp1TGFIHRF4pSmtKZ7HVwwP5NfkYJ/97+DxY0HuLnzrV8FhVGuOrmxkbYqw7i5ZhUdsTW06yEs9A0BOvX71N34Qiqs+7k+xX/pn/9QRwxmdx94AoONlsZP/gHzL46kyhDEfYmN7u/GsNi/soL47/HItMaiJ/LhyN+xWO5u3n0phhGffEMsce+ZkbCEBj0TXIuDT1wvCuhpat70Hq6vLGzJihdbpJyAs6kfXYn0jWyK8HP//hhGRm0FBdT9uiCLlXNgsOh1iSlpbhYr7yFepwQQgghxJlIwttpYFleESu/UKga91MWJQ6FC26GHf+ClnqSP19KcuY1LDy8hkrPF2AEMGFWFW6t/5pkT382JbyDJbyG31iS+HO1hbrYgeApxmGJ5M5+5/N15U04GgfgVt9j67Hz+dT9NnkpaeQ0l7L0ihoaPlX4QrmWx1zX8HXcR1jt1xBR5yb87aUM836D6w2fUTbMzdeuz/jbhg95rKyEumYXj5VO50Clgzn/3UhGWhOTlKtwJt3Dgmtvave1arPqSorGMqd4u94x01/wHrQO37d2ljce76Kp+pq7BC1H9XcqljB29TWeDk6ka2RnwS9UqOpOpa+9xwZfs8ycE0IIIcTZQMLbaaDNMr09r4PXBWEx0G8obF9BTtJQypVGvgqPo8ITDpZ61sXDcGcp3vAoFIyUOiuZnZDClMMuFEskYaYqPoqJJD7i36RXz2Rb2VTuNb9MnukAqyOioa6QeW8+QlTtfr50X0tFxl4spq2YzEaGHKhkgOk9sr15DDAc4elLppPbPICc0gL2eQfw+MEruevqTJ54aw8tUe9TrBTwxtDLyeq/hZK6i9oNQdqsuve/LAXKiI4wsWjqxe0GqPZuL6psIKKumH8mr/It4Qzy9Ma9vFlYzo0l/2ZQ/To+LakmacZfQlbo2qsGhqrsSTOT7mkv+HW0z607lb6uPlZmzgkhhBDibCDh7TTQZple8MDp/CWkX3Aziz9/ld/XTOTDxjKMsf9hZtVR3k5IBJpR8eB1h1NOOR/EOrm9Iol3Bw4Eaqgxu7ncuoxG+wO86L6Rh93NQD05SSN5qf4aEqo28LLnRtKUZCJNTg56GnjRMB7c8LZ3HHcnfslV2b9gAVBnfIb7D17Jh8fisOUVsWTGaFYXgiUhFcXQzIaDrxETbtZDUKiw4z+rTuv86N8UxT9AtResluUVcdGBFVxleg/2JMKwwBlvSmu3zffip2M2GZhXdjWXtzOku70ljKEqeyejmcmZpidLEjva59adSl9XH+u/nLKrSzKFEEIIIU43Et5OR8EDp29+jqLKBm4/VEmV6U2cNdncaprEpzH/xer9FolUkhQdhvNQKu6oVyi2WPhLcgMZ1oe5Lu4jmvZ/zL01ZexWVlFjjGRz5GyezJlKUWUDn7y8lhtbT3O4MorMCyMoc3+Kg2YMlTBs2IV4xv+E+ZvLfQFs+p9ZUNnA7L8XcKDSwX8LbERHpHL9wPtZXfg5kweFM3HwRB7+4GEaWtzs2T2egxVW4HjYyUyK4oUfXgqgd3789thspg0ztwlQ7QWr2dmZrG35CXXG1ONNU/w8OHk40REmfpSdCXyLy1sDZCjtLWEM1bjkZDQzOdP0ZEli8D63UHoSCjt7jiyfFEIIIcSZTMLbaUBfGjjgGtJ3rqH0gjv4c9AMtA/eWskVlmfYEGsimka8FLIhSmFS3RsU73mKoxYji9VnSWg+ykPJyVSGeRid+BV/vPavLCn5Ky7nc4R5m7gyvICNLUdZtWMwnxeZudGxhh+a3uNK09f8pu5HqKWfghUyTAf5OvkYOXGJvLgrg//s2M7O5pd54cYHyUxK59GbLuSxdbtpaHGzrrCMrcXVHKh0M2PcdDYdXMuGgxsAcCp2hiTd3mFnxwZvGZaEPHJG3tluB8oAVfvJ/GQJD02aC4mh99f5VzOLKht69HMJ1bjkZDQzOd0FB6SoSRNx5OfjKi+jpbi4S0GrKxWzngStzp4jyyeFEEIIcSaT8HYa0JcGHsxnwd4tbD90hDXuGBo+nM6Sa4ZQkvckJc1buKXxGNFKJOObP+KV1Au51HGYz5R0rNZq+rnK+TDpEBijcBgVBriN/HTna/ytdDDfi9jOAMMRtoYNZGFyGjWWeoo+XUzu1OWsbfkJLRWHSK/dz+/C/gnV5eR6orHHpLKaaBzhETRWuxmc8RmHXJtZnB9JfNMPqGi0YTO+ynmWqcwYN4jrR6bw9i5fda7MOZEtZVtIDh9EjPV7PHD1WDKTokIuocxMiiJxwBZW7wtcbtkh/7EKNz/X4UNBljr2Bv/AFhyQ6tasxWWz4bLZqE5JbTdodbeS1pOg1dlzOguNZ3pXyjP9+oUQQgjRMQlvp8jHu1fx5Lan+cWlDx1fGjjgGuqMr/B/jU1YlK1EWK3wzw/JNdaxNiaalqhEflNZydOJVj7lCN6wCAymQ9wYs4LGpiY2RJi4ssHA5fWR/KpuP+luN+bqp6iMH8kR6zU8a46kxVIBwEhLBcvyirh+zDiWbl/InQPeIrL/aKI/eJScOjtL487jukHX0VQ1iXWFZQzufzVOpZk91WM4WFHKsAvfxhK/lYQBqSy61rfw8uoRyQD8I38TtgYbl6dezoIbr9Nfc3shqttt84P3BHZCljqeOP/h1wARY8fiyM/HnpenP8bUvz8eu73d6pt/6NNCoH/IaK/tf3f05DkdBdOeHONUhiZZFiqEEEKc3SS8nWxV+yF/CU/WfEwxLTy57Wlev/BTveI0f3s8hUXbGTNM4d6ij6G2hByTCeIzWFVzB0dcNmbX/Z1PE5MopZIBmPE2FDMjejhqs4XCCl+Q+n3if7i70UG8MZlL6t5nS8TVHKu+jkyrl/M4TJTpx6zcWkr+wa8oVzZQkjadJUdXgtpIbtJ5bPDWMNkbjrclkZuyFG4dm8bbu87XK2w3jL6H98oS2wSuosoGqo5czpiESj4utpGXupvsjAuB9kNUt9vmB+8J7ER7Sx1PxYiAM5X/8Gv7+vVgNoPLxdFFi0l7YSnG6Gj9vpa9e0l7YWmbEONfFQsVMiqfX4J9/Xo8djtpzz5z0l5bcKj0/9qTY5xoaDqRINje9Z8u4VIIIYQQJ8Zwqi/gnNO65O8X5kFkGCL5xaUP+W6v2k/J2tnYlcVcPuglBnm/5rC9lIVJyTD4SnKu+i2XXryTHTFWXs66iTsu/iXnmb/JcG8EG6KsvO2xc58xkvH9lxNz3sd8Euvk3+OmkZmaBEC4txHVmURj5V28f2A+1eYBjLx4AymDP8ASvxWX9X1QfZeSEzuSacOm4azO5s3CcqIjTFw9IpnZ2Zn60sjsjAtZMH5Bm/b9s/9ewLqdZew4uocy9xYWf/JX/X4tRJ0uLfa15aq5u3JP9aWc9sIyMvRAYE5LA5cLTCYMCQk4bTYAYqfegiUzE2dREdXLV4Q8RupjC/V9cpbMTKImTWzzuObCQlqKi/vy5QRImDVT73ipvc7q5Su6dQ3+x+gJrQumFrJqV60K+R52xv899ncixxRCCCHE6UMqbydb61K/K8fP5XX/odHvPU5uxQd8GBMNFvgSOJDUnxITfOYspumtP3DUepC0jFQ+ry3j2Bct7P5qDM0pRVzpreV8l5F5UQcojrJwnbOIbwybRs6Aa2DnG5SYTCyLc6A0VZKWMJxrzk/isPEfHKp9n7iWsTjrxmG2fhOuHQLhsaSPn8uCxKEUVTYQZTjepbG9ZY/aXrb6JhcHKh0kpX9Cs+koEaQS3TyROf9qO4z7dJiX1u3lmue46uUrsK9fT/SUKSgWC86iIpoLCqgoL8dls+HIz6f/gkdpeGdTp0O5XeXlOIuKqFuzlujsbD0omdPSfHvnlq/otIKlHStq0kT9nD3tSnmiA727ulyzvQpYb1T/OnKuNmqRiqMQQoizjYS3k6V1uSTj57ZZ8ldU2YD9cC05DjtHwiLZazIz3BhJiulbuFr+x8EwhWub95JQ78JakUbZ2O9wWcIt/KH+JY5GHGR0QyO5MWGUmSwMcLop9Q5gwYEvSf/kFUqaKri/fzLFlgYsCXlYLb4ljHv3jsFpqcbpnMjUoSOZnZ1JkQrL3D/h+mMxvL15J7OzMwNC2qi0ON7cWcaotDj9upflFWFv8nWcvCkrlRnjBnHD6J/xXtkADhZ9g/f2qPgP4/YPem8WlgMn3kQk1PLHroTDbi/XPMcEf/DVPvhHTZpI3Zq1mNPSMFituCsr9YYlDe9s6jDEaCHFnJYGgNfhoOzRBfqSS3NaGtFTpujnau/Dd0txMbb75uAsKqKxoABnURHQO10puxt0uhMQ2jtv8OiE3t6v1hfHPBPIHkAhhBBnG1k2ebK8/7ivQ+L7j+s3FVU2MH/NTp7euJefH72JCONABrQ0cszoZWB9GQ9FVDMp7MfcYm/igZpqflKp8GbN92guu4XPi8xEHBnKFXYr1ab+lJlcxDlNGPCyO/wo/639DOyHyY2NpthiYjARTE6bjorKyq2lVFbH0FI+lfMTM/WljFpl7bF1u1m5tZRleUUBL+GlvCLqm9281Hq79ngVlRnjBvHg5OEsmnqxvqTS4+wHQGpMWJvqnYLCjHGDeqWJSKjlj9p5/F+D9n73dHTA2cB/eV5n2ltqV7dmrS9opaSQdP9c3OW+EG5OSyNq0sQ2x/c/Z8KsmURPmYIlM5PoKVMwWK36h2tLZiYumw1jdLQegiqfX0LtqlVUPr+kzbU5i4qwZGaSPH9eyCWLHb3WjpY5trf0sD3tXWMo7Z3XabP5QmjrEtTOXkfwbd35uZ5LTnQ5qxBCCHG6kcrbyaL6fa3aD+8/TnVJDbaay7ki+WN2nN/IG5k/obrkfwz3xjE20sVDtdsJN3/Bw7U1pLvdNBgaAVDwzYAbvu8jDqnlZDqHEaOquNx1bI62kOF0MrHWwhbjGG6NjABjEzlX/Rav9VJm/m0rAF4gJtzEgm+P1C9RC1LXj0yhYPtWZjiW8vTKm7ll4jVkJkXps90evaltAxKDpYrcXU9zbcOtvPW5m9nZmUSHmQG4dHCCXv3SnnPDaBPvlf0XgyUHOLFlk6GWP4ZqjiIjA7pXiQiuQGnPjZ4yRf9AXL18BS6bTQ9RRxct1qtg2v16k5PWcxqjo7GvX68fwxgdTcKsmThtNo4uWhxyH1xH1xaWkUF0dnbA/S3FxZTOvguXzRayAcqpqkQFn1er2jny83G1vv7g1xLqZxZ8m1SYQjtXK45CCCHOXhLeTpZrfw3hsb5lk/lLYNdaxgLzIj9jdkwcdoz8rfQV7F4704zwduNRPo30FUY/HTCAJ48e49pmB3cm/53l5v/yhx2ZKCOTyDsWzQCOkdVQy60NDUSrCUypM/BPz0zWN1/E1UoiMeG1LN6znj1fVXCoxkpqTBgAI1Jijl9f6+DrRRPmQmIyV+99B7a/Qoq7kmVh57Fo6sVcPSKZd1vHAUBgF8eF+U+zet9qtpYcY9fO69haXM2jN11IdIQpIEBpz3novV+zsfQ1PtpfxYtTFoVc2tjVbpChlj+G6jApIwO6tyQw+INvqDCnBS0tqGnVMP+Okv5hD3zLLhsLCoiaNFE/R0txsR78ji5ajCUtjbCMDJLun6uHu+6ofH4JrhBVrL7gf41d3YenPU4LttYJE1AsFpLnz2vzOI/dHrCUFNr+LM7VPW1CCCHEuUbC28mitbff9y4UfQARCdBUzepYC3ajEYMXUixJnOeB9c4K0hUnEIHBC81G+EO/eDIrWlgSW42jSaGi6QjXDb6OjOY0ih02jkRZMRPOtyuTuZLPcSofcJVpC5+Ujqc2/n/siK3HqVQyJOl2lv14LMvyitjy2Va2vbCYhqt/yMVfLoZjX/uu9ebnYPxcGuqPcVGlgwlZarsvSwtYEwf7PsRfm3orvyot50Clg/8W2Fj6wzEhn+eszsZZU8b+6ktZllcUshKmDy+HXtmb1t7IgHPJiVQi/J9b9uiCNjPb/IOc/x654ADT8M4mnEVFNLyzSa8yacEPs1nvVqktXQw1E66rlSZTSgqAPnvOf3+adhztuD1tbhHqfelsH16oYNvebDytStnRNUmFSQghhDg3SHjrQyErR2/Pg9oSsPgqTd+vc/JpuIdSi5F9jiJMgNtopMgSzrR6O1nNLTwfm0pm7Whejt2Mw6hg9iiYW9Jp+no4v6nZwCtRDRxWUvlPxV1scbm50xhPutXND9X3uMz7Fab6oyxVUohMvoqZk8aSmRTFvVkqd+58miEc4cCHe4Aj0G/48cHXiUOJiunHJfvfgD3nwbDQc9VCBaystCYO1TSh0n7oe+DqK1E3JqEmqe1WwqQbZN/raWDxD2ah5rO1FBe32QOm3eZ1NGCdMCFgoHfCrJl64NEqd5pQQc2/ehfqtWjVMP8lm8HfAx0uPezJe5MwayYeux2vo4GwESParYSFqmKGOo//47o7TLy71386dWY8na5FCCGEOJ1IeOtDoYJNyaU/InfHC+QMv42abf/lX1EtDHa10KREUmWGaJcLh8ECVVdgi9hJgieMxqYBjGsp4WpnNU76c29dBR83DwHW8w3TIZKa0pjVcgeqmkRDlIW/mucyPq6WY6VOCmNqeSPRyCPHjpFcvomKslT+selZcgzxvuDGAH7r/DF3J37Ju1G3Urmhngkjt5G7O5fZQ7/BFyO+IufiqaSHeH3aQO7Jg1wBAeuhySOIiTB3uDwxMymq3aqcRrpB9r2e7pXqrNKjVYzAF5q0cKTdps2D0+4Ly8gg7YWlegUvVJdL/yDUXvWudtUqPHZ7wFJLLbRpla7oKVPw2O3ETr0FV3kZjvx87Hl5bQJhT0cG+O/pay94+L9/tgcexL5+fZtRC2EZGR1WO4Pfk1A/g+5c/+m0b+50uhYhhBDidCLhrQ9pgWbi4IkszF9Izsgccg++xWprGFRuxZXSnw1UAxBDDNEuOzVmM8MbzNSHF7MlxsE2qwOXsZp3jWZyqtzcVp/Kp87z2eEdwi9MK3GqBlZHfoPKhC0MjRhOreVtjtrj+YofcWG4m1eTGnAZDCzq1w+rvZH+WxayO6yBGhz8acxMLBfcQVqhwsqmya2t+8v4sPp/ELOFxV99hUMtw17yBtFFr5NTV4/xojn8udDXMGVZXhFvFLiZMW56wJ40WZ54eumoitEbe6WC96Vp+7SsE7IxWKMCzuGx2wHfQO+6NWsDqm9aUPEPKVqwC/4AH6rypp1HC2r+5/Q6HERPmULS/XP1YGCMjsZlO6w3CgkbMUKfPWdJSwu516yz97Oj97Sz52nX0d5yy+6OE+juz/Z02jd3Ol2LEEIIcTqR8NaHtMrRwvceYnXpRmiuJ+eKX8Invydn5EzY8xoNjS3sVFuoNNZjMnoBA42WBi5y1lCOFZfRd6wDYR5u6z8co7seV9U4FkTmsjg+DIjHrX6MEh1OufsLFJqxRFdS3fAqH8b4gpvJqzKypYltcftpVuMBqHM00WA8hqm+lO/aXkG5Yg6Qwk5bHbbKK0k0G3g4+1Z21X2M3Wn3XX+9netLm1l55PtA5w1ATodB3KLjKkZP9koFh5DgfWn++7SC96v5d31seGcTtatW0VxYSHhWFkn3z9WP5bHbA4JdsFCVN//mJ1qYDK72QWAwiJo0kaOLFpM8fx51a9YC0FxYqC8FjZ4ypcf77YKFqgxqTVk0sVNvaTPk3P/9DrX/rz3+P9uO5uW1N6z8VDqdrkUIIYQ4nUh4Owly6uqh3s7E6gpywz8g54YXSd/4W9iznj/FpVPSYGsdpG3CaghnTq2LOE8DH0eE02Q0Eu7xUm+B3ZZmAL5l+C+/tERTbLEAcKWjiUG1aXzdcgExiZsY7mzCYipmb/VkLjW8z4MGEzHVhfxfeAoDLvwFW7b+ngV1XxF1dD/2g19wmesQr73dDJnzePy7F/H2rkRmZ08jMymKaUykpK6EaK/KxOoKXrXG8+0BJj2Qhaqw9cUg7lOpq10vT1e9XcXorHW9//k6G4at7XPT5rv5z1fTQldwm//OXpM2My1q0kQSZs3EVV5O47ZtAY1QtGvxHzNgSUujZe9enEVFhGdlET1lCk3bt+OuqAjYz9fZ+9nZIG7/yqC+ZNTvNXY0KgA4oeAY/LyeBlHZkyaEEEKcGhLeTobRPwLHfv7ZWMxH+74gz5bPw2UmrgdKohNZYbTzi2PHWJ3Qn6bBV/L50e281c9Ak7G17IbKyOZmKg1GhrtcmPBSbIkgwuOlyWigWQFjZAnWsChcRpVjEbGUU09Y8iE+KJ7PA/1fJt3t5icRg7jvwAbsld9lt/IuhV6Vd0yT+JbxU/7UeB3FheUoKDw4eXhAxSw9Np0F1z7NwvyFrN+3mmnD+pGZdF27L1ebp3ZTVmqvDeI+lXq76+XJ1tUqRlc/kIdqohHcaVI7X3sNN7Tjm9MG4nU4MA8apFfawFf9AvA6HJQ9uqDNNbX3mlqKizn88wdQHQ4qFj7G0E3vYE5JQXU42jRCCfW6tVl1luHDqfnb3/C2LvP019n72V6406qKlc8v0ZdjHm/i4sBgterVx1DHi5o0kbo1awOeq733dWvWdukYwdfUlWAf6ufWWeiTcCeEEEL0DQlvJ0HukQ9Y7aki3OMBo5GKpiMsZiiXG+NZ0lzMxhgrn4abGexq4uPydzHEG/AajeAxEoGbJqORQ4DdaMSKysM1Dho9RoY2JLAl2kx5+BEOW4xEKMeoqx6HJ7EFPF9wZeQh3Jk25rqt/DHzZtYnJ3OodCMT0vqxwXkvr9msZLjLuCXBRITLAE5QUdsdZt3V7o/+yynPhuWS50rXy65WYdprohHqOaEac2hVrOrlK3BszgNAMZtpKijAGB0NoA/+Nlit7R6/vVChOhwAeJubsT3wILFTbwGOB5TgMOjfLdMYHY2zqEgPborVStSECQFLG0Odt7Plh8Fz3bRmJmWPLgjofgkE7B/UjqntBQx+bvBYAu35XV0e25VgH+r3oqfVRyGEEEKcGAlvJ0HOyBy22PKwNVUA4HVbuNnVTK1iZ7clCQCbxYILJwDxXhfHjEaiPdC/9gpcsR9xR10tL8fFU2yx8H5EC7fVtvBB3EGmNUSwWBmCwdDArIse4tDhQdww2sR7Wx4g50ABj8W7qImq4x7HeMZXT2YKO7j36A6Kw9byGj/i55Eb+KZjI0e8zfwt6Wc8NHkEpdWNbC2uZlRaHPPX7AyowN0+7CFefLeI2dkNejA72/e2nStdL3uyvLInz/E6Gih7dAFRkybiyM/Xg5p1/PiQg6iN0dFETZqohy4gIAhBYKjw2O00FxbistnaLL30D5Ba8xJva9hrLiyk/4JHAQjLyqJm+XKS589rs4xRC3uO/HwGLXuJsIwMPaw0FhSQ9sLSNnPj2htYri3rbN69G8VkwutwtDvKwL8CV/boAsKysjDn52NMSMCaNhBQMFit7VY5eyrUz7in1UeNVOaEEEKInpHw1se0/VLpCcOwHa4gwhBOk6mZ/yRXsrshEZvFTLjHQ7PRSJRXpdHj4Qf1dbweFcPBMBgYXkhys5UxLUe5tSyVF2KSiXDW8lD/elrC6skId+KyHMRZM45d+1OJjoCBUeexYNILYF3C9eowPIdWoqoX8+W2Un4dEcZO51j+oUzhvPgIUq99mLriJA44ruN8i6/qsaqglAOVDn71v0LcXt/r0Cpwoapywbe1V7k7UWd7SDzVetIkojvP8Z+9pgWd4Nb4Gv9jBnegBNq0/ffvWJn27DO0FBdTOvsuXDZbu9fjH6i00QUN72zSm560FBZiSUtr9/kum03fQ+e/f0+7zT/k9V/wqN5p0/+1hmVkYE5JwbF5MwARo0fr4c5/757/e629F5aCAlw2Gy6bjbjp0wPes+AqZyhdDVAdVRLbe65/85hQy16lMieEEEL0jIS3vlC1H/KXUHLxVO7//GmK64u5LuVKplkGMNFl4P95DmI3tvC1OZxp9XUUm0xsi4zAZjbTYjSwLjqK+ceO8XhCIonGo3xktfIbzsfUcgfmxhRWxr6CO+wTklqM/KK6hv+LzGBzdTZb7FVUOVw0eMtIHLCFiZdO59035rOw7iu2u9+jyejlW96PeSNiMp/Z+wFN3PmmiyUzfkvTrnLe3FqKgsJOWx0Abi/EhJsC9qyF6jAZfFtnXSi7K694N4s/+SvJ3ut4/0vfbWdyA5RzRaiulHowam0MooWlzviPBtAClX9FS1sq6H/OQcte0v+t3R479Ra9kqftH9OWRGqP1f4dqjMkELCEUnu8/5w67TavowHwhby6NWsxRkcHXKv/MbRg579frfL5JfroAv/qn37OrCyqX3wRS2ZGQGXSPyQ1Fxbq+wi1vXUABqsVoE3lsqu6Gr46a94iowCEEEKI7pHw1hfyl8D2FeQ6vqLYeYSMmAzmNHpI37uFkoTBXGJq4evIaPph4JghDFXxlbciVC9xTpha6+LJfvHYLEZGumB8XRh7qr7L/1P/ToTZyGMV36TK44HqCyj1fExx3Y14zGCPfoXksG+xt/ETPtj3AWv2bMYbUcNvYs6nxf5dzh/u5teuamYoMMvj4j9FYdQ3u/nFqzsZOziBG7NSUFEprWkiyWqh0eXhruxMnt64lya1gvgB+cy99M42wSm462Rvz3lb/MlfOeR6H5eiMmPc7DO+Acq5or0P7qGCjkZr4AEEhCqP3a6HvVBLK4NDV2NBAcnz57V7LcH7x7RjaOfW9skFd4bUrz9ENSu42uRtbAq4v6NGJu1Vx0Lxr8C5bDas48frYxe0ZZtJ98/VA3L18hUAbfbWaUs42wt+HQlewqk9Nziwd/SapeImhBBCdJ+Et74w3vehM+fiqXDkA3IGXAOf/oWFQ8dQHp3IR7Vf0d/jYo/FwB6LhTSnb69brcnEdQ0O/hNn4bDFTJrTyZyaOl5VBlE46F88TTMXuFw8WNeA42g/XvbE8aYyjmWWp7mr3/lUxu7H02hi3/4riEpqxl57AdHxe9hRfzW/njyBrxqeY21pNea6gywYPIBqx5WMK1/JGs93WVfYEtAZcqetjkqHk7U7DnOg0kFYyhosnq3EhJvb7P/qrJX+iS53nHfFT1n8ie9rdsaF3X6+ODU6mtfW3od3/7lsWvOS4L1iwUEsuKKjLV/UBl43FhQQP2sWloICwrKy9P1iltZKXnAzEe3coTpD+gcTe16ePiMuOju7zXGU1uqWOS1Nr6h1FFiCjx88/DzU++v/NXjZZnBA1oaV+6j6c3uyhLG9gerBIyNkX5sQQgjRuyS89YXEoZRc/cDxQPPhsyw8uoXVMdGE1dUD0OJxg9FCmtPFr4/VsDbaikMxsCPMQoXZDMAIp4fPPZn8Z1A1zUYAMx9ZzCR6jvC7li8YZ/gKC24GK0d5ssnE72LG8sOmSj7LcHM0IY6D7jT6my5na0MNefuqeNh0GOrt/MATzdOOSXy36V980/QeA8LD2ThyPtePTGFZXhEKCqU1TQxJsvLoTReyqqCUJvV7xA9IDdlxsbNW+ie6By4740KyM57v9vPEqRWWkRFyqWBH+6X8lxD67/uKnXqLvnTQfwllqHNqoUUbwO0sKqL6xRd9e9RavypvvonqcAQsZ9T20HkdDXjsdn3Pmlad0/aRucrLffvUWputHF20mOjsbP3x1gkT9D10lsxMvYFJZ4JDVGdhL3gId9iIEYSNGBGwlNN/KWlwJdO/EtdeQO1McIAMNePPv4mLEEIIIU6MhLc+ogWaLfteY6TXyLfVaDZ5rdQafH/5bjKZSHW6ua+qhXo1A7vHQ63RQ4XZDUCyy0PZ0Vt5o997NBsh3OPB7DFygcfDT+pqaCSCYYYjvOu+BNVgIW3iIn718StcVv8um6Oq+bz2GNMui6KkyEpYyhoc6lRQwW4w8GSYlXf3VDEz4zYiw0wMnfIwg+Mymf33Ag5UOrgxK0WvwmUmRXH1iOTWV3VDyNfaWSv93t4DJ06t7nzADxW0/FvzBy8XDF5CWL18hb5cUgtvDe9s0veChWp2oi3Xq16+guT582h4ZxMtRQdw2WyYUvqjWCwBrfW1a9QColZNihg7FkN0NGFZWQHX6CwqwrF5M9YJE1AsFuJnzaLs0QU4S31/oGjavZsBi35PwzubiJo0MWAZaPD75f9e+lcq7Xl5bV5bR++7VrH0Xwbqf19wsxctYLorK2kqKCB6ypSA8QPaXr+oSRMDlqoGnz84YPoHRo/djjktLaAaGEy6TgohhBDdI+Gtj+SMzGHbgbco9jZiM7jYFeHC4K4FixlUlRaDgTKLgQ9ioDHcSL6pEbP3+POHO5vxJr1OvWLiKkcj4Sp8nyHsbHBwT38vsa4IrqwcwBrPLaSfn0pC3Sco0Vfjcnu5dOi1fHroP2zJV0izLsYSX8sADrE0KoUNLVbAwdCh26g0zqL5uqd5cquNL2xb9WrbQ5NHtFne2NHSx85a6ff2HjhxanVnmZ0WtPzDV2f8P9CHCn/+ywDbu47g5Xuls+8CwJSUTMx3vsvRxYsJv+B8fVyA/zVqIwyaCguhpYWjixdjHXupvozRmJpK9csv42ls1Kt8tatW6cskvVVVeiMW/zluwY1KWoqLsd03Rw+SCbNm6vvUHB99pA8I18KQ9litkqW9zuDgF7xENdS+M20Pobl1yXZ77622FLOxoICwESM6bHASPBrBvn490VOmtBkB0d7PqatLNk/XwHe6XpcQQoizi4S3PvDx4Y95cuuTzBw5k1e3LWe/0YHNYj7+AEXB4lFxGhWOqTHENh+FqAhcBt/dER4v1SYzu8MMgJcMp4lii4W4+q/5ND4Sm8WMzeLmsBrPY43v8qw7nILSAibZvXzd7+f8p+wTWpRyosLfYmZdETZTMjcc+4JVqq+ql2ZNYyg38UZBGXvK6zlQ6asGDkmysuzHY0PuS+ur9v/izNOdToHB4UvrfOjf5TFYcKUoOPz5V3fAFzSq/7s6YC6bf0MN231z9FlykePHU/HYY+By0bL3a5w2Gx67XV82CL7A6T9iQHU49A/lHrudutYll80FBQF7ysKysji2dCkoiv56ted4HY42wUoLjYbWCpf/9167Xa/6lT26QA9bhtYh4loTEv/goy3/bNm7N2CZon8lMmrSxNbraSB6yhRip94SUFnzf2+NreevfOopnEVFhI0Y0WY0Q3s/N//fkY6CTE+6TvblmIETCWAy/kAIIcTJIOGtDzz+yePYHDaWFb3O4PPG01j+LkavisegYPCqeA0K0R43eE3sD3MxxANXORrxqAqHLCYOW8x8rfh+NIrHxMwqJ4VRdnLq7Ew0NTKv30AaWgZyh+sLno1zcn7FQM63qtxbX06S+89sHXk1ZeVQofbjH9F2bBaFP1kzucD4YyYPPICzOptpWaOJMpRz/cgU/ltgQ0X1VdyUcnh9CaUX3MGfCxV9qWN9k4ubslK5fmQKc/61/fjjZd7aOac7nQL9K2+WtDS9eqQt79M+LPsvz/MPXlo7/+D9WEDrgO0GHJvzsL/zDrjd+v4zTd2atQF7z2z3zQGXCwCv3c6RBx/Ca7cTN306TpsN231ziLzqKhSrFUtGOqaEfhisVj0E6sstDQYwmWgpOoCzNehZx15Ky9VXU7tqlb6kM2rSRL3xin39er0BSfXyFYRlZWFYvx6v3a6/di1Ytez9GndFhb5Hz78zZN2atXjsdr0jpn8AcuTn4ywqovL5Jfry0+CqnfYa4qZPx5KWpu/v81/WqQU+231z8NrtWDIz9SplqHEHwdfR1d+R7jZxCT5PV5/TVScSwGT8gRBCiJNBwlsfSI9Lx+awkR6XjtXke4s9BoVopxOPIYxGr5H+bi+7IxXAyDYiSHM6Gel0cVdtHX/sF4/daMTkVUk/lk6+9Suy7LH8IS6MSiWSpuYM7rW7WZvkpNhioTShAZMay+VGFwMcceSkXk/pMS/XVx/gYGIjEEVVzEhmTrqVZXlFvFpQSpShXK+gHd/TBrzuG3NwpKSalUe+r9/8ZmE5M8YN4u1d5awrLAMgJsLcYRWusy6UIIO3z3b+lTetsmTJzNTDWKhQoXWQDG7n7z94WqsyaUsVcbvBbCZ+1qyAAd3WCRP00FO9fAXxs2ZR+dRTemVLCyba0kqXzYbz0CFwu1Ebmzhv9YuAb+i1s6gIY2Ii3qYmVIcDnE6aCrZxeM8DqK2VNfBVFYGAZYehumVaCgoCzq81eLGvXw+t/7+hulxYJ0wAjocCbWllqApbeFZWm8Hk/hW9+FmzaMzP14/n390z+Hj+P6/k+fP0kK39XIPHC2iBL9Qev+AREF0NVaHCVGeB71QFMBl/IIQQ4mSQ8NYHfvGNX4DqCy+3DL2FDSUbQAG72QyKCrhxtRgJ97hpNhoJ93iwWSzYLBY+jginwWjE5PXiNhg40u9r9htNfBXh5KDFAriBfbzouYg5doW/xnloMTbRbGxiab8I/nZ0J4vyfkvOlS/wv4O7uaLqP2RYk/nxN2bw040PkBoTxrfGXI4z7hNK6mLYWnyMJz95iaFhN7Do29/CfMEdHCmppmLMNEYmbuCG0T9lYNR5AFw/MoVVBaWMS4+nrK6Z60emdBi+OutCCceXY24trm53yaY4c/lX3rS9ZOa0NBwF2wJCVPysWbQUFgZU2MKysjDn5+MqL8Oel0dzYaF+XP+RAJhMGOPi8FRV0VJYSEthoR5gDFZrQDAz5+cz4Jmn9eupW7NWP6Yppb/vMRnpKB5vwJw4jWI0ojocGBMT8Tgc0BrkFKuVpu3bcVdU6GEnuOFHcOUo1H1RkyZSu3atrzpoNuOuqMBgterBVgtu/ssnQ82nA/SljVpFzmWz0ZifH9AQRqv2NRcW4iwqwnbfHD3AJcyaiau8HGdRETX/+AeOzXn6c/yrkP6BJXjUg3ZfqNtDVVKDq2U9CVMSwIQQQpzNJLz1sjVfr+Gpz/6A063gws6SHUtAafu4I+G+4JbhdDKztp4l8XHUGA00GI1Eebw0GH0b4BqNBkxeLzfb6/k8PJwdFiuqqmAwH2ObYsBhNALgdYfzA0csubG15Ec5KNq5ghfvXMSyvEv4U3Ymc9/5JWXuLZRVQ0bMIbaWFhMTbmbdziM4I/PZUeNm7SYjV1W+wryyq4k89C6HXO/zXlk/FoxfwKKpFzN/zU7eLCxnSJKV0pom3t5VDtBmL5wW6G4YfSvQfhdK8HWg3FpczYFKB8vyimQ/3VkmuHW8y2bzhYitW1Gbm8FoxGu368HNv429paBAf7zLdljft6ZVbtJeWErJ9Nt8ATA8nOjWypbTZsORn68/tmLRYj3MuWw2vZkIoA+3NkZHY0ryVaDDhw5r0wUz6f65eB0OGj/7DABDeDipi35P3Zq1NH3+Oe7yctytIU7rhJn27DPY8/L0ymOoDo3BTVwa3tkELheG6GiSHnmElsJCPeRp+960cKgFv+BgFDyeISwjA0tmBi6bDa+jIeB82jw5LfxpSy615Z3a+w8EVA6DK6j+4dF/1IP/74F2u1a18x9XoP07uANpV8NU8FJJCWBCCCHOVhLeelPVfv74yWM48KB6FRRD0P2KAqqK1ePBYTJh8nq5yd7A/8XFUWX2/SjMXi//71g1r0dFsissDC/gNBpZERfL5U0tNJhVQAVLGfudHq5odHPQPBSXOZWR37oVzwd/xtBcS1Kkr4p1/cgUZv+9gO+Nm8H/DjWRmWjl9otuZdPBTVybeit7vtrPHruXWOc3meF+nQHVr7M4FZxXzOe9sn4BwUvb/3b9yBTe3lUe0Prf/9/Hm5sMYtHU9rtQgq8T5bIfj9Wrd+Ls4v9B2v8DfONnn+FpbsYYH0/0tdcGLCcM3t8FEDl+vN6QBNCX7A145mmOLlqsV+7geMMR6/jxhGVkHN+nZjRivfLKNqHC/6tWLQu1JLDl669Rm5rAYMBls+kB7dBdd+MuL8fUvz9hI4bj2JyH19FA2aMLaNi8GXd5ORULH8Oy7KVO92IF7/cDsKSlBTQR0Z6vDQb32O1YJ0zQq4xAm86TBquvoq19BfQh4+a0gTg25+mdIf27eCbPnxcwiDzUe+ZfhTNGRwfstfN/H7XbtXEE/ktJtfDnryuVOY32u6MtqZWOj0IIIc5WEt56U/4SHj5awR+S+xNpuIxKNR9VgWi3B0VVqTeZQFFoNBhBVXEbDLwUF0eL0UCYx4NTUXAZDCyLiwWg2Wikv8tFLUbsRiNNispVjka+MkdSq4Rjs8BIp4OElhq+NJfyl0++otI6FK+pmD31HzPzbxdQVNnAoZomXt0K/3fnr1j6+VLW7lvLnNFzePHderZ8bWRI0g85UO1g5aCbeWhMBP0vnkrukf+22auWmRTFogmRkP8EV0+YC4m+D4LB1bLguW6d7WuTUQLnBv8P8Fpw8A8FwfPW4HhlquzRBfryy+AqTXR2tr4fzpGfT/8FjwLHw0X/BY/qjUnMKSl6oxT/apEWDLS9dsF7plqKi3HX1PheiNEI3uNzPQyt++4ixozxVbGsUTQXFuLYnIepf38AvM3NHJo5C3dFBS1FRXiqq0mePw9XeQWVTz1F0iOPkHDrND3saq8HCKigtTdqIG769ID7gqtvsVNvoWXvXiLHj9cDlTbAXHU69RAFvjl81gkT8NjtWNLSGPLWm22CmHYu7ediTkvDkpkZEBiDu08Gj3/w/zlrYxj8g3Wo+XT+PxN/nY2PCNXERFr7CyGEOBNJeOtN4+cyFZg6fi7j3robtfVmu9Hgq7q1UlQvqsG33FFVIM3pxGax6PfbLGaucvja98e7VI6ao8DYwD5zBANawqiyuLC4w4FmqohD8bYO9naX8Y3yOF6Mv4zaqvF84KzkG+nxKJYqhp2fx4KPqtle9QkA0ZZobhh9KzubN5BzYQ6fFyVwS3YmJN1Ebv7C9veq5fsamgBw83Mh34bgMHYiYwakocnZxf8D85C33gy4L9RMOC3kxc+aRRy+D+kVixYD4K6s1AOFt/V/Ly6bjfJf/4aIMWP040ZnZ5O+6hX9vMHz1bR9ZNr8NP/Kkna9rvJyaGkBIOKSSwhrXTIIgcFDCwEum621EjcCFAV3ebl+PU07doDbzeG596O2tICqUvnUUyTcOq3N+2VOSwsINP6hV3u/tFEDHc3H07puHnvhBdzl5Xjs9pBVNa1JjCUz09egpTX8tdcERDuPdj3+gTF4yazWwCVsxIh2xz/4C7V3rb19bMEjDoIfF3z9oebmSYATQghxJpDw1psSh8L4uZC/hLQIK/saW29vXS6pBTjVcHw9pdNgoMZo5NLGRmxmMxVmMwOcbn5U38DjCUkUWg1AAxYPVFgMxDoVLHXDSDceJAMHd9a0EE4zfzANZ0RdBv9uvpkLLReyi3oceOgfHc5FF+9h9b43iVB8e3oilCRyRuaQuyuXQ6732dPYL2B5o7ZUMuRetfFzA752JVx1txLnT+bLnV066gQY6sO6Vh2qWb5cD3tapctdXk5TQUHAbQDuioqAtvzBXQ61zpGmlBTChg8HQHU69QYg/sFHCyTmtDTAF6ZSf/eYr7L039WUTL+NpEceCVkNUsxmHJs36x0xtSqcu6ICjEbfnr9WxpQUbA88GDD7TpvD5l8d0t6Po4sWk/bCUr1pS8M7mwDazMc7umgxltZrB1/g1Y+fnd1mz5322rVlqFGTJmJ74EF9LlxwKPIP3ME/v+Als9q1qk6nXtkLNS/OP4T6v68nMnog+Nr8O3BqP3fZJyeEEOJMoKiq2vmjTpKxY8eqBa0fxs5Yr/8Mtq/gWxmZHMWt3xzhdtPU2v5b8XpRDQainE6aTSbcBgMZTidPVxzjxfhoqgxG9oRZaDQaUVyRmAyNDHc1sys8nOsaHDjUMD6KNnGVw0GKx8tYZzQfXvhNbhl4A4YP/sWAKQ/jihuiBySDpYrcXbmkhA1j+a4VfHfot/nwyAbi3ZNJTT7K3Evv9C2PrNrvq6xpAU37d+LQdl/u/DU7Wbm1lBnjBnU5XHXnOVJ5O7t0d6laqOWVoWbDAXpHSWO/figmE4akJFzFxb62/oAhOpoBzzxN3Zq12NevJ3rKFH15oal/f1AUwoYPx1lUpDdHSZ4/j4qFj2FKScGUlKSHq+rlK/Rh3YboaEZ8tjWgSqgFn7L5v8RTVYUxMZHB//g7lc8vwb5+PUpEhL5/zn8JpiUzk7ARI/TqlzktDcfmzURPmaI3QAk+h/YeOG02/b0CAmbYJcyaeby5S3Q06ateCZizp/08tCWj2jJM/yWk/kszQ/0MQv08g/etadUurbIXvNyzo6Wgvamr1y+EEEKcCoqibFNVdWyo+6Ty1tvGz6XE00hLzVbwrYwk1uWh2XB82aRqMBDl8XCl08UGi4Uoj4ebazy8GB/DLotZX0Jp9XgIUx1UGxUqPGa+W+fgJ/V1LI2PBUyUmC18ZDWzxmPGe/R9rLV7WVC9BfYkwM3P+QWjKBaMX8DC/IU41DJeL1mJ3WXnYPP/6Gf6xfFZbP5LIqHT5ZHQtqrWFd15juyHO7t0txNgqOqQ/xI5/9sGtTYE0bsYVlQA6JUvr92uV6z8l9b5z5hzl5djnTABxWIhef486tas1TsuaoO8tVAUPnYsLXv2YBkxgpbi4uNVscWLiZowAUtaGt6mJt+5W7+Cr3rnstkwp6XhrqrSK3BKRISvGpSQoHet9Db6yveNn33Gvmu+iTltIGEjRtCYn6/v+wOoaD231k1Te73m1iHcgN7cJXn+vIBZbv6V0OAKlX+TGX3OnF/A0u4LHvKtCT5+2gtL24Qm/4AXqpNlR2G/p/vWOur4GYrsjxNCCHG6kPDWi0rqSlj65V/Z5bVRZ/T9Nd3k8VJnNIBfeAv3eBjV3Mx37A04FIV9ZhMrE8wcbe04afR68RgMXNzkxKiofGSJ5HxXC7+rPoZdDWdOTR3RUalMbKnnSZwUWyDZaSLHEAYX3nK8chYkZ2QO9c0ujh1UKPW+xQjLDVgS8li97zUAFgQtiaS5DlrqfBW5dqpvPQlXEshETwQPeg4OBv5Doq0TJuBtdOCyHSZs+HCiJk3Su1UGB0itumZMScEYGQmohI0YEbDc0JiYSMOHH1L3xhu+ipnZTOLdd+lVvNLZdxHzve9x7M9/RnU49GWbyfPmcXTxYsIvOF+vuplSUjCnpWFKScFls6GEhxM5bhzexkaaCgpwl5bq1cKw4cMxREYGhEuAiLFjsWRm4nU4cGzerF+nEh6O/b33sKQP1oeFa9eS+tjCdpdJanvjggdtx069Re/AGSpgaceHwLlumo6WUvrvs/NvbKJ99a8Eaj/j4BB1srpMnsjgbyGEEKI3SXjrRUs/X8qGgxsAUDCg4sVtDJwXYPJ6GeJ08ZHVSrhXochsocLi+zH0d7mwGww0ts5/u6PaS4pSR6zbxLcb7TySkEqqczw/aXib2xJ/wPkRn/H812/w59j+3Ft3BDjEfclZfN/uJDux9YStSyFLL7iDFwsVmptuYdKBx/mhaT+fJnxB/0t/R0y42be/LTY9sMoWHkvJzn+S6y4j54YX8ToTZQljEFnWefL4zzNr2btXXx4Y3KHQvn59m2V/5pSUNg1SNNpoAcViwZyUFBBGtGYk2pBrAEwmcLk49uJLNO/ZA/galNjfeANcLsxpaYRnZenXZe7fn6aCbURPmaIvF/RnSkyk//x5emAKGzFcb+oRf/uPsKSlUbFosW9UgduNp6oKl82Gu7wcY0JCaxDsj7u8ApfNhqe5maaqKn1WXKgGHv6vPbh5iBYyAeybfHPntNfvKi/HYLXqown8h4L7n8M/ZPl3mgwVrIIDm384CrVXLVSl0FVe3mZOXG9Wy05k8HdXSXVPCCFEV0h46yOqVwEDAY1KtPEAu8N8yyLzrOEMbXFyBBP9nF5GtMDmaCNpLV5mlCXS6DZRRBoPVNv4a79I1seYubluMzE4qPvsT2wyDCMq7EoeGH8XqQdWc3/NPjaHHaPkk78yKOFB33LIChvpX6zCsut9ttTfT8bIAbwyNI7w2kmMm/IwLmcizeW34B2WqAeRe7NUBu35G1xwM8vqd/O68wj1217GUjv9jGgecjIDlTRUOXmiJk3EkZ+P6nbrgUOrxmhdJ9tb9qc1xwDaLNvzb6YRNmKEXrHyDxP2vDy9OucuLcVdUUHT55+Dx4MSEUH4yJG+2XITJhB/+4+o+cc/KZ19lx7WtIHhTpvNN/MtM5P423+kL7WsXr5CD4pRkyZytHWwuHaN5pQU+s+fp+9rMyYk+AaDl5fjstkIz8rClJSsX5+nrg6v3R7Q6CWUDkOJougDwy2Zmb7h6tu2oTocAd0otSqoJribJ6B3mvTv6theYxJ/nYU5rVJYOvuuNs9tb1RBT4LRyRj8LdU9IYQQXSHhrRfNGT2HXVW7sDlsYPAEBjc/WrfJFoOBIyYzKU4PQ10tDKlP46jaQrEhkf8kHmUYZUyrhv6eY9xsT2KfIYIWo4NCUxSVLalM5GNet1+O+8OVpF59PXM+3km5+UKMhjHMXn8H5S2VlHvDSElKJqfmMItTP2TVgMF8VlpA7uAMLk2OZdm7vvCR2HKIqypfYUvZ1XzX9iGDql8HoD78YZy2VTit2dx3ddf3qpXUlRzfS+c3K+5kOJmBqid7/kTnQlUhtAqZ1mikvWqM/wffsIyMgJlncDxI+LeJ1/ZiBc8B00JhdHY20Zve8VXyCgpQrFZ9aWPkN76Bs6gId3k5islE3Zq1+lJGb3Mz5rQ0jAkJemVNGyAenZ2NJS0t4LxatdB/35f/0kBtrIHZ6dS7UfrPvoubPp3Uf/4joNFL8PupvWehKl0txcW+jpQmE7jdepMXgKbPP8drt+uNXLRgGfz++3dyjJo0EUtamv5+a10d7Xl5+t7Brix5DF4y6/+46uUr9AYz/t06/YNexaLFODZvxlVeznkvvdi1X8Je0J1q2smo7nX1uqQKKIQQpy8Jb70oPTadX184i4X5v2OIsxG3qrLFGnk8wPlV4LR/Nxqgxmik3BLJZyY3FzqdtEQdpAgowkqz6uTyKkhu9hAWpbAh1sqGiHhiDl9NWUs/YpRGrqpfBxs3M7ylnufsLq6JNmKJ9bUE36Y20hQVDpY0FtyyAHdLBZ+UbaK4vpjcbUuYne374Han+6/EVr/O4lQYMOVhX9OT8XN5QE0hKi9Vr2KFDEP+XSpb98bl7sptf1ZcHzuZgUr27/WNUFWI4OV1mlAfeoOX7QXfr1W3/ANFcLWjo2twlZfj2LwZc1oa/efPo6K1Uubbh9YQcByt4Qn42v9r3R/9A4nTZguY0ebIz8fQGvi0pYkeu10PRVq1zb/TZMvevfreNf9GLy3FxXonTi0ohRpCrjV70ccvtAY3LRxqwU2rnmnH194T/7ECWlOWujVrfXv//MIe+EZAaJ0voeMB3NrPQlvKCQSEvVC/F8HhQ6sCBi9ZDaW94NKTQNPVatrJDkudXZdUAYUQ4vQl4a03Ve3n1Y8epywMMp0KhRFhbStvQdW4FqMRg1fF67bSElaP1d2I1ROGw+hrVWmKiKEEE0VqP35Tv4vvRQ3EaXRS0/9Dnq34Nbcl7Gds7REah08j9qv/UGHoTxTNOFuP32Q0MtBtJGfSc5A4lE35/8LudpDhdJJTV4/WpLx+9N3E0shlChBv1fe+ZdKF6lWIwd0dzorrYxKoznyhAld7S9dC3d5RNU77tyUtjcrnl7S7pDIsKwuLXzMP//byNf/4h2+A9t13U718BfG3/whzSkpAsw8AQ3wc3qYmTAMGQFMTsVNvaTMQG44PCtdoga+Z441A7Hl5tOzdS+RVV1G/di3uysqAQBO8d83/vdD367Xz3mrvl3XCBEwpKaCqpDz+O33OnXVCtm+p56xZbUKGf8VQux5zWpq+/DTUkkn/AeFaYxhXebk+6y44wPh3vdSOqf0su/Lz77/g0TaVyI4qeaGCS0+ao3S1mnayw1Jn13Uyq4BCCCG6p8/Dm6Io1wPP4Wuc/7Kqqov7+pynTP4SDC47hEXxcWR420pbO8sovQYFRTVxRZ0Fo8GJw2gkzelkpNPJnMSRlOY8ym/X/55bK6NJRsVGKXHEc8ThItGygafjGigtf5ur3JnMVT/i0ejJrE/5Fp6K7cTabeT0mwDxg1mYv5CJgydCcz05dfWkZ/+C+Zu1JYaDWBQe6wthYbEdjgdoo7U7ZcnFU8nNX6gvlTzZFTdx9jjRPUZd+fDZ2ZJKS+vXujVrfUs2W6tt2nw3gGNLl+KuqAholBE79RYaNm9GdThwHylDdTjwlJfjtdv1Y0VNmojHbidi7FjfbLqEeKJHHB+C7bHb8ToaMFij9CpdxcLHfKGurAy1qUkfXxA1aSJ1a9bqQ7SDqzj+wUcLKsF7wLTHNBcW4i4vJ276dH1Zp3Y9zqIiql98sbVxSRkGa1SbY7rKy2nctk2vDIIvyAUPwg5eMmqMju6wa2VYRkZAIxKvo4GGDz/k0F13099v9EF7P//gkRP+lbzg8wU/1z+0a+9FV4NWV3+PT3ZY6uy6TsYePyGEED3Tp+FNURQj8AIwCbABnymK8rqqqrv78rwnld+SwZKLp1JXux28tYFhrTWwWd1u4lWVWI+XcpMRLwqqqmJRFCZVRzGv+Us+Dg/jgCWJX1fXMM5tYs+Ft/Lzt5/Daf2Cpc5xPFJRhS3KzqUuJ3HJ/2bVeXG874gCGihThjC6+SbGXPcIlyTHkrttCTl1I0jP/gUPt3bCtDvt/PHap/XLn53d0Po1ExRfCCu94A7+vGZn1xt+JA6F8XPJfec+VjuPACd/qaQQmp7uM3LabDQWFLQZgN1SdMDXbj/R18JVdTiO73kL8ceYhnc26cO74++4A/sbb+jH1D746wExM1NvPGLq31+fl6YFFU3Zowv06pkhMhJPUxNhw4cHVLzipk8P2V4/LCOjTVMRrdLjXxEzti7H1PbaQeBMPWN0NK7WBikte7/G3TpHTws/YRkZmFNSUB0OzGlpNBcW6nsUrePHt1kq6nU04Nich8duJ+n+uW3mybUnLCMDl+0w7ooK3BUVVKekhAx7HYWP4Pl1wb8zoSpu2vupvRfdCVqhfic7OqcQQgjRnr6uvI0D9quqWgSgKMp/gO8AZ09405YMFn3AkwlRbFHrQfs85//BTlUJU1VUYLq9gRVxMRRbzPR3ujCpKjuI55GEFErNBmxhBlbExHHl0QoqNr/MTAzkxl6Eu/5qct0VDIk+xnkNTm7yriPNOB1nyrc4XHGUmc5mflN5PZcXKoSn5LK6dCP1g76DZXMjlWH1ADjcjoDL15YYFlU2MH9zI7Ozn+hZw4/8JeQcKIAhY9tdKilt9cXJ0J0laP4fmrVGG435+Rijo7GkpZH62EL2T5wEgGI06ksBtUYh/l0rNVGTJlK/fj1eux37G28cXy546zT9g7//0syjixf7qnQVFfpMtuDKmLYPTutS6X/O4I6a/rf772XTKk1ai30tQNrum0PaC0vb3VPo/z61FBdTnZKCq7wcd0WFb+moX4XKY7cHzJfTmohox7M98KB+Hab+/QOOHxxYOxI/axZHFy/GNGAAjvx87Hl5IYdttxfkg88XHHj9hepw2d2gFep3sr3fU2kWIoQQoiN9Hd4GAqV+39uAy/r4nCfX+LlwMB+OfU2JNRUsZt/taut/+TUrqbb4RgQ8ltgPj0Eh0uOlovXx5f2/ZJfBgtXjAcBmVtjnHcBhs5PquAPkVI9kmdqfioQPqYmuw2ixciBsFHeO/hF/dblh+0xoqSctNYKk7O9jsPgClKfkPEbsXsCXwyMAsHpUeP1nAc1F4HiHxgZvGZaEPL49Nrt7DT/GzyWd1kHf7XSXlLb64mTozhK0UI1NgpfF+e+X8g8I2r+DQ0PViy/htdshLKzNckGNpTX02O6bg+pw6EEmbMRwfd+cff167O++iyEiQm/Vr1gseqjU+C//bC4sJDwri9iptwSENuuECb4mIuVltBQX+8LLC0spnX0XzqIiKp9fQtqzz3Q5RERNmoTLZtOHnkPgjD1tKWRHASRsxAiirr6620sFW4qLqVm+PGA5asXCx2hore51Zf9asOAAHBz0ghu7dNYZM/gxoX4n2/s91X72/ktxu3oeIYQQZ7++Dm9t1xS1xhr9AYpyF3AXwHnnndfHl9MHEodS8u0/kvvRb5ntMbBAPep71aoXDIEDurVllB6DgtXjwayqNOJ7jMegEO3xMKO2kX9FpXBDdQKHTFZejlWoi4nGoobhPOqlf/X5jDN+CoajvBZmpanoHX5Q8BrroozkGOO47AcLIDEK9n3Kgm3raIgcSJTpfS623MKa86aRU2GDL1b4rsdvX5sW1Jxxq9hY+hrThpnJTLqu05cfMBKgk31y0lZfnAzdqYyEamwSvCwuuHNj8Jy44A/OLa2DuzEY9CATfD7/tv+WzEzCRozAvn49UVdfDUBzYSEAnmPH8LQ+V5ut5t8QRWuN77Hb9Q6PrtbOk86iIr27pcdu1++rtEaR9uwzhGVkEJ6Vhctmo2n79jbNQjqqFhlaK4v+DVKiJk2ksbXBi3+lruzRBfr7pXXO9O3ns+rvX6j3VTtf8Htc+fwSnEVFKFYr8XfcQf2rr6K6XCFDWmdB3j8AaQG4Ze/egOYq/roSrNqbL9fdpZ2dkY6QQghxburr8GYDBvl9nwYc8X+AqqovAS8BjB07NiDYnSmWlrzBBk8VWzww1N3C/rAwX3Dz+gU4/2YlXq/eTRKgv8uFWQWbxcw70WE8d6yB9KQM+pe+xf+FnUch0GBqJCnjDWZVHWbWsVI+iziPI1EjGb/zK34dloYtxgYxMSzAtzzRuvoB+rccJMrjhDEzyRo/l6zEob49esZIvcmIRls+WVIXQ0y4uctdItsbCRBqzpt0gRSnQkcViu50tYTAvWJad8jgxybPm0flU0+R9MgjJNw6LeA6XOXlmNPS8DocvnltaWmEjRhB7NRbApZLumw2FKsVS0YG3tpa+i94VO+Oqe0lA/RGK/b164meMkVvEqIt6/QPR/7PA7Dn5dFcWIgpJQV3ebm+nDHt2WcClkC2tyTUf28c+Pb6OYuKOLpoMZa0NMIyMvSw48jP9w35bt1jp72P2n65UO8rdDxCQHU4cH79NeFZWdjXr8eclkbUpIn6bL6uVKOCg1bwPLqe8P+d6moVzZ82rL2zimR3m5x0t1InlT0hhDg99XV4+wwYpihKBnAY+D7wgz4+50mj7eHS9pPZjGDBfPwBBkNg05KgBiaoKmOaWrirro6X4mIo9xoptlh4op+Txyu20ORNpsbgBCxYrGXUcZjPRl5GwZHRRJpv58Z96/ie6T1MiZP5xBtPzuFdFP7fPdSZk3nNfh3TY9/m2fgLuCfrbrK1JZKJQwMqbkWVDTyz8WtUVB6aPILMpO51iQw5EqBqvzQvEaeNjioU3a1+aNUl/6YmWljQzpUwayYJn20NGPIdlpFB9fIV+vDu8KwsvSKm7XPznyWnhQi1sZFBy14K+PDsstkw9e9PxJgxeqfJ8LFjaS4sJOHuu2nMz6duzVq9iqZ9CO+/4NE2s9ZcNhvmtDS9auf/nml71vxn0DW8s6nNvDdNcPhJmDVTryBaMjNRLJaA+7T30zcbzhEwdNx/CWvwUsak++cGBFFva+dPS2amPtpAC4mdhafgfX7+w9pD6UqwCvU71VxYGPAaOgpGXf2d7O7vbncqdS3Fxdjum9PuHyiEEEKcOn0a3lRVdSuKMgfYgG9UwN9UVd3Vl+c8mbQ9XMOzvPptzuCFon6hTVFVVEXB7PXiMhhAUag0mnktMp7tEb79cCavSrHFwtroFtIjTFRaTBhQ8OLFqqTiNUfxcdgx0qxrUYd8l8E1kYy57hHS1y0g3e2mwbWDKMVJv4TL+VNUPIXs4rG8v/BOxhJCWZZXxLrCMgBiIswBlbGuNBgJORLAr3nJxMETWeg3PkCIk60327Br1aWWwkK906P2gRgCK0XBwSFUy/5QnQu1EKF9ePavAmkDwMNGDCfp/rnHP2CbzeByUfnUU779dvhmx2mNPVSHw1f1S0nRzxM/axaVTz1Fwt13Yx17qb4cU2t6ogUxbZi59h76xhg49M6Y/vPetPATNWkitvvm+AKW2UzUpElYx17aprui/2y4uOnT9dEE2vH8Rzlo70FYRoa+DzF26i3UrVkLQOO2bSTPm6ePeLDdNwez3/E0HXV5bC8QdTQXriNJ98/Vl7D6/xxPxZLH9kYghAqQWvOe4OqqEEKIU6/P57ypqvoW8FZfn+dU0PZuXTri+/y1YDt1nmaa/ZZD+lfaTF4vboMBq8fD6OYWPrJGEu3x8KvqKmqxsj1SwaMaGVh7HrVxJVzjtjA/VqHFdyBS3WZm2RzE13zNoeQkSh0HMSsf86OjP2BGocK9Ny2k9D/7GOTx/TX6AlM586ts/CbmfMwJUzp8DfYmNypqm71oPW4w4te8ZOHX/wq5rFKIk6WzCkVPRwuE+hr87+Dr0JYkdtYivr0qkDZbzVlUHLD3SxtfkPTIIzTm59NcWOjbH/fUU/pMupavv8axebPemr9m+XK8djsthYUk3DqtTVBKe2EpZb95lOZdu4gYO5awrCxs983BmJBAU0EBgN4gRQs02ry34FBZ/eKLtBQGNhSx5+XhyM8nfOxYzElJeuBzFhXpg7C12Wr+70FLcbEeKBve2UTS/XNxfPQRXrudmuXLA4Kv6nQSPWWKvjewo4pSZ2Gmozl07Wnv59jeHxT6cqliZyMQ/HXUeTSYLK8UQoiTq8/D29nMYKkiPGUtW4r2U4GL/h4PXkXB6b9cUlVRVJWBLjcHwyykut1sCw8jxu0hwuur2OVbzVSYzVg9Hg7HH6LKbOKDpBRurg3nX5EVDPVYeLZiH+mqG2oguiGNTTEOLvLYKRg3iBtGm/hb2X/ZGXYXOfVvURPuZnfmYH7UOJpR6jSyh/bj0+dvx3vND9nY8CnXpt7KW5+79Yra0h+OaVNlK6pswN7k5saslO43GPFbmpljDrGssg/IGALRUz0dLRDqe/9/x069hZa9e/UmHd09n3Zse14epbPv0scEaEsG3VVVWCdMAMCxeTNREyaQcOs0Em6dhj0vj6OLFhP97W9T/+qrWDJ9/xt2l5fTXFioBz//ykqojoste/eiNjXRsncvNcuX+8Ji63w3xWrVm6AA+rJE7djmtDQS7r6bYy+8gOvYsTaz0g7//AE9WGb88x+UPbpAf572Gv3n0Gm045v699crmQOeeVrvCAq+Tpaq04nLZsM6frz+fD3wRkTQUlQU0KSloyWWYVlZKG++SfgFF7QJkp0Fl1ABPdRtwcEyeFxEb+uoIn0iTX+EEEL0LQlvPaA147A77Ww4uIHrnAYycFLcOgoAOL5cUlVRDQYOmn1v9QGzGdVgoAmox8hDyYnMq2hhoxUcRiMOI6R4wsi5+rf88eNoEr/czp3Jr5I76HxylATqjhn5a+VlzFLzGJcezveyKlm45SlWO4+QmDSB+6vvJyxqDZaarUQOm8aC8Tfx9nM38KlhN6Vb9vFpmIOtJcfYtdPXSVKrqAVX2bTllDPGDdLD0Id7j/LYut08etOFXD0iuUvvVchllX2gvSqhhDrRmd5cVulPW2KpdWT076jY0fmCA4G2N81ls2GwWvXHqc3NtOzdS8rjv8Nls/nCYnExFYsW07htm6+VflkZQze9oy/789/bplWk/PfFAQF78OLvuINjL7yAefBg4m67jZrlywP2+1UsfAyXzYbX4dD392l70EwpKbQUFqKYTNDUhMFveWj18hV6cLNkBrbTd5WX49i8GcVqDZhDFxxgFLM54Fq1rpfaUkz/4eDB1KYmvXoYXElrLizEnpcX0PBFH01QXd2jUQRdEbxUsbNjd6fiFeqxvTUY/FRUEYUQ4lwm4a0HtA6L1w2+jmnDppETNRznB3/gPq+RckuT70EKgc1JWrtO6u00WztROoxG1lvjGFw+nsrkjzEYBtOPWTz2kQNLv/X8fOABCtQ9rCYahk7m2uxbafzoj6TVlBO1/2Oo2UtOXREMGcvoQZNRbSsosIzGazxEzoBrKKkrYWncUQ4SzbcwMW3YNF/lLdwdUFELbuMfqq3/Y+t2c6DSwWPrdvNuUHgL1V3yZGpvDIHMlhOd6emH2M4+nAZ/qO3qB/3gxyXPn0f5bx4FVcVdWekLX63dbN21tXqAanhnE4DeFCU4LGnz3jx1dXpFCnxh5/g8uOzWeXDlvg6Vn38ObjctX35Jy4UXMuStN30X2dpF07LsJX2enHbNWsBs3rOHpoICrBN8oUoLaVonS1/VUMVgjQoIS9resshLL8Vls+njEbS9gVrTkFAD0v3fb+1+f9rySd/eQUUfV6Ddp+1PC97nZ05LQ3U69cqexn88Qme68/uiLT/1vz1Yd4JjX1bH2vvfj1TkhBCib0h46wFtCeDEwRP55+5/Mqv0A9TEKBqaq8EQarRdK1UlWlWxA5e2tBDmUdkfZuW6liO8G7ORyGYrE2vredm5l6qB6zF6KjmQmMRAdxpplnAmxl3A+s338DXV/CzCyciBg5lz2YOkl2xhwfi5fPrvhVzm2cgllbuhupxc4++xp2ZxkBYyDJH8PPsJiB/M0s+XQgIYLHMAXzUquI1/qLb+j950oV55g8Cq1j/2hR4ZcLK0N4ZAZsuJvtLZh9PgD7WdfRjXljrGz5pFnN/jorOzaZgwgdpVq1DMvm62EWPG0Lxnj68RSWvHyIRZM3HabDR8+CEoCim/ewxAn7MGvu6N/m3+tddgTksjeopvb+zxeXBWGrdtA3xLJDtaXqc1XtG6X2oVPt9Sz9s5umgxjs152GyH9Zl2WjMR/7l02jJI/31qlc8voWn7dtwVFfp+vWDBDUWCm8n4NzsJ1XVSC1bJ8+fR8M6mgGCodQmNmz49oIKqdd/0r652pLu/L539UaE7FeO+qi6fbucUQohzgYS3HtCWAi7MX8hHRz7SbzfiBdVwvNoGgfPdFAW70YjZ62V8YxNro6M4alZZFheDzWIBXAxRD5Ch/o+a8HpUr4HylkrKAZrq2LT9L9xZ+iU7+ydSbLFgQ8Vw+BOeuvl5AM678jY+fns7j0edR0pkAgXOI1xHFtOGTWPi4InkHtyEvcS31BMguqyQBZNe8O1R64KrRyQHVNz8q1p3f+vk7G3rLpktJ/qK/4fTnu598qdVe2qWLz9e4Qo6V1hWFjXLlxPzne/gLi/H1br0MDwrSx9H4K6o0Ds3BjcA8Z8nB4HDvUMtMVQdDgzR0Qx45ukOl74Fd4/0X7KoLQfEZNL3tPkv37RkZuqhSasAxk2frp/PGB2Nu3WfndfRoO9N8+/yqT1Pe3zqYwtDhodQPyf/vWb+e+y0MNZRBdW/yhdqZETwKIXg6+lIZ79T3akY99YSye44FecUQohzgYS3E5AzMoc8Wx4VTb4PFh7/TpMaJagSp6q4DAaWJMTr98W5DbiMRhIUIx9GxtPfew1h6lZaDOW+47qi6G++kByTg3S3m4XVBp6PtdLPU8Glx2zMb9rJ7OxMDIde48lkNzaLjRbGMXlQKnNG30l6bDoL8xf6lnqak7ku5Uo4ts/Xzt+6JGDuW3f4V7XSY6Okm6Q4p/h/OA1V5ekurXV//KxZAbf775XzD3gumw0lIgJjTIzeFMU/IGihyb8BiBbi4PhSSv+g5V+ZCh5jYHvgQSB0q3z/a9SqZ1GTJuq3BQ7fVgnPytIrhv5hKdTohIRZM2n48MPWABf0/6cmU8BSS60C2V7wCVX90t4nQ3R0m5b+oY4TvLyxs5ERmp7OZdN+Zj3ZO+b/c/Hfw9cX2nvPZe+bEEL0LglvJyDd5WahIYXHI6CmqRZHa2P/NlSVIU4nLYoBm6V1iHdrcLN6PER6vVQYPRxTFNwGNxWet4kzDMStGvHgAa+ZDOP3SJ84HIzhjFbh0trLGXx4LRHmZrZ8thWA8LgYiustxBPHLSXlVJqv0veh5YzMwV6yGWqKmRORSfrkFyF/CYxvuwSpq6SqJYRPbywRayks1Fv3a3vK4PiHeC0AGaKjif72t6n529/w2u24m5r0ZXv+ASG4AYg5LQ2vo0HvqKgtP2yvWuR/LNsDD3bYKt8/FGkjB7SlkABpLyyldPZdrU1XovROnMnz5wV8oNf2evkvgQT05aIaQ3w83poaws4/n8rnl+CurMSclkb/BY8SlpHRJkwHh0utoyb4qnahBoQDITtQhgphwT9/7RzBjU/a/Mw7GtadlYVh/XrclZX6e9/dPwwE/+705BhduVb/cwWfo73bJdQJIUTPSHg7ASV5T/JkzWfYLBYMXjX0frfWZZMlZjMvVFTyeL94bBYLZg+AB4fRiMfgpJ/TzDGLC1QwGJ3Uq74PFkYMEFZDRPSbzN+cxjw1ktjd/+L2CyMpcURySd17JCWHEZ39fQxlY+HAJqYbo7lA/YhfeZ2s3VfOtiNbeN6YRnT8EFa7jhIdG8MCv3b+QogT0xtLxNoLgP5L87TKm/2NN/C2LnkMz8oKOXjZUbCNujffRGntgmvJzPSrfvWscmhKSQkYJdDRtWv737RKnP9g7VB7xbRrD14C6bHb9SqdwWrVq4XG6Ghc5WX6YwH9eMFjD7QQ5sjPx5KZqTd1MUZH68s0o7Oz29231lxYqIe9UJWssIwMfU5d8vx5IQNsV5p6+P/8tDl87vJy4qZPD/kz7spcQld5GS17v8Y6Ibvbf1zwP1dne/Y6+/0Nvv10aWgiIVIIcaaR8NZDJXUl3O+xUWyx6AO4Q2qtsHkMBhYkJoAKwxssqAYz+yJ9+1WOmCwk2a5DSdtAlcVFmstDk+rhmMXCKKeXjGY7N1Vs5uGa0bx88Q08NMZM7Pi5XPL+41AHl6TFQlIUJav+CN46IlQVxsxk9sVTKfz8aYrri1nasBfiM7hu8HVMHHYLC/MXnrTOkNKuX4jOdTSwW7vdkpbW4TI4LaQ0bN6Mu6oK3G5UhwNLZiaG1rlslszMNk0/OuuaqHV41IJVe4OqHQXbqFm+nOT583yNVt7ZpIeYsBEj9MAW/IHef99Z9JQpevOUsKwsji5eDKAPA/dfVlk6+y4ATP37EzFmzPG9gRkZAUPHNS6bDfexY4Bv/1zs1Fv01x3qQ7x/B0r/YKkt4/SvyGnB+siDDzHgmaf191VrgKLtieso9PoHmuT58/TZdf6hsrPQozW+0Z7nsh3GXVGBwWptN5x0ZZlpZ9Xlrvz++uurhibdDWO9GSIlCAohTgYJbz2UuyuXYoeNfpiowdnxg1WVMK+XitalP5WmFkY31wERmLxeflpbg2Lcxyb1p8QZ/8vsYTdxePvLPGkxM3vYdxm49f/IjbAzx/I273t/+f/bu/c4uco6z+Ofp6q7Okl35X6FhnT3BC+EoJgIEog6GoSgo4MvzOiusx1mVmdcAirOCnFcYlwQcNQZSXBWmRno186syismI47GmDgqQWQwAYcQbgnpjulcOglJOpVOX6qrnv2j+lROnT5VXdeuqu7v+5906nLOU9XnBeeb53l+P/ZffS8tMxvgD79IR00tbVMm0/r8d9lQ08fWCWEiM97C1z7wTZqAB6fNp23XeiJHf8fW6DFuDoXZfmD7qFaGVLl+kcJ5bwydPWJH7l5L3aJFnPz2t4n3JlqVDB5N7JfFGEILF9L4N18FSNk75beXzm8mzH1z7S544g4JTkgLbNlCPBJJNAgfmgFzluzFe3qobWyk4brlw27o3Y29Z91+Gz07d3H8q1/l3G9/m+gHV1vLpKVLk8spIXHT7YTR2WvuonvTZo4/uD65J88vHDh7/wAC9Q0pM4BOMPMukWx8aEPyezqy5gsAxAfOL5F3vidnz2I8EqF702aC4TChxsa0lS+d4zszbu6qoO7fsddIocf5XTq/A3cITHc9pQswfvv70sk1uGQ6XiEhKNcwVswQWSmziSIytim85cmpqvhvr/2IOGlm3YbMHIxxovb8Vx0PGP5zQh1zolG6amv5Zf1UTMPv2d3ZzawFN/JfOr7PG/5gMe3HdvE/D2ziLQ1BnqyvZ5cN8/zuI1gsD/3XxTBzAW1zGhNB7LXtMHgOaICGOclzNU1pYu17vk5Hdwd21z9w4vA7WLn4opTPAOdnx25YOJef7jla1FkylesXKZxfAQvnMSc0QaJoh7NEMtrZCefOAcNvllNm6Y4exdTXp8y8ufd7ObNYcP6m1Jkpc880OdUwnaBQ19zM7DV3cfiOzyUrRvoFxLinambHyj9JfJ7BQUx9Pbanh9cfeigZSp3PH4tEiPecTfa6cz/nvflv/NtvDGsp4Gi4bnmilx4kx+IOEE4Ai504AYAJ1iSXMjq/g3AkQv211yaP6f6+cu3Z5oQ5371wI4Qob1gLL1uWnLENNTYmj5fNrFouy4GL3bA832PlGsaKWRVT7RFEZDQovOWpaUoTrQtb2d6xnb54mkIlANZyoiZ4vmWAtQSsZTAQ4NxggAkxwxkTZdekXt4681/ZbaPEor28fPJl6mvqiQz28HL9VJrr59DQ+24Aem1XctmjE8BaG94AO75GeNqbaT3VDSf2pbQAaJrSROj0Sn6w8yANgUHu+3DqjJszO/ZM+0leO564eSnWLJkKm4gUzl0Mw3vT7cy8JfqqfZyz27Yz7U8/npyFcSoo+s2m2Wg08WdPT0qwckJMvKdnWHETSFTH7Pryl4lHIpzdtv18oZShYivuPWx++/Pg/E16/TuXEWppSVbNnPZnf8br3/oWMz75SQZefTWxr81a4HxFSSBlT5n7+O49bhc9/J2UIizuPm/OPrbuTZuTwdBpMu4u9tH40IbEbOPPf07s9dcJNTUNKwzjbnOQ8pinaIk3QHpnCd1LSCH38OK3d88vDHln1fwCby6KGVwKOVaxZwiLeW4RkWJQeCtA2542TkdPZ36Rp+fbbSdPsXAgyj0zpnE8WEt/ELomTeFDNNAf/m/0mG46A9/nTdPfBMCuY7voYZATPZ2876K9fKz+CgamPsbGvT8E4E8v+Rx9R28ivqyFlls/xtrHPw3/+SgEJw0rSJJpBsx5zD3zJiKVwRu63MsfvaHJvUQvZcnf3WtTioE4gaz3ueeA1FAE50NMuhDRv3s3RKPJht/e8br3sDmBxgkvzqySuyLmwP79dG/aTHjZMmJHjkA0SuzIkeQ+N+8+vyN3r2Vg/35q5szB1NYyZ+3dw0JLtLOTrvvuo3buPN+bdXeT8vp3LktWwnSWMDpLPo8/uJ5gOMyEhQvpeeIJambNGvY7ci8pdc4T2bJlWJsG9+8AEjOC3uIxA/v3E2ppSbtXLld+YcgbNAqdOStmcCllCNLSRhGpdgpvBWhd2Mq/vbKRPu+qSXdjbpeAtWwOh2k43UOAIP1Bw8S44YtX3ck1+37N/3wd9pzcw6SJ89kV28WcSXNoNBPotH00ByZx2+JEz7aO7slMnlBL6wXvpuu7n+LpI+8CEjNlHZd/mLael2m9/MM0ec7fMquBTyxr8S0e4p4de5erEbeIlJ/3hjPTTaffjEq6IBUMhxk8epRQS0uy35oTFJ1y/ul6n3n3Zzncwc0pjuJXVMXdriCbz+CeLelvbycWiVD/zncml4ee3badUGMjxx9cT/T4cZg4EXp7GdjfTs8TO5LHcx/HvSevfunSYfvTnPDrzHY6M4STli5Nhirn80SPHqV27tzkZ4j3nAUgOHcu9UuXJo8RXrEipUm5lxMa3XsJnd97vrIJQ+Nlyd94+ZwiMnYpvBXg0NlD9Dl/cQc2n+AGEA8E6AwFuG/GTAgOEo7FuO94N8Ef/D3EnqV37kJC0yIMxhPv7zrXxfXzruEdr7fTevUXkpUhm6Y0JQqNPP5pmk4+zv3zYNayjwLQdviXbBw4DId/ydqmdw0bg4qHiJRfrku3vKXvM73Hb0YlXZDyhiTvkj0nvPgtdXTe537eCTpO8ZG6N75x2Pic5ZiDx48ne7/Vv3MZ4RUriPf00PnZO5h1+20pe7/cs1Xzvrwu2WA81NKSLFrilOp3t0Nwipk4M3beEOwuSOJuyeCuvOnsq3PGN7B/Pye//e1h4av/1VfpeeKJZCjtf+VVAIKTJiU/izMzN9DZybH77k8Jgc7vxSmi0r1pMwDhFStKFjS812G2AbHUVRW1tFFEJD2Ftyx1dHckG15z6gBtv/kKOwL9JGuVpAlsjjnRKKcCQQaCAUy8hgYGiASDfH16A3cePcSPat/HLbUxno2dJhIMEjIT+cOL38nqK1anL+c/1GD7qqW3wczELFpyD5yrGIlbrsVD3J97NNoKiIwHuSzdcm5kgbRl+jPd7LpnctIVv3CHJPeSPb9jugOfu4iKM0vnlPrv273bd7zOcsy+l15KVJIEwCR7rkGiKuSERYuSn9k9Y+geg3sppRNSa+bMAWOobWykZtasZMVH93fhLszivZl3V6B0zg8wdeXK5GtsNJoSqhI9544m982dfOTRZIEW5/O6v2tnL+KpRx4ZtizVbw+de0bUb6azFJUZMx231EsPtbRRRCQ9hbcste1pS5bX58BTbBw4TJ33RX7LJa1lQjzO/zjVzYlggIemTWeWjdEVDFATj9MeCvGZOZP5X5d+nO7fPsgb6uDF2on8ee9c/uLGvwQnMO39Ofz0LrjhfrjkvYnH3I22T+yj+9+/wQ9iN/Knyz9H0xT/SpG5Fg9xf+7RaCsgMh7ksnTLvS8r3SxMpptddxhxqg562wU4QcE5x6zbb0t7zJR9dq6g4Z7dc5fx947X3TvNTJyI7e0FbHJ20dkj5jTmdsbjfE73LJFTcMQ7lsiWLQQmTaJ3586U8OhtDu4uHuI0D3cvBwXoffFF+l96id4XX2TWp2+n99lnGezqovfZZ1O+j/72dk7OnZsyDufzurnbIrhnBb2c4i3eRuPuVgbpfvd+wcvvsXTX4UhFU0q99FBLG0VE0lN4y1LKjNYF72bXrz5HO54qk0PVJL0Bri8Y5KszpmGBeABOBxPV3QYDAepihr66Mzx38FtQ8xq76sNcG4lw1ByjY8cDNN30ML8+9Gse2PEZ7uzp4pqf3gWX/Hb4AH9xD1Ne3MwbYq/xcN3FRVsSOdJMnojkLpelW959WX6zMJludt1LLp0AAOeXH3r3YTnl+rO5gXYHF3exDuec6d7jLFWMHj1KzxNPEKhvSFaCjOzYQde6L2MHBxNLKoc+c+dn7xgWXvz2Ajpjcc/Kub8L95/O8kuAniefTLZbcP9uogcOQCxG/wsvcHbbdsxQv87Bri46b11N40Mbksfytibw4ywbDbW0jFgZ0t2ioW/37uRrvKETSClu4hfo/B5Ldx26l9r6BbtiLGnMdJxCljaqUbaIjHUKb1lK7jMDmNLEnTvfxD2nfssECx01QQYDgURo81SXdAJdTzAIQDgWY9Xpbr43fTZ9cUtdoJElcxew/KKl/POv/zeNAwOcDTWwsa4GpkxmLXDPb+6hMwj3zJ7DlmX3+w8wUUWbi6dPYmERK0WmfG4RGXV++7L89m6lu9l1CpM4Ic1v+aFTeMPZx1Vow2X3Of2WeToB7/iD6wmvWJFS4fHYffcT7ewkvGJFMhB6OeEl3tMzbDbSPRZvMPKOs+G65fQ89RQ2GmWwq8t/pvDzn+fY/fcz4c1vZvotq2i4bnkyXDrFXICsl/k5yyidP738QnMsEknOZDozm07odL5fd6EVv2PkMpvl3QvpNtKSRr9WCH5yXRqZ6Zr0azju7oeoECciY4nCW5421xk6QyEC8cRsmi/PDFwwHmf9oXNsmDaT42YQghDhAIvr3sT20y/xZAggxMLpl3LzjAXJ2a6mqU109nTSdPG155dMup3YBwa49Cbe8p4vJve/icjY4A0dfjfimfZExSKR5PJD93Pu4zol+dPN3ORqpLDgBJCpK1cmqyo6M4ze4irO8kj3kk73XrR8bs6dvWfRzk5q5s5NLmP0Hqt+yWKmvP/9ye+1rrmZ8PZtw9o3eAvKpPt9uJdRuovPeJuCZ2oP4YTOUMvwGdJ0xUdymc3y7oXMZqmlwy9Yuj+v85lyXRqZ6Zr0azju7oeofXMiMpYovOWoo+NXtP3mK5yIdgOu4JamPQAkZtvixvD510+xOHaO2kAIqCMEDAy9pnVhK5GOJ+DUfm6qt2x3vf/Ot9/J3ElzWT5/ebI5t7t4SMeOB2g7+ktaG6+jaagxtwqNiIxdfjfi6W5u3SEpmyqV7rBXyJ6jkcKC++Z9oLOTczt3Mu2WW+jfvXtY4PF+BveyzGzG6G5v4C1wEhhqlwCkNCl3n9v9vXpbJTif1TvTmKkqp7Pnzx1uvOfJ1B7i7LbtySWu+VSLzJbfPrtsfq/pfjfuxuez19yV01gy/YOFt22FdynvSLTUUkSqicJbjtp+8xU2DhymkRAAM6KD9AQMfUPLIoHUIGctkZoamgcGmBOLsXbGdAYCcQCm0sQV899Iz2APG57bwE1v/Qu2/+c/snlyA1v3bmRX1y4efM+DyaWL655a51s8pG3KZDaeCSeXWUL+hUZyCX37j5/17RlXTpU4JpHRkG4mY6QZDu8Mj1OoIlPYK8bNrjsEOEGqf/du32Dg/QzO/jhnLO7ZIb+xOSHk7BNPMHj0aEqPuYbrlifL8k+/ZZXvjFp4xYphjc6dWUJIhCv3GL0B2B3EgJSKnp2fvQM4v3fNbyYt3fdRibNLzu/G+z06Icv53pyKm5Dd2HP5B4t0r09H1S1FpJoovOWo9eovwG++wvK3/Dnbf/lFjhLjyfpJqS9yz8AZg7GW9lCIe2ZMozMUYsZAomDJxZwmHLdsPfQkAK+eepX2gcNM6wpTY2poP9NO2562ZPhKVzykdfFtMGFyyuOZCo1kCmi5hL5K7BlXiWMSKaeRbmL9gkVgqOBHNu9Jt+8p1z52zp9+7830GUaasUphbfLHdHvjnMIo0aNHiHYeSgZZZ2mn06jbPUvoPd6Ru9cOmyl0f07nZ2/BlAu+8fWsPrPzXGTHDvpfeYW6RYuSAdb5Trw/5xOy3Utpc+W9rryNz92zoPn+Y0ChVSn9Zu5ERCqdwluups2HOZfBi49zqn4WL0dPpj7vmXXDGKwx1MTjnA0k1lg6xUtmnO3k6qMX8mMzizfPWMAn39rKV371fdrP/o5g3SDh2nBK+EpXPMTv8UyFRjIFtFyqS+baM240VOKYREZDvrMH3htgZ2bEbwkhkNWyylzH4g0+ubw33UyVe2xOCPGrQJnOwP72lAbg3Zs2E16xYui5/Zx76imC4XDy9e4g4P5+/GagvFU5zz7xBPFIhGP33Z/ynY8UapzWB06/OHe/PUc232WmMRZzz6P3mM5nzfV37ih0qahm3ESkGim85ahtTxsbD/6MpwcG6AyFoNbnK3T+ddcYJsRiGKA3GOR0IEBtzNIXDDAnGsUGQqwfDNBrj3Oi+zKuufAa1l/3Fu79xb9yOPA9vnj1GpqmNBV9/1qmgJZLdclce8aNhkock8hoyHcWwnsD7J4Z8RargOz20BUyI+K8J9353SI7dnDsvvtTCo24P4+3CAgMr0Dp5Q56znLK7k2bk595+i2rCIbDw5YsuvdzuZeduoMJpIYpd3sE53O4jRQukrN+ixZx6pFHiPf0+Jb4H+n3kG6WrJBA472ust3/mK1iLN0tdOZORKQcjHUtIym3JUuW2J07d5Z7GBl1dPyKb//4UxwJDrBr4kQC8ThBa4m697x5BOJxLGADAQLxAPFAnAsH4VANLJtxFebwPj7+jru4+vIbfN/v7HW7+ZKbVbZfREaFEzqcwFKspXj5nD/djf9rN74/GVb+4Cc/zuoY2Zacdwcvd9sCb3VIJ+TFe84SqG9INvv224MH/t9dpkqh3iWGft+5M87axkYmLFqUsUS/n2zGmM17sz1nMYJXNteHiEi1MsbsstYu8XtOM2856OjuoO3JL/EXp7r4+rTZAMQDAWw8PvzFrtm3eCCQ/Puivj5e7n8bk6a/neZJ21h+cBdPxU/zgxf+hnnz3+Q7s6ZG2SIy2tyzEpkqH47G+dOZveYu3xkrh1Mgo+G65cnA4BQcgcwl571FSNI1knb2t0Gi8XaosXFYz7uRZqDSzbA573U3KHdaJXjL93sbuefCb4x+bQL85LP0cDRaUYiIjFWaecuBMwPWPDDAql5YF65JBDOAeBwC6Rq+JcweGOAfu07AwHS+NCfMroYojc7yS+Caue/lrnd8hrZd62ntPkPTsjthqPR/vlR9UWT8KlYJ9Gotpe6enQFSZtGc2SzvPjjv5xxp9stpSt23ezfRzs68ZoJG+n6d8OaM3W/GKdPsXTZNszN9d5nOU66ZNxGRsUwzb0XSurCVp/c+TnsI7quxxAOuqpLe4OYULrGWmnicwWAQg2FaNMSUwDFmEgPqaYxaIBHg9p84m9xTx5kIa5+aBB/8ZkFjVvVFkfGrWAUZStFDLJ1i3thnKprhhJMzW7YQH+pL5jer6N3L5rzOUdfczKzbb+P4g+uZsGhRxpmgdEFqpO/Xr+qj9zxOARTvd5epaXYm6Wa2vNdUurFn+j2O5vUkIjLWKLzloGlKE00zL6PzxLP0uYObn6GKk9ee66U3YNg1cSLzYnH+rnY5kck7eUP/GZ6cOJFra+fx9gmzuSceZJU5y88iR7h+3jW01p2G/m44sW/Y7FsuBUxUfVFk/KrGpWXpmlvnE+QyLVl0LzX0FvhwcxdQSVep0glIoZbM/53NN0il+xzpevS5X+NUtHR/lnzO6cj2mlIlRxGR0lB4y1Wdp6ebuzWA5/GJsRh7Q7XMHIwB0BCPEZ/2NFsbJvCr2Az6gpZvc5wFwem8seYMPzu5l63Rem6+6H00TaqDZx+FuinDZt9y6cWm6osi41c1znBk2mtXTHXNzTQ+tCE5E+bIVLwjXRl/dxA8+cijaceab5BKJ13zb/ex/ZpmFzKjme01Vap/OCjXksvRPK+WlYpIJgpvOejo7mDvqb2pD/oFt6HHe2tq6AVOBRKVKHsNHCNGo5nATZMu4J/O7SUSDHJP9ACdRLk+1MDNZ87Q2n0Glt2ZOM7S24YdWgVMRKTS5XsD6g4HpZ45rGtuTu4hc2bC0pXNd8KkMwPnLXzitFjINFYnSBWL3/fTcN1y36WZ2QThYu5fS7eMMxd+xy7XjN5onlezliKSicJbDtr2tNF1rguAMIaIjaeGN+8s3FARk1osA8DB2jqO1QbB9nFk9gKu6DzOkzZCb6ie6+ddxeqmP6Lp+U2JwDZzQdr9brn0YhMRKYdi3IDmO3OYSwhJ19zb3ZjbPQvozLCFV6xItlEoZKyF8FtO6a5+2f/KKzQ+tCEZpCBzEE73O8v0fWZ6j98yzlz4HbtcS4GLcd5sr8tqXO4sIqNH4S1LHd0dRAYizJk0h65zXeeDmzuwOX8OVfA0BLBAj0nMvDVGo4RsjPnRGO9p/nN+GDBwYCuvD5wmHArT1PQuaHpXyjmL2ZxbRGS0lPMGNJfg6A1A3hYATrNtv71vo7Gkze+GP1MIcJZm9u3enbKM0y9ceo+TbZES7/nSvWek/YTZfBbvscu1FLgY5832uqzG5c4iMnoU3rLUtqeNrQe2snj24sTsmzewuQ09Zp2nAtA8MEAoPpHOkGXCudn85LlBll62lCcPPcmbwvNp7eocVpwkl71tIiKVpJw3oMUIjk4IikUi9Le3p3we9963UvO74fc+5q1i6d3jlu2x8ylSks178u0TN9ZCjGbURKQYFN6y5Owv23FoR+KBdIVKvKyl5VwNd7we4PuD17LEPs+MyR/jE8ta+MyTX6RnsIeTp16jqX0vHHgGPv6DZIDLZW+b+rmJiCR4b/rz2cvltx9uJNn2QMvl8XT72pzm4+BfxTKb4JNtmMgnRI3XwiaZjLUwKiLlkbmrtCQ5+8waGxoTD3iWSKZwP2YMJ2otP5rex5/U/IJHTuxm9dT/4P/u/TqrFq6isaGRC02IjpoaON0Bv7gHHv80nNiXPGc2Syadfm4P79hf8GcVERlLnNmdk488mtP7pt+yKmVfWzr97e0cuXstxx9cn3KedOfN5XH3Df+Ru9fS397O2W3bGdi/n7PbtifHGV6xIrk/L1vOsUcr3DjfU397e8nHkO/vfLzz+x2JSGXRzFuOJtZMTH0g3bLJeBxMAAycCcXZGmpg5+ACPnf2Mp6dMpmNezeyq2sXC2csZOvZrcy96M2sPRWB/gjs2Zw4Tg4NutXPTUTkPG8pf8h9difbBtROUPAWMUl33nSPe2fU3OdyV7f0vr/YVSxLZTSrKGqJYn5U6VKk8im85aCju4OXT748/Am/JZSBxKRmIAaWqfSfm82EyDUsnv8yi1o+yNOnXqT9TDsXNlzIzZfcnNjz1v4YXHAFvG2Vb4uATNTPTUTkPO9NaKE3ou7A5j12uv1d6cJfusfdM2rOvjq/YOh+f6HLA7375QqdAXPG41fYJV2gKsUSx0pcoliJSzm9FHpFKp/CWw42PLeBE30nzj9gAUPGvW+WIDZ4mgXTr+D/zd7HqVd/QFvNAZpmttB5tpP62vpEMZIT+yA46XybABERyVuhN6HpZtf8jl2soOA35pEKfzjjikUiBMPhnIOBd79cob3ZvC0V4PwMTrrvya8AS7H6zVWSapjVcn5HzvLJSv4+RcYrhbd8WIsx5nw1SffMm+vnUCzGB05MYWfj2/jiZTfym188yIaLmjkwcJjraxclZtwWtiZaArz6L7S+67NqCSAiUgSFBirvjbZ7SWOpZnX8jjvSuZxwF4tE8goGTlVN5+fjD64nsmULsUgk41LMdGHJr6XCSMHKG1rzCTnu0Oj0tqs01TSrVQ1BU2S8UnjLUkd3BwB1gRD98QFSypQ4/d7cPxvDH/b28b5zNdxRM0jHz/8PP6t5kQOEaZ7czOorVieD2rqn1qklgIhIBfHeaHuXNFbK7JB7psSZOcvlfJn2y+XTnNuvpcKRu9dmDALe2Z66RYsIefb+ZdLf3k4sEqG2sTGlt91I7xntmbpKXMqZTjUFTZHxRuEtS06ft1pT6/8Cz8xbfSzGlef62DLjMBe+upsFMxfTGHsLjRN6ufPKO1Nm2LwtAUZqzq3m3SIipeW90S50dqi/vZ3OW1cPW0pYqvGONMZ04WXW7belLJ/MtTm3H79CLH6c84WGlly69/6N9L7Ili2EV6ygfunSrMakmaXMqiloiow3Cm9ZWj5/OTsO7Ug06PZjLaF4nIFgEICeYJD7Z04jGgjQF5hM2BwhsvB6Og9sZfsTX+aa6x6io7YmGcKcGbeO7g5u//fbaT+TKNPrNxOn5t0iIqPDHXIyhTm/17tD0clHHmVg/35CLS1Zz2YUOjuUKWBlM3M2UnPubPfH+RViyTRe95LLbDjhcMqHb8q6gbpmlnJXDfsKRcYD9XnL0vYD29MHN0jOvM0YGGBGNEpdPE40EGBSLA41k9g4IfGy62tnEzn5Kh07HkiGsLY9bcnDtO1po/1MO82Tm9M2514+fznNk5tZPj+7JSUiIpKfdP3C0vUnS/d6p2dcLvuxCu1V5h6jt39XNj3sRurBlu34vOdK10vMCYTeKpUj8fa9y8Zo97jLpFp6q6l3nkhl0MxblpbPX86PXvsR/fH+4U8OVZ0cCAZ5fWjmbUJsItDL2/p6WT3vvYTnNNK6sJW2+Ho2Ro8RnjJ52HJJ98/uJZH7j5/l4R37+cSyFlpmNbD9wHbaz7Sz/cB2rrnwmlJ+bBGRcS3XGZp0r89nGVq2585mRsRblTLb/WTFGN9AZ2dKsZdMxUXyWc5Y7bNo1bKEs9q/Z5GxwlhrR37VKFmyZInduXNnuYfhy11UBPDv7eYy51wt5yZMYP4g3H7V3Vx9+Q0A/PrQr3ngmQe488o7sw5eazY9z3efOcjHrryI+z58ufa8iYhIklMQZOrKlWlv/r3NvkMtLQzs35/xPbnIFCBfu/H9ySWjf/CTH6fs//Oev5KW5o3WWCrpM4tIZTDG7LLWLvF7Tssms9S6sJWA83VlCm5OGK49RyQQ4YVQhHte+An7j59lzabn2fTKluSsWbY+sayFj115ETcsnMuaTc8TH5jJ2qVrFdxERCrQaC+Dy2UJ5Kzbb2PqypXMXnPXsPcUMu5MS+pmr7mLUEsLs9fclRxL40MbfMdcScsZR2uZYLk+c7Us1xSRVJp5y8GqLavYdWxX2ueNtVwUjfL7UIjF53qZGY9xODCPP756Pc/tr+W7zxzkj5bUMPOCp/OaNfPOwImISOXxmwkr1uxKKWdpspnBK8e4RoPf+Kv9M42kkN+3iJRWppk37XnLwZeWfokNz21g64Gtw5+0FmsMB2sTrQQ6J9bz0e5GHjh6E8/NqeUTy1oAhvatXZ/X+d3HEBGRyuS3N6hY+5pKuT/KXe3xyN1r04YWv1Djt6evFOGnVIHK73stZ7n80QiO2sMmUp0KWjZpjPmIMWaPMSZujFnieW6NMWafMeYVY0x+aaWCOPvMVl+xmsm1kwEIEGCCJWUZZWDozy4TZ9uiK3jH268sOGx1dHew7ql1BEInuO/Dl9Myq6Gg44mISOn4LYPLZmljNop1HD/OuM9u287pxx6j89bVvkvqsl1OWIplh6U4ptPkO7xiRVmCjN/yxdFYsllJS1RFJHuF7nl7Afgw8IT7QWPMpcBHgYXADcC3jDHBAs9VNh3dHXxq26fYuHcjH/nRR+gd7AUgTpw+A7XxOAC1sRhhzu+Fu6bxbcmw9fCO/Xz3mYM8vGN/zuf3aykgIiLVI58bZb+b+tG44Z5+y6pkQRO/8JBtgMwlaPa3t9P52Tvo/OwdGfdgTb9lFeEVK4hFIr6vy2cfl9PkOxgOF+V7zXUMfkEt03eXz2fU/jaRsaOg8Gatfcla+4rPUx8Cvmet7bfWtgP7gCsLOVe5OE2zO3s6AeiL9RG10fMvsJZLooMs7h9geixO3BXeHn7+4eTPTtGRfGbhWhe2cvMlN6ft+yYiImNPobMv2dywpwuI6QqKOM9nEyBzCZpOgIps2ZLx89Y1NxMMh9O+Lp/vrNizmbmOwe/8mb67fD7jSO9RuBOpHqXa83Yh8LTr751Djw1jjPkk8EmAiy++uETDyZ/TNLuxvpE59XN44cQL9Mf7CQGBWIy+YJAXJ9QRjkMkABBPvrdpalPy55ZZDXkXGWma0sTapWsL+hwiIlJdCt2TlM3+uHSvyWW/VzH2Z02/ZRWxSCT5c6ZzZVriONJ3lu1+vULk+nvL9fz5XBcjvadaes2JSBbhzRizHZjr89RfW2t/mO5tPo/5lrW01n4H+A4kqk2ONJ7R5m2a/Ve//Cu2HtjKADAhWMPFsRn02y5mRgfYO3ESA1im1k7nqgvezuorVpd38CIiUrUKDRXZ3OQXo2hFMW7865qbafzbb2R1rsiWLUxduRJgWGGVkb6z0QgppS50ks/xR3pPuYqXjPWKniKlMGJ4s9Yuz+O4ncBFrr83AofzOE7ZeWe9Vl+xmt8d/x1d57row3KE14nWBOmqmUg9kxigh9p4I19799fKOGoRERnvsrnJL0bQGM0bf/e58gliY7HCYjECULkqa2rGTyR3pWrS/TjwUWNMnTGmGbgEeKZE5xoVTsXHQ2cP8dZZb+XaC65lRt1sosHEZOGEWIzaWA8AF02rL+dQRURkjKnkPUn5FlHxfqZsPqP7XPnsVStHhcVS/+5Gq5l4KZSyeqrIWFVoq4CbjDGdwNXAj40xWwGstXuAx4AXgZ8Ct1prY4UOtpycio8PPPMAWw9sZW79XJbMvQKA+qG9b6eH6mnOqp9cxpGKiMhYk8sNeiUHPTfvZ8o1hFRLqftSh6tqDkDV8jsUqSQFFSyx1m4GNqd57l7g3kKOX0mcvW/L5y9n+4HtvGfeR3hs10FWzB7gQ6cOsjl6lB4ToH7uIu11ExGRospluV+1LEXzfqZiLWmstH1UpV6qWc5m4iIy+oy1lVMjZMmSJXbnzp3lHkZW1mx6nqd/+wz3z/sVV007B69th0tvouP6L9G2py1Z4ERERGQ0VVp4yUUxxn7k7rWcfuwxpq5cmVeoqebvT0TGBmPMLmvtEr/nSrXnbew4sQ8e/3TizyEd3R0MTH2Mz1y4latOPg4Hh7oimBwbavscW0REpBDplqIVezllpuPle65iLDEcqZH3aIxBRKRUFN5G8tR6ePbRxJ+cb9r9s4M/5HeXXgwz3kBHvI91FzbR8fZbkg21l89fzrqn1tHR3ZH1sUVEREql2KEk0/HyPVcx9m+N1Mh7NMYgIlIqpWrSPXYsvS3lT6dpd/PkZloX3waXf4q2bbeyceAwkY4fET4cpnVha3IGDkjfYNtzbBERkWJzlgE2XJfo/FOsUJJpL1e2+7y8SxSz2b+VzbLGQvaZlXMPWS5LNrW8U2R8UngbycwF8MFvJv/qbdoN0Hrjt2FPG5GBSDKwuV+X7bFFRESKrVQFTDKFnGwDUD5jy+Y91VrEI5fvI9fvTmFPZGxQeMuRt2m3202X3EQ4FE4Gu7QzbiIiIqOkkhtT5zO2Sv48hcrls+X6PYwU9hTuRKqDqk0Wwbqn1rFx70ZuvuRmBTYREREpSCmC1EjHLLRKp4gUT6Zqk5p5K4KslkiKiIiIZKEUS11HWkqaaSZPs3IilUPhrQi0RFJERESKpRxLQzOFO3eYnH7LKgU5kTJSqwAREREZ84rd566YvGNL16uvXNztE9QHT6S8FN4qSEd3x8i94URERCRnlRw6KnlskBomq6EPXiUHdZFCadlkBcmqN5yIiIjkrJKrVFby2LyqoQ1DqdpjiFQCVZusIB3dHbTtaUvpISciIiLjkwqFZM/9XQH63qSqZao2qWWTFcQpfKLgJiIiIpW+nLKSuL8r9zJPLaGUsUbLJkfTiX3w1HpYehvMXFDu0YiIiEgFq6TllJU+C5juu9ISShlrFN5G01Pr4dlHEz9/8JtlHYqIiIhUttHYX5ZtKKv0EJTuu6qkACxSDApvo2npbal/ioiIyDCVPsszlmQbyqo1BFVDgRWRXCi8jaaZCzTjJiIiMoJKn+UZS7INZbmEIIVvkdJReBMREZGKUq2zPNWoFDNTCt8ipaPwJiIiIhVFS92qm8K3SOkovImIiIhI0Sh8i5SO+ryJiIiIjEPqgSZSfRTeREREpOIoWJReNTYB13Uh453Cm4iIiFScagwW1Wb6LauYunJlRe1NGymc6bqQ8U573kRERKTiqOhF6VXi3rSRKlXqupDxzlhryz2GpCVLltidO3eWexgiIiIikkGpermpR5wIGGN2WWuX+D2nmTcRERERyUmperlV4mygSCVReBMRERGRnGj5okh5KLyJiIiISE40QyZSHqo2KSIiIiIiUgUU3kRERERERKqAwpuIiIiIiEgVUHgTERERERGpAgpvIiIiIiIiVUDhTUREREREpAoovImIiIiIiFQBhTcREREREZEqoPAmIiIiIiJSBRTeREREREREqoDCm4iIiIiISBVQeBMREREREakCCm8iIiIiIiJVQOFNRERERESkCii8iYiIiIiIVAGFNxERERERkSqg8CYiIiIiIlIFFN5ERERERESqgMKbiIiIiIhIFVB4ExERERERqQIFhTdjzN8YY142xjxvjNlsjJnqem6NMWafMeYVY8z1BY9URERERERkHCt05m0bcJm19nLgVWANgDHmUuCjwELgBuBbxphggecSEREREREZtwoKb9ban1lrB4f++jTQOPTzh4DvWWv7rbXtwD7gykLOJSIiIiIiMp4Vc8/bnwFbhn6+EDjoeq5z6DERERERERHJQ81ILzDGbAfm+jz119baHw695q+BQeBfnLf5vN6mOf4ngU8CXHzxxVkMWUREREREZPwZMbxZa5dnet4Y0wp8AHivtdYJaJ3ARa6XNQKH0xz/O8B3AJYsWeIb8ERERERERMa7QqtN3gDcCXzQWnvO9dTjwEeNMXXGmGbgEuCZQs4lIiIiIiIyno048zaCDUAdsM0YA/C0tfYvrbV7jDGPAS+SWE55q7U2VuC5RERERERExq2Cwpu1dkGG5+4F7i3k+CIiIiIiIpJQzGqTIiIiIiLio7+9nSN3r6W/vb3cQ5EqpvAmIiIiIlJiJx95lNOPPcbJRx4t91CkihW6501EREREREYw/ZZVKX+K5EPhTURERESkxOqam5n35XXlHoZUOS2bFBERERGRcaVa9yAqvImIiIiIyLhSrXsQtWxSRERERETGlWrdg6jwJiIiIiIi40q17kHUskkREREREZEqoPAmIiIiIiJSBRTeREREREREqoDCm4iIiIiISBVQeBMREREREakCCm8iIiIiIiJVQOFNRERERESkCii8iYiIiIiIVAGFNxERERERkSqg8CYiIiIiIlIFFN5ERERERESqgMKbiIiIiIhIFTDW2nKPIckYcxw4UO5xSMWaCZwo9yBkXNC1JqNB15mMFl1rMlp0rRXHfGvtLL8nKiq8iWRijNlprV1S7nHI2KdrTUaDrjMZLbrWZLToWis9LZsUERERERGpAgpvIiIiIiIiVUDhTarJd8o9ABk3dK3JaNB1JqNF15qMFl1rJaY9byIiIiIiIlVAM28iIiIiIiJVQOFNKp4x5gZjzCvGmH3GmLvKPR4ZO4wxFxljfmGMeckYs8cY8+mhx6cbY7YZY/YO/Tmt3GOVscEYEzTGPGeM+behv+tak6Izxkw1xmw0xrw89N+3q3WtSSkYYz479P/PF4wx3zXGTNC1VloKb1LRjDFB4CFgBXAp8DFjzKXlHZWMIYPA56y1bwbeAdw6dH3dBfzcWnsJ8POhv4sUw6eBl1x/17UmpfBN4KfW2jcBbyFxzelak6IyxlwI3A4ssdZeBgSBj6JrraQU3qTSXQnss9but9YOAN8DPlTmMckYYa09Yq19dujnCIkbnAtJXGNtQy9rA/64LAOUMcUY0wi8H/gH18O61qSojDGTgXcC/whgrR2w1p5G15qURg0w0RhTA0wCDqNrraQU3qTSXQgcdP29c+gxkaIyxjQBVwD/Acyx1h6BRMADZpdxaDJ2/B3weSDuekzXmhRbC3AceGRoie4/GGPq0bUmRWatPQR8Dfg9cATottb+DF1rJaXwJpXO+DymEqlSVMaYBuAHwGestWfKPR4Ze4wxHwCOWWt3lXssMubVAG8D/t5aewXQg5atSQkM7WX7ENAMXADUG2M+Xt5RjX0Kb1LpOoGLXH9vJDElL1IUxphaEsHtX6y1m4Ye7jLGzBt6fh5wrFzjkzHjGuCDxpgOEsu/32OM+Wd0rUnxdQKd1tr/GPr7RhJhTteaFNtyoN1ae9xaGwU2AUvRtVZSCm9S6X4LXGKMaTbGhEhshH28zGOSMcIYY0jsC3nJWvsN11OPA61DP7cCPxztscnYYq1dY61ttNY2kfjv2L9baz+OrjUpMmvtUeCgMeaNQw+9F3gRXWtSfL8H3mGMmTT0/9P3ktg7rmuthNSkWyqeMeZGEntFgsA/WWvvLe+IZKwwxlwL7AB2c34f0hdI7Ht7DLiYxP+cPmKtPVmWQcqYY4x5N/BX1toPGGNmoGtNiswY81YShXFCwH7gFhL/YK9rTYrKGLMO+BMS1ZufA/470ICutZJReBMREREREakCWjYpIiIiIiJSBRTeREREREREqoDCm4iIiIiISBVQeBMREREREakCCm8iIiIiIiJVQOFNRERERESkCii8iYiIiIiIVAGFNxERERERkSrw/wFVvhBlJB3mFQAAAABJRU5ErkJggg==",
-                        "text/plain": [
-                            "<Figure size 1080x432 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
+            [
+             0.8,
+             "#7fbc41"
             ],
-            "source": [
-                "#labels 'STANDING', 'SITTING', 'LAYING', 'WALKING'\n",
-                "\n",
-                "#fig, ax = plt.subplots()\n",
-                "plt.figure(figsize=(15,6))\n",
-                "for activity in df['Activity'].unique():\n",
-                "    filtered_val = data_visualisation_transformed[df['Activity']==activity,:]\n",
-                "    plt.scatter(filtered_val[:,0], filtered_val[:,1], label=activity, s=1.5)\n",
-                "\n",
-                "plt.legend()\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 59,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1080x432 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
+            [
+             0.9,
+             "#4d9221"
             ],
-            "source": [
-                "fig = plt.figure(figsize = (15, 6))\n",
-                "ax = fig.add_subplot(111, projection='3d')\n",
-                "\n",
-                "for activity in df['Activity'].unique():\n",
-                "    filtered_val = data_visualisation_transformed[df['Activity'] == activity, :]\n",
-                "    ax.scatter(\n",
-                "        filtered_val[:, 0], \n",
-                "        filtered_val[:, 1], \n",
-                "        filtered_val[:, 2], \n",
-                "        label = activity, \n",
-                "        s = 4\n",
-                "    )\n",
-                "\n",
-                "plt.legend()\n",
-                "plt.show()"
+            [
+             1,
+             "#276419"
             ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 60,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "application/vnd.plotly.v1+json": {
-                            "config": {
-                                "plotlyServerURL": "https://plot.ly"
-                            },
-                            "data": [
-                                {
-                                    "hovertemplate": "color=STANDING<br>x=%{x}<br>y=%{y}<br>z=%{z}<extra></extra>",
-                                    "legendgroup": "STANDING",
-                                    "marker": {
-                                        "color": "#636efa",
-                                        "line": {
-                                            "color": "azure",
-                                            "width": 0.05
-                                        },
-                                        "size": 2.5,
-                                        "symbol": "circle"
-                                    },
-                                    "mode": "markers",
-                                    "name": "STANDING",
-                                    "scene": "scene",
-                                    "showlegend": true,
-                                    "type": "scatter3d",
-                                    "x": [
-                                        -11.646608782317305,
-                                        -10.735523666780427,
-                                        -10.493257762047435,
-                                        -10.713837435187976,
-                                        -10.900113176058632,
-                                        -10.78327592205203,
-                                        -10.310643888654335,
-                                        -10.595253596142921,
-                                        -10.824502028190786,
-                                        -9.430757317366792,
-                                        -9.653739749909265,
-                                        -11.542945986321808,
-                                        -11.439638417968158,
-                                        -11.325376672536082,
-                                        -7.627182261032478,
-                                        -10.71499451224212,
-                                        -10.960280145316958,
-                                        -10.840997250795867,
-                                        -10.994243419905903,
-                                        -11.513889114580296,
-                                        -11.658597264064863,
-                                        -11.86623367141224,
-                                        -11.374924850009778,
-                                        -11.495942723407108,
-                                        -11.355137528195492,
-                                        -11.28515866383423,
-                                        -11.635682948295216,
-                                        -9.795992132600045,
-                                        -9.328766815199248,
-                                        -9.83813134753764,
-                                        -8.603748422649428,
-                                        -8.203126674076747,
-                                        -9.565338473899843,
-                                        -10.656232836142667,
-                                        -10.945501598202457,
-                                        -9.943588899811736,
-                                        -9.978082025655526,
-                                        -10.894676108848351,
-                                        -10.607719984712029,
-                                        -10.812635817761054,
-                                        -10.371570189051653,
-                                        -6.561288910439135,
-                                        -10.13916480679341,
-                                        -11.243402220316058,
-                                        -11.196981886022272,
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[0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
-                            "                            \n",
-                            "var gd = document.getElementById('cbd10e54-a31d-4eaa-b7df-8736d9d2a85f');\n",
-                            "var x = new MutationObserver(function (mutations, observer) {{\n",
-                            "        var display = window.getComputedStyle(gd).display;\n",
-                            "        if (!display || display === 'none') {{\n",
-                            "            console.log([gd, 'removed!']);\n",
-                            "            Plotly.purge(gd);\n",
-                            "            observer.disconnect();\n",
-                            "        }}\n",
-                            "}});\n",
-                            "\n",
-                            "// Listen for the removal of the full notebook cells\n",
-                            "var notebookContainer = gd.closest('#notebook-container');\n",
-                            "if (notebookContainer) {{\n",
-                            "    x.observe(notebookContainer, {childList: true});\n",
-                            "}}\n",
-                            "\n",
-                            "// Listen for the clearing of the current output cell\n",
-                            "var outputEl = gd.closest('.output');\n",
-                            "if (outputEl) {{\n",
-                            "    x.observe(outputEl, {childList: true});\n",
-                            "}}\n",
-                            "\n",
-                            "                        })                };                });            </script>        </div>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
             ],
-            "source": [
-                "### Interactive 3D-plot with plotly\n",
-                "\n",
-                "# representations require a lot of computing power**\n",
-                "\n",
-                "si = np.ones(7349)-0.7\n",
-                "fig = px.scatter_3d(data_visualisation_transformed, \n",
-                "                    x=data_visualisation_transformed[:, 0], \n",
-                "                    y=data_visualisation_transformed[:, 1], \n",
-                "                    z=data_visualisation_transformed[:, 2],\n",
-                "                    color=df['Activity'],\n",
-                "                    #size=si\n",
-                "                    )\n",
-                "fig.update_traces(marker=dict(size=2.5,line=dict(width=0.05,color='azure')),selector=dict(mode='markers'))\n",
-                "fig.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Ergebnisse Visualisierung mit PCA\n",
-                "\n",
-                "* Nach der PCA/Transformation in 3 Hauptkomponenten k\u00f6nnen wir die 3 Klassen in angemessener Weise visuell trennen. \n",
-                "* Allerdings beschreiben die 3 Hauptkomponenten nur 62% der Varianz der urspr\u00fcnglichen Daten. Das bedeutet, dass wir einen relativ gro\u00dfen Informationsverlust haben. \n",
-                "* Ein zweites Problem ist, dass es schwierig ist, Modelle zu interpretieren, die auf dem Ergebnis einer PCA basieren\n",
-                "\n",
-                "-->wir verwenden die PCA nur zur Visualisierung und nicht zur Modellierung"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Feature \u00dcbersicht"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Aus dem Originalpapier \u00fcber die Daten/Quelle der Daten k\u00f6nnen wir die Information erhalten, dass es die folgenden 17 Hauptmerkmale im Zeit- und Frequenzbereich des Signals gibt:"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "|Name|Time|Freq.|\n",
-                "| --- | --- | --- |\n",
-                "Body Acc |1| 1\n",
-                "Gravity Acc |1| 0\n",
-                "Body Acc Jerk |1 |1\n",
-                "Body Angular Speed |1 |1\n",
-                "Body Angular Acc |1 |0\n",
-                "Body Acc Magnitude |1 |1\n",
-                "Gravity Acc Mag |1 |0\n",
-                "Body Acc Jerk Mag |1 |1\n",
-                "Body Angular Speed Mag |1 |1\n",
-                "Body Angular Acc Mag |1 |1"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 61,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "data_temp = df.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 62,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Features: 561\n"
-                    ]
-                }
+            [
+             0.1111111111111111,
+             "#46039f"
             ],
-            "source": [
-                "print('Features: ' + str(data_temp.shape[1]))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 63,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "<class 'pandas.core.frame.DataFrame'>\n",
-                        "RangeIndex: 7349 entries, 0 to 7348\n",
-                        "Columns: 561 entries, tBodyAcc-mean()-X to angle(Z,gravityMean)\n",
-                        "dtypes: float64(561)\n",
-                        "memory usage: 31.5 MB\n"
-                    ]
-                }
+            [
+             0.2222222222222222,
+             "#7201a8"
             ],
-            "source": [
-                "data_temp.info()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Multikollinearit\u00e4t pr\u00fcfen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 65,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "variables = data_temp\n",
-                "vif = pd.DataFrame()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 66,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
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-                    "text": [
-                        "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:193: RuntimeWarning:\n",
-                        "\n",
-                        "divide by zero encountered in double_scalars\n",
-                        "\n"
-                    ]
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-                    ]
-                }
+            [
+             0.3333333333333333,
+             "#9c179e"
             ],
-            "source": [
-                "# This takes many minutes to compute \n",
-                "# This is very cpu intensive\n",
-                "\n",
-                "tempList = list()\n",
-                "total = variables.shape[1]\n",
-                "for i in range(total):\n",
-                "    print(i, ' out of ', total-1)\n",
-                "    x = variance_inflation_factor(variables.values, i)\n",
-                "    tempList.append(x)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 67,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# add name of features to vif dataframe\n",
-                "vif[\"Features\"] = variables.columns\n",
-                "\n",
-                "# add the computed VIF Values to the vif dataframe\n",
-                "vif[\"VIF\"] = tempList"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 68,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>VIF</th>\n",
-                            "      <th>Features</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>1.673655e+02</td>\n",
-                            "      <td>tBodyAcc-mean()-X</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>2.458447e+01</td>\n",
-                            "      <td>tBodyAcc-mean()-Z</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>2.000913e+06</td>\n",
-                            "      <td>tBodyAcc-std()-X</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>5.503812e+05</td>\n",
-                            "      <td>tBodyAcc-std()-Y</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>5.281580e+05</td>\n",
-                            "      <td>tBodyAcc-std()-Z</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>...</th>\n",
-                            "      <td>...</td>\n",
-                            "      <td>...</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>552</th>\n",
-                            "      <td>7.882126e+01</td>\n",
-                            "      <td>fBodyBodyGyroJerkMag-skewness()</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>553</th>\n",
-                            "      <td>1.882061e+02</td>\n",
-                            "      <td>fBodyBodyGyroJerkMag-kurtosis()</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>558</th>\n",
-                            "      <td>1.310881e+03</td>\n",
-                            "      <td>angle(X,gravityMean)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>559</th>\n",
-                            "      <td>3.877562e+02</td>\n",
-                            "      <td>angle(Y,gravityMean)</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>560</th>\n",
-                            "      <td>2.950325e+02</td>\n",
-                            "      <td>angle(Z,gravityMean)</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>522 rows \u00d7 2 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "              VIF                         Features\n",
-                            "0    1.673655e+02                tBodyAcc-mean()-X\n",
-                            "2    2.458447e+01                tBodyAcc-mean()-Z\n",
-                            "3    2.000913e+06                 tBodyAcc-std()-X\n",
-                            "4    5.503812e+05                 tBodyAcc-std()-Y\n",
-                            "5    5.281580e+05                 tBodyAcc-std()-Z\n",
-                            "..            ...                              ...\n",
-                            "552  7.882126e+01  fBodyBodyGyroJerkMag-skewness()\n",
-                            "553  1.882061e+02  fBodyBodyGyroJerkMag-kurtosis()\n",
-                            "558  1.310881e+03             angle(X,gravityMean)\n",
-                            "559  3.877562e+02             angle(Y,gravityMean)\n",
-                            "560  2.950325e+02             angle(Z,gravityMean)\n",
-                            "\n",
-                            "[522 rows x 2 columns]"
-                        ]
-                    },
-                    "execution_count": 68,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
+            [
+             0.4444444444444444,
+             "#bd3786"
             ],
-            "source": [
-                "vif[vif['VIF']>10]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 69,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "#Drop all features with a VIF > 10, as they are correlating to much with other features\n",
-                "features_to_drop = vif[vif['VIF']>10].Features"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 70,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "data_with_important_features = data_temp.drop(features_to_drop, axis = 1)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 71,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
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-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>tBodyAcc-mean()-Y</th>\n",
-                            "      <th>tBodyAcc-correlation()-X,Y</th>\n",
-                            "      <th>tBodyAcc-correlation()-X,Z</th>\n",
-                            "      <th>tBodyAcc-correlation()-Y,Z</th>\n",
-                            "      <th>tGravityAcc-correlation()-X,Y</th>\n",
-                            "      <th>tGravityAcc-correlation()-X,Z</th>\n",
-                            "      <th>tGravityAcc-correlation()-Y,Z</th>\n",
-                            "      <th>tBodyAccJerk-mean()-X</th>\n",
-                            "      <th>tBodyAccJerk-mean()-Y</th>\n",
-                            "      <th>tBodyAccJerk-mean()-Z</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>fBodyAccJerk-maxInds-X</th>\n",
-                            "      <th>fBodyAccJerk-maxInds-Y</th>\n",
-                            "      <th>fBodyAccJerk-maxInds-Z</th>\n",
-                            "      <th>fBodyGyro-meanFreq()-X</th>\n",
-                            "      <th>fBodyGyro-meanFreq()-Y</th>\n",
-                            "      <th>fBodyGyro-meanFreq()-Z</th>\n",
-                            "      <th>angle(tBodyAccMean,gravity)</th>\n",
-                            "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
-                            "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
-                            "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>-0.020294</td>\n",
-                            "      <td>0.376314</td>\n",
-                            "      <td>0.435129</td>\n",
-                            "      <td>0.660790</td>\n",
-                            "      <td>0.570222</td>\n",
-                            "      <td>0.439027</td>\n",
-                            "      <td>0.986913</td>\n",
-                            "      <td>0.077996</td>\n",
-                            "      <td>0.005001</td>\n",
-                            "      <td>-0.067831</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1.00</td>\n",
-                            "      <td>-0.24</td>\n",
-                            "      <td>-1.00</td>\n",
-                            "      <td>-0.257549</td>\n",
-                            "      <td>0.097947</td>\n",
-                            "      <td>0.547151</td>\n",
-                            "      <td>-0.112754</td>\n",
-                            "      <td>0.030400</td>\n",
-                            "      <td>-0.464761</td>\n",
-                            "      <td>-0.018446</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>-0.016411</td>\n",
-                            "      <td>-0.013429</td>\n",
-                            "      <td>-0.072692</td>\n",
-                            "      <td>0.579382</td>\n",
-                            "      <td>-0.831284</td>\n",
-                            "      <td>-0.865711</td>\n",
-                            "      <td>0.974386</td>\n",
-                            "      <td>0.074007</td>\n",
-                            "      <td>0.005771</td>\n",
-                            "      <td>0.029377</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>-0.32</td>\n",
-                            "      <td>-0.12</td>\n",
-                            "      <td>-0.32</td>\n",
-                            "      <td>-0.048167</td>\n",
-                            "      <td>-0.401608</td>\n",
-                            "      <td>-0.068178</td>\n",
-                            "      <td>0.053477</td>\n",
-                            "      <td>-0.007435</td>\n",
-                            "      <td>-0.732626</td>\n",
-                            "      <td>0.703511</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>-0.019467</td>\n",
-                            "      <td>-0.124698</td>\n",
-                            "      <td>-0.181105</td>\n",
-                            "      <td>0.608900</td>\n",
-                            "      <td>-0.181090</td>\n",
-                            "      <td>0.337936</td>\n",
-                            "      <td>0.643417</td>\n",
-                            "      <td>0.073636</td>\n",
-                            "      <td>0.003104</td>\n",
-                            "      <td>-0.009046</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>-0.16</td>\n",
-                            "      <td>-0.48</td>\n",
-                            "      <td>-0.28</td>\n",
-                            "      <td>-0.216685</td>\n",
-                            "      <td>-0.017264</td>\n",
-                            "      <td>-0.110720</td>\n",
-                            "      <td>-0.118559</td>\n",
-                            "      <td>0.177899</td>\n",
-                            "      <td>0.100699</td>\n",
-                            "      <td>0.808529</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>-0.026201</td>\n",
-                            "      <td>-0.305693</td>\n",
-                            "      <td>-0.362654</td>\n",
-                            "      <td>0.507459</td>\n",
-                            "      <td>-0.991309</td>\n",
-                            "      <td>-0.968821</td>\n",
-                            "      <td>0.984256</td>\n",
-                            "      <td>0.077321</td>\n",
-                            "      <td>0.020058</td>\n",
-                            "      <td>-0.009865</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>-0.12</td>\n",
-                            "      <td>-0.56</td>\n",
-                            "      <td>-0.28</td>\n",
-                            "      <td>0.216862</td>\n",
-                            "      <td>-0.135245</td>\n",
-                            "      <td>-0.049728</td>\n",
-                            "      <td>-0.036788</td>\n",
-                            "      <td>-0.012892</td>\n",
-                            "      <td>0.640011</td>\n",
-                            "      <td>-0.485366</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>-0.016570</td>\n",
-                            "      <td>-0.155804</td>\n",
-                            "      <td>-0.189763</td>\n",
-                            "      <td>0.599213</td>\n",
-                            "      <td>-0.408330</td>\n",
-                            "      <td>-0.184840</td>\n",
-                            "      <td>0.964797</td>\n",
-                            "      <td>0.073444</td>\n",
-                            "      <td>0.019122</td>\n",
-                            "      <td>0.016780</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>-0.32</td>\n",
-                            "      <td>-0.08</td>\n",
-                            "      <td>0.04</td>\n",
-                            "      <td>-0.153343</td>\n",
-                            "      <td>-0.088403</td>\n",
-                            "      <td>-0.162230</td>\n",
-                            "      <td>0.123320</td>\n",
-                            "      <td>0.122542</td>\n",
-                            "      <td>0.693578</td>\n",
-                            "      <td>-0.615971</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>5 rows \u00d7 39 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   tBodyAcc-mean()-Y  tBodyAcc-correlation()-X,Y  tBodyAcc-correlation()-X,Z  \\\n",
-                            "0          -0.020294                    0.376314                    0.435129   \n",
-                            "1          -0.016411                   -0.013429                   -0.072692   \n",
-                            "2          -0.019467                   -0.124698                   -0.181105   \n",
-                            "3          -0.026201                   -0.305693                   -0.362654   \n",
-                            "4          -0.016570                   -0.155804                   -0.189763   \n",
-                            "\n",
-                            "   tBodyAcc-correlation()-Y,Z  tGravityAcc-correlation()-X,Y  \\\n",
-                            "0                    0.660790                       0.570222   \n",
-                            "1                    0.579382                      -0.831284   \n",
-                            "2                    0.608900                      -0.181090   \n",
-                            "3                    0.507459                      -0.991309   \n",
-                            "4                    0.599213                      -0.408330   \n",
-                            "\n",
-                            "   tGravityAcc-correlation()-X,Z  tGravityAcc-correlation()-Y,Z  \\\n",
-                            "0                       0.439027                       0.986913   \n",
-                            "1                      -0.865711                       0.974386   \n",
-                            "2                       0.337936                       0.643417   \n",
-                            "3                      -0.968821                       0.984256   \n",
-                            "4                      -0.184840                       0.964797   \n",
-                            "\n",
-                            "   tBodyAccJerk-mean()-X  tBodyAccJerk-mean()-Y  tBodyAccJerk-mean()-Z  ...  \\\n",
-                            "0               0.077996               0.005001              -0.067831  ...   \n",
-                            "1               0.074007               0.005771               0.029377  ...   \n",
-                            "2               0.073636               0.003104              -0.009046  ...   \n",
-                            "3               0.077321               0.020058              -0.009865  ...   \n",
-                            "4               0.073444               0.019122               0.016780  ...   \n",
-                            "\n",
-                            "   fBodyAccJerk-maxInds-X  fBodyAccJerk-maxInds-Y  fBodyAccJerk-maxInds-Z  \\\n",
-                            "0                    1.00                   -0.24                   -1.00   \n",
-                            "1                   -0.32                   -0.12                   -0.32   \n",
-                            "2                   -0.16                   -0.48                   -0.28   \n",
-                            "3                   -0.12                   -0.56                   -0.28   \n",
-                            "4                   -0.32                   -0.08                    0.04   \n",
-                            "\n",
-                            "   fBodyGyro-meanFreq()-X  fBodyGyro-meanFreq()-Y  fBodyGyro-meanFreq()-Z  \\\n",
-                            "0               -0.257549                0.097947                0.547151   \n",
-                            "1               -0.048167               -0.401608               -0.068178   \n",
-                            "2               -0.216685               -0.017264               -0.110720   \n",
-                            "3                0.216862               -0.135245               -0.049728   \n",
-                            "4               -0.153343               -0.088403               -0.162230   \n",
-                            "\n",
-                            "   angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
-                            "0                    -0.112754                              0.030400   \n",
-                            "1                     0.053477                             -0.007435   \n",
-                            "2                    -0.118559                              0.177899   \n",
-                            "3                    -0.036788                             -0.012892   \n",
-                            "4                     0.123320                              0.122542   \n",
-                            "\n",
-                            "   angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \n",
-                            "0                         -0.464761                             -0.018446  \n",
-                            "1                         -0.732626                              0.703511  \n",
-                            "2                          0.100699                              0.808529  \n",
-                            "3                          0.640011                             -0.485366  \n",
-                            "4                          0.693578                             -0.615971  \n",
-                            "\n",
-                            "[5 rows x 39 columns]"
-                        ]
-                    },
-                    "execution_count": 71,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
+            [
+             0.5555555555555556,
+             "#d8576b"
             ],
-            "source": [
-                "data_with_important_features.head()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 72,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
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-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>count</th>\n",
-                            "      <th>mean</th>\n",
-                            "      <th>std</th>\n",
-                            "      <th>min</th>\n",
-                            "      <th>25%</th>\n",
-                            "      <th>50%</th>\n",
-                            "      <th>75%</th>\n",
-                            "      <th>max</th>\n",
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-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyAcc-mean()-Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.016299</td>\n",
-                            "      <td>0.038629</td>\n",
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-                            "      <th>tBodyAcc-correlation()-X,Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.024801</td>\n",
-                            "      <td>0.360763</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.242409</td>\n",
-                            "      <td>-0.058502</td>\n",
-                            "      <td>0.172579</td>\n",
-                            "      <td>1.000000</td>\n",
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-                            "    <tr>\n",
-                            "      <th>tBodyAcc-correlation()-X,Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.185987</td>\n",
-                            "      <td>0.338450</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.395440</td>\n",
-                            "      <td>-0.171765</td>\n",
-                            "      <td>0.020009</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyAcc-correlation()-Y,Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.096760</td>\n",
-                            "      <td>0.415218</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.187455</td>\n",
-                            "      <td>0.138724</td>\n",
-                            "      <td>0.405517</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tGravityAcc-correlation()-X,Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.103601</td>\n",
-                            "      <td>0.735996</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.665946</td>\n",
-                            "      <td>0.231863</td>\n",
-                            "      <td>0.851334</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tGravityAcc-correlation()-X,Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.166366</td>\n",
-                            "      <td>0.726580</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.879864</td>\n",
-                            "      <td>-0.367198</td>\n",
-                            "      <td>0.567089</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tGravityAcc-correlation()-Y,Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.075266</td>\n",
-                            "      <td>0.735481</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.688232</td>\n",
-                            "      <td>0.185269</td>\n",
-                            "      <td>0.823623</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyAccJerk-mean()-X</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.077434</td>\n",
-                            "      <td>0.108154</td>\n",
-                            "      <td>-0.581496</td>\n",
-                            "      <td>0.071259</td>\n",
-                            "      <td>0.075963</td>\n",
-                            "      <td>0.081112</td>\n",
-                            "      <td>0.855403</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyAccJerk-mean()-Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.009156</td>\n",
-                            "      <td>0.124891</td>\n",
-                            "      <td>-0.948714</td>\n",
-                            "      <td>0.000186</td>\n",
-                            "      <td>0.010928</td>\n",
-                            "      <td>0.021387</td>\n",
-                            "      <td>0.835172</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyAccJerk-mean()-Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.003619</td>\n",
-                            "      <td>0.113880</td>\n",
-                            "      <td>-0.807273</td>\n",
-                            "      <td>-0.015246</td>\n",
-                            "      <td>-0.001097</td>\n",
-                            "      <td>0.012727</td>\n",
-                            "      <td>0.735045</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyAccJerk-correlation()-X,Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.074377</td>\n",
-                            "      <td>0.242017</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.228501</td>\n",
-                            "      <td>-0.078196</td>\n",
-                            "      <td>0.075621</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyAccJerk-correlation()-X,Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.039575</td>\n",
-                            "      <td>0.284099</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.145351</td>\n",
-                            "      <td>0.062285</td>\n",
-                            "      <td>0.232011</td>\n",
-                            "      <td>1.000000</td>\n",
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-                            "    <tr>\n",
-                            "      <th>tBodyAccJerk-correlation()-Y,Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.054372</td>\n",
-                            "      <td>0.274488</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.127156</td>\n",
-                            "      <td>0.042153</td>\n",
-                            "      <td>0.222467</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyro-mean()-X</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.028776</td>\n",
-                            "      <td>0.074857</td>\n",
-                            "      <td>-0.757669</td>\n",
-                            "      <td>-0.034259</td>\n",
-                            "      <td>-0.027690</td>\n",
-                            "      <td>-0.021722</td>\n",
-                            "      <td>0.524123</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyro-mean()-Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.075976</td>\n",
-                            "      <td>0.086467</td>\n",
-                            "      <td>-0.851973</td>\n",
-                            "      <td>-0.089776</td>\n",
-                            "      <td>-0.074841</td>\n",
-                            "      <td>-0.062999</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyro-entropy()-X</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.238753</td>\n",
-                            "      <td>0.401771</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.550684</td>\n",
-                            "      <td>-0.290427</td>\n",
-                            "      <td>0.102805</td>\n",
-                            "      <td>0.944059</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyro-entropy()-Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.205995</td>\n",
-                            "      <td>0.364969</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.457364</td>\n",
-                            "      <td>-0.209115</td>\n",
-                            "      <td>0.067220</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyro-entropy()-Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.196045</td>\n",
-                            "      <td>0.459841</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.563556</td>\n",
-                            "      <td>-0.272572</td>\n",
-                            "      <td>0.228766</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyro-correlation()-X,Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.188702</td>\n",
-                            "      <td>0.405215</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.483125</td>\n",
-                            "      <td>-0.208589</td>\n",
-                            "      <td>0.084417</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyro-correlation()-X,Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.041870</td>\n",
-                            "      <td>0.408883</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.220456</td>\n",
-                            "      <td>0.018146</td>\n",
-                            "      <td>0.302452</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyro-correlation()-Y,Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.168283</td>\n",
-                            "      <td>0.440983</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.530728</td>\n",
-                            "      <td>-0.177793</td>\n",
-                            "      <td>0.150636</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyroJerk-mean()-X</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.098036</td>\n",
-                            "      <td>0.086125</td>\n",
-                            "      <td>-0.968976</td>\n",
-                            "      <td>-0.104611</td>\n",
-                            "      <td>-0.098272</td>\n",
-                            "      <td>-0.092254</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyroJerk-mean()-Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.041384</td>\n",
-                            "      <td>0.083025</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.047188</td>\n",
-                            "      <td>-0.040452</td>\n",
-                            "      <td>-0.033379</td>\n",
-                            "      <td>0.720665</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyroJerk-mean()-Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.055329</td>\n",
-                            "      <td>0.087333</td>\n",
-                            "      <td>-0.749480</td>\n",
-                            "      <td>-0.064313</td>\n",
-                            "      <td>-0.054572</td>\n",
-                            "      <td>-0.047179</td>\n",
-                            "      <td>0.600600</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyroJerk-correlation()-X,Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.017183</td>\n",
-                            "      <td>0.278593</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.169257</td>\n",
-                            "      <td>0.013469</td>\n",
-                            "      <td>0.198487</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyroJerk-correlation()-X,Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.063215</td>\n",
-                            "      <td>0.266236</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.106320</td>\n",
-                            "      <td>0.057007</td>\n",
-                            "      <td>0.226712</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>tBodyGyroJerk-correlation()-Y,Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.111144</td>\n",
-                            "      <td>0.262790</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.279851</td>\n",
-                            "      <td>-0.112190</td>\n",
-                            "      <td>0.051061</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>fBodyAcc-meanFreq()-Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.057152</td>\n",
-                            "      <td>0.247243</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.099065</td>\n",
-                            "      <td>0.053560</td>\n",
-                            "      <td>0.228640</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>fBodyAcc-meanFreq()-Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.104336</td>\n",
-                            "      <td>0.265157</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.069322</td>\n",
-                            "      <td>0.118173</td>\n",
-                            "      <td>0.288123</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>fBodyAccJerk-maxInds-X</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.294016</td>\n",
-                            "      <td>0.275268</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.480000</td>\n",
-                            "      <td>-0.280000</td>\n",
-                            "      <td>-0.120000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>fBodyAccJerk-maxInds-Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.354530</td>\n",
-                            "      <td>0.273795</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.560000</td>\n",
-                            "      <td>-0.360000</td>\n",
-                            "      <td>-0.200000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>fBodyAccJerk-maxInds-Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.261875</td>\n",
-                            "      <td>0.274175</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.440000</td>\n",
-                            "      <td>-0.280000</td>\n",
-                            "      <td>-0.080000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>fBodyGyro-meanFreq()-X</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.064359</td>\n",
-                            "      <td>0.263692</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.229611</td>\n",
-                            "      <td>-0.054360</td>\n",
-                            "      <td>0.112420</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>fBodyGyro-meanFreq()-Y</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.168671</td>\n",
-                            "      <td>0.274139</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.357676</td>\n",
-                            "      <td>-0.174484</td>\n",
-                            "      <td>0.018202</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>fBodyGyro-meanFreq()-Z</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.013731</td>\n",
-                            "      <td>0.263507</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.191156</td>\n",
-                            "      <td>-0.019524</td>\n",
-                            "      <td>0.161677</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>angle(tBodyAccMean,gravity)</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.010961</td>\n",
-                            "      <td>0.267908</td>\n",
-                            "      <td>-0.977973</td>\n",
-                            "      <td>-0.074345</td>\n",
-                            "      <td>0.008115</td>\n",
-                            "      <td>0.096642</td>\n",
-                            "      <td>0.977810</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.005884</td>\n",
-                            "      <td>0.369485</td>\n",
-                            "      <td>-0.979326</td>\n",
-                            "      <td>-0.206153</td>\n",
-                            "      <td>0.010343</td>\n",
-                            "      <td>0.211965</td>\n",
-                            "      <td>0.991899</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>0.013123</td>\n",
-                            "      <td>0.488843</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.344634</td>\n",
-                            "      <td>0.010699</td>\n",
-                            "      <td>0.381334</td>\n",
-                            "      <td>0.994532</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
-                            "      <td>7349.0</td>\n",
-                            "      <td>-0.006028</td>\n",
-                            "      <td>0.460188</td>\n",
-                            "      <td>-1.000000</td>\n",
-                            "      <td>-0.359519</td>\n",
-                            "      <td>0.001035</td>\n",
-                            "      <td>0.335377</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                       count      mean       std       min  \\\n",
-                            "tBodyAcc-mean()-Y                     7349.0 -0.016299  0.038629 -1.000000   \n",
-                            "tBodyAcc-correlation()-X,Y            7349.0 -0.024801  0.360763 -1.000000   \n",
-                            "tBodyAcc-correlation()-X,Z            7349.0 -0.185987  0.338450 -1.000000   \n",
-                            "tBodyAcc-correlation()-Y,Z            7349.0  0.096760  0.415218 -1.000000   \n",
-                            "tGravityAcc-correlation()-X,Y         7349.0  0.103601  0.735996 -1.000000   \n",
-                            "tGravityAcc-correlation()-X,Z         7349.0 -0.166366  0.726580 -1.000000   \n",
-                            "tGravityAcc-correlation()-Y,Z         7349.0  0.075266  0.735481 -1.000000   \n",
-                            "tBodyAccJerk-mean()-X                 7349.0  0.077434  0.108154 -0.581496   \n",
-                            "tBodyAccJerk-mean()-Y                 7349.0  0.009156  0.124891 -0.948714   \n",
-                            "tBodyAccJerk-mean()-Z                 7349.0 -0.003619  0.113880 -0.807273   \n",
-                            "tBodyAccJerk-correlation()-X,Y        7349.0 -0.074377  0.242017 -1.000000   \n",
-                            "tBodyAccJerk-correlation()-X,Z        7349.0  0.039575  0.284099 -1.000000   \n",
-                            "tBodyAccJerk-correlation()-Y,Z        7349.0  0.054372  0.274488 -1.000000   \n",
-                            "tBodyGyro-mean()-X                    7349.0 -0.028776  0.074857 -0.757669   \n",
-                            "tBodyGyro-mean()-Y                    7349.0 -0.075976  0.086467 -0.851973   \n",
-                            "tBodyGyro-entropy()-X                 7349.0 -0.238753  0.401771 -1.000000   \n",
-                            "tBodyGyro-entropy()-Y                 7349.0 -0.205995  0.364969 -1.000000   \n",
-                            "tBodyGyro-entropy()-Z                 7349.0 -0.196045  0.459841 -1.000000   \n",
-                            "tBodyGyro-correlation()-X,Y           7349.0 -0.188702  0.405215 -1.000000   \n",
-                            "tBodyGyro-correlation()-X,Z           7349.0  0.041870  0.408883 -1.000000   \n",
-                            "tBodyGyro-correlation()-Y,Z           7349.0 -0.168283  0.440983 -1.000000   \n",
-                            "tBodyGyroJerk-mean()-X                7349.0 -0.098036  0.086125 -0.968976   \n",
-                            "tBodyGyroJerk-mean()-Y                7349.0 -0.041384  0.083025 -1.000000   \n",
-                            "tBodyGyroJerk-mean()-Z                7349.0 -0.055329  0.087333 -0.749480   \n",
-                            "tBodyGyroJerk-correlation()-X,Y       7349.0  0.017183  0.278593 -1.000000   \n",
-                            "tBodyGyroJerk-correlation()-X,Z       7349.0  0.063215  0.266236 -1.000000   \n",
-                            "tBodyGyroJerk-correlation()-Y,Z       7349.0 -0.111144  0.262790 -1.000000   \n",
-                            "fBodyAcc-meanFreq()-Y                 7349.0  0.057152  0.247243 -1.000000   \n",
-                            "fBodyAcc-meanFreq()-Z                 7349.0  0.104336  0.265157 -1.000000   \n",
-                            "fBodyAccJerk-maxInds-X                7349.0 -0.294016  0.275268 -1.000000   \n",
-                            "fBodyAccJerk-maxInds-Y                7349.0 -0.354530  0.273795 -1.000000   \n",
-                            "fBodyAccJerk-maxInds-Z                7349.0 -0.261875  0.274175 -1.000000   \n",
-                            "fBodyGyro-meanFreq()-X                7349.0 -0.064359  0.263692 -1.000000   \n",
-                            "fBodyGyro-meanFreq()-Y                7349.0 -0.168671  0.274139 -1.000000   \n",
-                            "fBodyGyro-meanFreq()-Z                7349.0 -0.013731  0.263507 -1.000000   \n",
-                            "angle(tBodyAccMean,gravity)           7349.0  0.010961  0.267908 -0.977973   \n",
-                            "angle(tBodyAccJerkMean),gravityMean)  7349.0  0.005884  0.369485 -0.979326   \n",
-                            "angle(tBodyGyroMean,gravityMean)      7349.0  0.013123  0.488843 -1.000000   \n",
-                            "angle(tBodyGyroJerkMean,gravityMean)  7349.0 -0.006028  0.460188 -1.000000   \n",
-                            "\n",
-                            "                                           25%       50%       75%       max  \n",
-                            "tBodyAcc-mean()-Y                    -0.021403 -0.017029 -0.012643  1.000000  \n",
-                            "tBodyAcc-correlation()-X,Y           -0.242409 -0.058502  0.172579  1.000000  \n",
-                            "tBodyAcc-correlation()-X,Z           -0.395440 -0.171765  0.020009  1.000000  \n",
-                            "tBodyAcc-correlation()-Y,Z           -0.187455  0.138724  0.405517  1.000000  \n",
-                            "tGravityAcc-correlation()-X,Y        -0.665946  0.231863  0.851334  1.000000  \n",
-                            "tGravityAcc-correlation()-X,Z        -0.879864 -0.367198  0.567089  1.000000  \n",
-                            "tGravityAcc-correlation()-Y,Z        -0.688232  0.185269  0.823623  1.000000  \n",
-                            "tBodyAccJerk-mean()-X                 0.071259  0.075963  0.081112  0.855403  \n",
-                            "tBodyAccJerk-mean()-Y                 0.000186  0.010928  0.021387  0.835172  \n",
-                            "tBodyAccJerk-mean()-Z                -0.015246 -0.001097  0.012727  0.735045  \n",
-                            "tBodyAccJerk-correlation()-X,Y       -0.228501 -0.078196  0.075621  1.000000  \n",
-                            "tBodyAccJerk-correlation()-X,Z       -0.145351  0.062285  0.232011  1.000000  \n",
-                            "tBodyAccJerk-correlation()-Y,Z       -0.127156  0.042153  0.222467  1.000000  \n",
-                            "tBodyGyro-mean()-X                   -0.034259 -0.027690 -0.021722  0.524123  \n",
-                            "tBodyGyro-mean()-Y                   -0.089776 -0.074841 -0.062999  1.000000  \n",
-                            "tBodyGyro-entropy()-X                -0.550684 -0.290427  0.102805  0.944059  \n",
-                            "tBodyGyro-entropy()-Y                -0.457364 -0.209115  0.067220  1.000000  \n",
-                            "tBodyGyro-entropy()-Z                -0.563556 -0.272572  0.228766  1.000000  \n",
-                            "tBodyGyro-correlation()-X,Y          -0.483125 -0.208589  0.084417  1.000000  \n",
-                            "tBodyGyro-correlation()-X,Z          -0.220456  0.018146  0.302452  1.000000  \n",
-                            "tBodyGyro-correlation()-Y,Z          -0.530728 -0.177793  0.150636  1.000000  \n",
-                            "tBodyGyroJerk-mean()-X               -0.104611 -0.098272 -0.092254  1.000000  \n",
-                            "tBodyGyroJerk-mean()-Y               -0.047188 -0.040452 -0.033379  0.720665  \n",
-                            "tBodyGyroJerk-mean()-Z               -0.064313 -0.054572 -0.047179  0.600600  \n",
-                            "tBodyGyroJerk-correlation()-X,Y      -0.169257  0.013469  0.198487  1.000000  \n",
-                            "tBodyGyroJerk-correlation()-X,Z      -0.106320  0.057007  0.226712  1.000000  \n",
-                            "tBodyGyroJerk-correlation()-Y,Z      -0.279851 -0.112190  0.051061  1.000000  \n",
-                            "fBodyAcc-meanFreq()-Y                -0.099065  0.053560  0.228640  1.000000  \n",
-                            "fBodyAcc-meanFreq()-Z                -0.069322  0.118173  0.288123  1.000000  \n",
-                            "fBodyAccJerk-maxInds-X               -0.480000 -0.280000 -0.120000  1.000000  \n",
-                            "fBodyAccJerk-maxInds-Y               -0.560000 -0.360000 -0.200000  1.000000  \n",
-                            "fBodyAccJerk-maxInds-Z               -0.440000 -0.280000 -0.080000  1.000000  \n",
-                            "fBodyGyro-meanFreq()-X               -0.229611 -0.054360  0.112420  1.000000  \n",
-                            "fBodyGyro-meanFreq()-Y               -0.357676 -0.174484  0.018202  1.000000  \n",
-                            "fBodyGyro-meanFreq()-Z               -0.191156 -0.019524  0.161677  1.000000  \n",
-                            "angle(tBodyAccMean,gravity)          -0.074345  0.008115  0.096642  0.977810  \n",
-                            "angle(tBodyAccJerkMean),gravityMean) -0.206153  0.010343  0.211965  0.991899  \n",
-                            "angle(tBodyGyroMean,gravityMean)     -0.344634  0.010699  0.381334  0.994532  \n",
-                            "angle(tBodyGyroJerkMean,gravityMean) -0.359519  0.001035  0.335377  1.000000  "
-                        ]
-                    },
-                    "execution_count": 72,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
+            [
+             0.6666666666666666,
+             "#ed7953"
             ],
-            "source": [
-                "data_with_important_features.describe(include='all').transpose()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 73,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Text(0.5, 0, 'Feautures')"
-                        ]
-                    },
-                    "execution_count": 73,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 2016x1080 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
+            [
+             0.7777777777777778,
+             "#fb9f3a"
             ],
-            "source": [
-                "plt.figure(figsize=(28,15))\n",
-                "boxplot = sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(data_with_important_features.iloc[:,:19]))\n",
-                "boxplot.xaxis.set_ticklabels(boxplot.xaxis.get_ticklabels(), rotation=90, ha='right', fontsize=15)\n",
-                "plt.ylabel('values')\n",
-                "plt.xlabel('Feautures')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 74,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Text(0.5, 0, 'Feautures')"
-                        ]
-                    },
-                    "execution_count": 74,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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VbONRPT+sT55tPKrjRY7q+PpWHQf8VmdkumW8mvH53gAz38iNH+PVjM33VKichgsAMCu0tbXFnDlzIiJizpw5tvFg2tnGY/K8yFEd2xRVx9241Zk3b17ZmvJ8b6CWisVirFu3zlaTk+BrHDDdNFwAYAbyw9PkdXR0jG65k+e5FzpgBlqyZEnZmrHZpqg6d911V0n90Y9+NFGS+rJhw4aS+t57702UpD6ZGqWWenp6Yt++fbaanITXX3+9bA0wVRouADAD+eFpaoy4Uwvbtm2L733ve/GlL30pdZS6sX79+pL6nnvuSZSkvtimqDp33HHH6FRLlmVx2223JU5UH5YvXz56x/e8efPihhtuSJwIGEuxWIze3t7I8zx6e3vdqFWha665pmzN2N785jeX1G95y1sSJYGZT8MFAGYYPzxVZ9u2baONljzPY9u2bYkTMZsVi8XRczT6+vo8Tyt05ZVXltRXXHFFmiB1yDZF1RmZcjHdMjkbNmyIpqYm0y0wg/X09IxOdw8PD7tRq0LPPfdc2ZqxvfzyyyX1Sy+9lCgJzHwaLgAww/jhqTpf//rXS+rdu3enCUJD2LZtW8nz1JRLZXp6ekq2xvL1rXK2KarOHXfcETt37jTdMknLly+PHTt2mG6BGayvry+GhoYiImJoaGj0RhDKO//mj/NrgKnScAGAGcYPT9U5fxsx24oxnc5v6GnwVaavr6+kUeXrGzDbOIePWmlra4vm5uaIiGhubo62trbEierDs88+W7YGmCoNFwCYYfzwVJ0VK1aUreFCGjkXYryasfn6Bsx2zuGjVjo6OkqmRm05WRk3aQHTTcMFAGYYPzxV58477yyp/9f/9X9NlIRG8I/+0T8qqW+66aZESepLR0dHySHmvr4Bs4lz+KilQqEQ7e3tkWVZtLe323KyQosXLy6pr7322kRJ6svIz6fj1cCPeXYAwAzjh6fq7Nixo6R++OGHEyWhEVx88cVla8ZWKBSipaUlIiJaWlp8fQNmFefwUWsdHR2xbNkyNzBMwoYNG0rqT37yk4mS1JeRr23j1cCPabgAwAzkh6fJO/8sCGdDMJ2efPLJkvqJJ55IlKS+FIvFGBwcjIiIQ4cOufsbmFWcw0etFQqF2Lp1qxsYJmHp0qWj16tQKERra2viRMBso+ECADOQH54m76d+6qdK6v/lf/lfEiWhEZy/hdjNN9+cKEl96enpGd0r3d3fk+Mg7uq4btV56KGHYtWqVfHII4+kjlJXnFMF9eHEiRMlvwNcSBouAMCssHfv3pL6v//3/54oCY3IgauVcfd39RzEXR3XrToPPPBARER87nOfS5ykvjiHD2a+PXv2xMmTJyMi4tVXX43vfve7iRMBs42GCwAwK5w+fbpszfjcAT55528pdn7N2Nz9XR0HcVfHdavOQw89NNpEzvPclMskOIcPZr7777+/pP693/u9REnqy5IlS8rWwI9puAAAs8KcOXPK1ozPHeCT19bWNvo5NmfOHI2DCrn7uzoO4q6O61adkemWEaZcJsc5fDCzvfLKK2Vrxnb8+PGyNfBjGi4AwKzwjne8o6R+5zvfmSZInSkWi7Fz587I8zx27tzpDvAKdXR0lDRcvLBWmUKhELfeemtERNx6663u/q6Qrdiq47pV5/wtEm2ZODnO4QNmo2KxWLYGfkzDBQCYFf72b/+2pP7hD3+YKEl96enpKXlB0h3glbFtzNRlWZY6Qt2wFVt1XLfqnP/c9FwFAKichgsAzEDO1Ji8m266qaS++eabEyWpL7t27SrZq3/Xrl2JE9WP1atXxyWXXBK333576ih1o1gsxje+8Y2IiPjGN77ha1yFbMVWHdetOnfddVdJ/dGPfjRREgCA+qPhAgAzkDM1ps4WKJVZuHBh2Zrx7dixI1599dV4+OGHU0epG+dOVL3++uu+xlXIRFV1XLfq3HHHHaNTLVmWxW233ZY4ETAeN2kBzDwaLgAwwxSLxejt7Y08z6O3t9cPUBV68skny9aM7fDhw2VrxuZ5Wh0TVdVzEHd1XLfqjEy5mG6Bmc1NWgAzj4YLAMwwPT09cfr06YiIOH36tB+gKrR8+fKS+sYbb0yUpL6sXLmy5E7mlStXJk5UH3p6emJ4eDgiIoaHhz1PK2SiqnoO4q6O61adO+64I3bu3Gm6pQomDqgVN39U54YbbihbA0yVhgsAzDB9fX0lDZe+vr7EiepDf39/SX3gwIFESepLR0fH6KHSc+fOdRd4hfr6+ka3xhoaGvI8rdBzzz1XtgaodyYOqBU3f1TnoosuKqkvvvjiREmA2UrDBQBmGJMa1Tl48GDZmrEVCoV473vfGxER733ve90FXqG2trbRRlVzc3O0tbUlTlQfrr766rI1QD0zcUAtufmjOk899VRJ/a1vfStREmC20nABgBnm/EmN82vGNm/evLI1Exs5W4OJdXR0RFPTmf9KNzU1mQyqkDODgNnMxAG11NbWFnPmzImIiDlz5rj5A2CG0HABgBnm/MmMZ555JlGS+jJyh994NWMrFovx2GOPRUTEY4895m7cChUKhWhvb48sy6K9vd1kUIVuvvnmsjVAPTNxQC11dHSM3iyT57mbPwBmCA0XAJhhFi9eXFJfe+21iZLUl1/8xV8sWzM2d+NWr6OjI5YtW+YFjkl47bXXSupTp04lSgJw4dluEgDQcAGAGaa1tbWk/smf/MlESepLR0fH6LYKzc3NXgSvkLtxq1coFGLr1q2mWybhySefLKmfeOKJREmAcvbv3x933nmnbU0nyXaT1FJPT0/J55ubZioz8vPCeDXAVGm4AMAMs2fPnrI1YysUCqPTQIsXL/YieIVuuummktoWT0ynkWmq8WpgZti8eXOcOHEiNm3alDpKXbHdJLXkppnqOPcRmG4aLgAww7S1tZXcrWY7isoUi8UYHByMiIjBwUFnkVRpZC9wmA4jX9vGq4H09u/fHwMDAxERMTAwYMplkmw3Sa3Ywq46x44dK1sDTFVz6gAAQKmOjo7o7e2NU6dO2RprEnp6ekru8tu+fXvcfffdiVPNfOdv8XR+DRfSjTfeGE899dRo/a53vSthGmAsmzdvLqk3bdoUX/ziFxOlqT8j203CdBv5mSHCFnZUpru7+4I20bu6uib9mNbW1ujs7LxgGWAmcksZAMwwhUIh3v3ud0dExLvf/W7bUVRo165do9MZeZ7Hrl27EieqD21tbaN7V8+ZM8fdkUyrkbvmx6uB9DxPoT7Ywg5gZjLhAgAz0P79+0t+Z2ILFy4seVFo4cKFCdPUj5G7I0+fPh1z5sxxdyTT6tlnny2pDx06lCgJMJ4lS5aUfD9dsmRJwjRAOR0dHTEwMOD/b1RkKpMlDz30UPz+7//+aP3bv/3bcdttt12IWDDrmHABgBlm//79oy9CHjp0yN7pFTp8+HDZmrG5OxKAc61fv76kvueeexIlASYysoWd/78x3e64446SWrMFxqfhAgAzzMaNG0vq++67L1GS+nLzzTeXrRmfA36plfMnz0yiwcyzdOnS0amWJUuWRGtra+JEAMwEixYtiogz0y3A+DRcAGCGOXjwYNmasb388ssl9fHjxxMlAcZTLBbL1sDMsH79+rj00ktNt8AMt2fPnli9enV897vfTR2FBrBgwYJ4xzveYboFJuAMFwBgVnjqqadK6m9961uJktSfnp6e2LdvX2zfvj3uvvvu1HHqRrFYjE996lOxYcOGhtrKo7u7u+qtDk+fPv2Guqura9J/T2tr65T2IQfKW7p0aTz44IOpYwATuP/++2N4eDh+7/d+L/7iL/4idZy6MGfOnJL/j8yZMydhGmA2MuECADNMU1NT2RoupGKxGL29vZHnefT29sbRo0dTR6ob5zaqqMwVV1xRUl955ZVpggBAnduzZ0+88sorERHxyiuvmHKp0Fg3fwBcSCZcAGCGaWtri0cffbSkZmLuVqtOT09PDA8PR0TE8PCwKZcKnd+oWrNmTcNMuUxlsqRYLEZHR0dEnGkm/8Ef/EHDXDcAuJDuv//+ktqUC8DM4JZZAJhh1q5dW1J/+MMfTpSkvrhbrTp9fX0xNDQUERFDQ0PR19eXOFF9GKtRxcQKhcLolEtbW5tmCwBUaWS6ZbwagDQ0XACAWSHLsrI1Y2tra4vm5jNDz83NzSaqKqRRVb1FixbFZZddppkMAADMOhouADDDbNu2raT+0pe+lChJfXnnO99ZUv/cz/1cmiB1pqOjY7Q51dTUFGvWrEmcqD5oVFVv7ty5cf3115tuAWalYrEY69atcyYa0+6GG24oWzM258kB003DBQBmmN27d5etGduhQ4dK6oMHDyZKUl8KhUIsXLgwIiIWLlzoRfAKdXR0RFPTmf9Ka1RRC17Erc7+/fvjzjvvjP7+/tRR6sqXv/zlWLVqVfzZn/1Z6ih1p6enJ/bt22erSaZdoVAoqa+66qpESerLa6+9VlKfPHkyURJgttJwAYAZZuRciPFqxvbss8+WrRlbsVgcbU4988wzXsytUKFQiPb29siyLNrb2zWqmHZexK3O5s2b48SJE7Fp06bUUerKH/3RH0VExB/+4R8mTlJfisVi9Pb2Rp7n0dvb63sq0+qb3/xmSf34448nSlJfXn311bI1wFRpuADADJPnedkaLqTzt7A7v2Z8HR0dsWzZMtMtTDsv4lZn//79MTAwEBERAwMDplwq9OUvf7mkNuVSuZ6entEbZYaHhzVImVYjk7bj1QCk4asxAMwwfniqzpw5c8rWjO38Lesc/l65QqEQW7duNd3CtPMibnU2b95cUptyqczIdMsIUy6V6+vri6GhoYiIGBoa8j2VaXXixImyNQBpeAUHAGaYkfM0Rlx99dWJktSX06dPl60Zm+tWPWdqUCtexK3OyHTLeDVcaG1tbdHc3BwREc3NzdHW1pY4EXC+iy++uGwNMFUaLgAwwxw+fLikfu655xIlqS8mXKrjulXPmRrUihdxq7NkyZKyNVxoHR0do5PJTU1NtpxkWi1atKhszdhee+21sjXAVGm4AMAMM7JtzHg1Y3P2TXVuuummkvrmm29OlKS+OFODWvIibnXWr19fUt9zzz2JktSXX/u1Xyupf/3Xfz1RkvpTKBTi1ltvjYiIW2+91ZaTTKsXX3yxbA1AGhouADDDZFlWtmZsGi7VOX8bhYsuuihRkvriTA1qqVAoRHt7e2RZFu3t7V7ErdDSpUtHp1qWLFkSra2tiRPVhw9+8IMl9S//8i8nSlKfXn755YiIOH78eOIkzHbn3yRzyy23JEoCwLk0XABghhm5i3m8mrG5btV58skny9aMzZka1FpHR0csW7bMdMskrV+/Pi699FLTLZM0MuViumVyisVi/Lf/9t8iIuKpp54y/UhNudkIYGbwSgQAzDALFiwoqRcuXJgoSX255pprSmr7WFemra1t9NyWOXPmOBuiQs7UoNYKhUJs3brVdMskLV26NB588EHTLZP0wQ9+MHbu3Gm6ZZK6u7tHX/TO8zy6u7sTJ6ofxWIx1q1bp0k1CW6aAZiZNFwAYIY5cuRISX348OFESerL888/X1Kffx0ZW0dHR0nDxd3zlXGmBgDn++Y3v1lSP/7444mS1J9t27bF9773vdi2bVvqKHXDTTMAM5OGCwDMMCPnQoxXMzbbKFTn3AN+3/ve97p7vkLO1ADgfM6Tq06xWIxdu3ZFRMSjjz5qyqVCbv6ozsiE8ng1wFRpuADADOMskuqcOnWqpH7ttdcSJalfXhianJtuuimyLHNILQAREbF48eKS+tprr02UpL5s27atZCs2Uy6VKRQK0dLSEhERLS0tbv6oUJZlZWuAqfIKDgDMMCtWrChbw4VULBbjsccei4iIxx57zF2lk/CFL3whhoeH7dEPQEREbNiwoaT+5Cc/mShJfdm9e3dJ3dfXlyhJfSkWizE4OBgREYcOHfJ/uArZTQCYbhouADDDrF27tqT+8Ic/nCgJjaCnp2f0B83h4eHYvn174kT1Yf/+/TEwMBAREQMDA9Hf3584EQCpLV26NK655pqIiFi0aFG0trYmTlQfTp8+XbZmbD09PaOTQf4PVzmfb8B003ABgBnmhRdeKKlffPHFNEFoCH19fTE0NBQREUNDQ+4qrdDmzZtL6k2bNiVKQqPYvXt3rFq1anQijcrs378/7rzzTk3RSXrooYdi1apV8cgjj6SOUnfe9ra3lfzOxEYOfh+vZmz+DwcwM2m4AMAM86//9b8uqX/3d383URIaQVtb2+je1VmWRVtbW+JE9WFkumW8Gi60rVu3RkTEpz/96cRJ6svmzZvjxIkTmqKT9MADD0RExOc+97nESepLsViMp556KiIivvWtb9niqULnb5/r/yKVaWtrGz3wvbm52XUDmCE0XABghikWiyX1888/nyhJfRn5gXO8mrGtXr265KDa22+/PXGi+uDzjVravXt3yV3MplwqY+u/6jz00EMl3xdMuVTONp3VWbt2bTQ1nXl5qqmp6Q3b6zK2jo6Okuu2Zs2axInqw1ve8payNcBUabgAALPCyIuR49WMbceOHSUTLg8//HDiRPXB5xu1NDLdMsKUS2Vs/VedkemWEaZcKmeLp+oUCoXR6YyVK1fG/PnzEyeqD4VCIdrb2yPLsmhvb3fdKvTSSy+VrQGmKmnDJcuy92dZ9rdZlu3PsuyeMd7flWXZ35z9tS/LstNZls0/+76/z7Lse2ff953apweA6TFyp9p4NVxIfX19JXcye3GoMkuWLClbw4WkwVcdW/9VZ+R7wng147PFU/XWrl0bb3/72023TNLq1avjkksuMaEMMIMkewUny7I5EfFARKyOiJ+OiA9lWfbT535Mnudb8jx/Z57n74yI34mIb+R5fu4mqCvOvv/GWuUGgOl25ZVXltTuVmM6eXGoOh/5yEdK6s7OzkRJaAS2sKuOxmh1RqYex6sZny2eqlcoFGLr1q3+3ztJO3bsiFdffdWEMsAMkvKW2Z+PiP15nvfneX4qIr4cER8o8/Efioj/VJNkAJCQM1yoJS8OVefJJ58sqb/5zW8mSkIjWLduXUn9iU98IlGS+rJ+/fqS+p573rCpAmO46667SuqPfvSjiZLUH1s8UUvFYjF6e3sjz/Po7e2No0ePTvwgAKZdyobL4oj4n+fUz5x92xtkWXZpRLw/Iv78nDfnEdGbZdmeLMt+Y7xFsiz7jSzLvpNl2XeOHDlyAWIDwPRyRy615MWh6py/9Zqt2JhOK1asKJlEu/XWWxMnqg9Lly4d/R66ZMmSaG1tTZyoPtxxxx0lZ3vddtttiRPVl46Ojli2bJkbGJh2PT09MTw8HBERw8PDsX379sSJAIiISDmLPtZc8nibw94REU+ct53YzXmeD2ZZtjAi/muWZT/K8/yxN/yFef7FiPhiRMSNN95o81kAaqK7uzv6+/ureuzcuXNL6osuuii6urom9Xe0trba4oiKrV69Ovr6+uz/PQk33XRTPProoyU1TKd169bFpk2bTLdM0vr166Orq8t0yyTddddd8fu///umW6owsjUWTLe+vr7RM72Ghoair68v7r777sSpAEg54fJMRPzEOfW1ETE4zsd+MM7bTizP88Gzvx+OiAfjzBZlAFD3Lr300tE7Sy+++OK45JJLEiditrP/99Q544DptmLFiti5c6fplklaunRpPPjgg6ZbJumOO+6InTt3mm6BGaytra1kGs05fAAzQ8oJl29HxNuyLPvJiDgYZ5oqHed/UJZll0fEeyPiX5zztssioinP85fP/rk9Iv5tTVIDQAWmOl1y1113RX9/f3z2s5/1IhHTqlgsxs6dO0f3/16zZo1txSpw/hkuTzzxxBvO2QAAmC6rV6+Or371qxERkee5SWWAGSLZhEue50MR8VsRsTMifhgR/znP8+9nWfabWZb95jkfemdE9OZ5/so5b7s6Ir6ZZdl/j4j/FhEP53n+tVplB4Dpdumll8ayZcs0WyZh5OD38WrG1tPTM7odxeuvv27/7wotX768pL7xxhsTJQEAGtGOHTtKJlxMKgPMDElficjz/JE8z/9BnufX53l+/9m3fT7P88+f8zH/V57nHzzvcf15nv/s2V8/M/JYAKBxjRwaOl7N2Hbt2hV5fuaYuzzPY9euXYkT1Ycf/OAHZWsAgOnU19dX8n+4vr6+xIkAiEjccAEAIK2FCxeWrRlbsVgsqZ9//vlESQCARtTW1hZz5syJiIg5c+Y4wwVghtBwAahAsViMdevWxdGjR1NHAbignnvuubI1MDPs378/7rzzzujv708dhQbg8w1mvo6OjtGJ7jzPY82aNYkTARAR0Zw6AEA96OnpiX379sX27dvj7rvvTh0HZq3u7u4L+uJOV1fXpD6+tbU1Ojs7L9j69eDqq6+OgYGBkpqJXXLJJfHqq6+W1DCdNm/eHCdOnIhNmzbFF7/4xdRxmOV8vkF9GdlaDID0TLgATKBYLEZvb2/keR69vb2mXGCGuuKKK0rqK6+8Mk2QOmPCpTq/+7u/W1L/m3/zbxIloRHs379/tDE6MDBg6oBp5fNtakzGUyvbtm0rOcNl27ZtiRMBEGHCBWBCPT09o6Paw8PDplxgGk1luqRYLEZHR0dERDQ1NcUf/MEfxPz58y9UtFmrUCjEwYMHS2omtnz58tEpl0suuSRuuOGG1JGYxTZv3lxSmzpgOvl8mxqT8dTK17/+9ZJ69+7dsW7dujRhABhlwgVgAn19fTE0NBQREUNDQ9HX15c4ETCWQqEwOuXS1tam2VKhZ599tmzN+H73d383mpqaTLcw7c7d9m+sGi4kn2/VMxlPLZ2/jZhtxQBmBhMuABNoa2uLr33tazE0NBTNzc3R1taWOhIwjkWLFsXrr78eH/7wh1NHoQ5M9cyggwcPxpVXXhk9PT3R09NT1d/RiOcGMXlLliwpedF7yZIlCdMw2/l8q57JeGrpmmuuKZlSXrRoUcI0AIww4QIwgY6OjmhqOvPlsqmpKdasWZM4ETCeuXPnxvXXX2+6ZRJuuummkvrmm29OlKT+nDx5Mk6ePJk6Bg1g/fr1JfU999yTKAmNwOdb9UzGU0vnT1AVi8VESQA4lwkXgAkUCoVob2+Phx9+ONrb272QC8xqjbQdxVQnS7q6uiIiYsuWLRciDoxr6dKlo1MHS5YsidbW1tSRmMV8vlXPZDy1dPPNN8ejjz46Wt9yyy0J0wAwwoQLQAU6Ojpi2bJlpluAWefJJ58sWwMzw/r16+PSSy81bUBN+Hyrjsl4Umqkm2YAZjINF4AKFAqF2Lp1q+kWYNYZ2Wt+vBqYGZYuXRoPPvigaQNqwudbdQqFQtx6660REfHe977Xzw6TUCwWY926dW/YJovxffOb3yxbA5CGhgs0GP+RBeBcI3fijlcDAJNn2mByenp6Yt++fbF9+/bUUepGc3Nz2RqANPxEDQ1m27Zt8b3vfS+2bduWOgoAM8A111xTUi9atChREgCob8ViMb7xjW9ERMQ3vvENN7lVqFgsRm9vb+R5Hr29va5bhY4fP162BiANDRdoIMViMfr6+iIiYteuXf4jC0AUi8WS+vnnn0+UBADqW09PTwwNDUVExNDQkGmNCvX09IxuaTo8POy6VWjevHllawDS0HCBBrJt27aS/8iacgHg6quvLlsDAJXZtWvX6FZieZ7Hrl27EieqD319fSWNqpGbBClv5JqNVwOQhg0eoYF8/etfL6l3794d69atSxMGgBnh8OHDZWsAoDILFy6MgYGBkpqJtbW1xde+9rUYGhqK5ubmaGtrSx2pZrq7u6O/v7+qx1566aVx8uTJ0fqyyy6Lrq6uSf0dra2t0dnZWdX6AIzNhAs0kPMPbnSQIwArV64sWwPUs4ceeihWrVoVjzzySOoodWXPnj2xevXq+O53v5s6Sl1xE0N1Ojo6Suo1a9YkSlJfzp1KzrJMgw9ghjDhAg1kxYoV8eijj5bUADS21atXx1e/+tXR+vbbb0+YBuDCeuCBByIi4nOf+1zcdtttidPUj40bN8bw8HDcd9998ed//uep49SNlStXxsMPPxx5nkeWZW5iqFChUIiLL744hoaG4uKLL4758+enjlQzU50u+dCHPhRHjx6N22+/Pe6+++4LlAqAqTDhAg1k7dq10dR05mnf1NQUH/7whxMnAiC1Bx98sKT2whowWzz00EMl52mYcqnMnj174vjx4xERcfz4cVMuk9DR0RHNzWfua507d65JjQrt378/XnnllYiIeOWVV6reYqsRXX311XHZZZf5XAOYQTRcoIEUCoXR/XDb2toa6s4hAMa2e/fusjVAvRqZbhnxuc99LlGS+rJx48aS+r777kuUpP4UCoV473vfGxERt956q5+3KnT+59z999+fKEn9mTt3blx//fU+1wBmEFuKQYNZu3ZtPPfcc6ZbAIiIiNOnT5etAeqV8wurMzLdMl5NZbIsSx2hbhw8eLCkfuaZZxIlAYCpM+ECDaZQKMTWrVvdAQMAwKx2/gveXgCvzLx588rWjK9YLMY3vvGNiIj4xje+EUePHk2cCACoNQ0XAAAAZp277rqrpP7oRz+aKEl92bBhQ0l97733JkpSf3p6emJoaCgiIl5//fXYvn174kT14T3veU/ZGgDqiYYLAAAAs84dd9wxOtWSZVncdtttiRPVh+XLl49OtcybNy9uuOGGxInqx65du0a3rsvzPHbt2pU4UX3o7Owsqf+P/+P/SJQEAKZOwwUAAIBZaWTKxXTL5GzYsCGamppMt0zSwoULy9YAwOyn4QJQgWKxGOvWrbMPMzDrNDU1la0B6tkdd9wRO3fuNN0yScuXL48dO3aYbpmk5557rmzN2Lq7u8vWAFBP/EQNUIGenp7Yt2+ffZiBWWd4eLhsDQBU5oorrihbM7bHH3+8pH7ssccSJQGAqdNwAZhAsViM3t7eyPM8ent7TbkAAABv8Oyzz5atAYDZT8MFYAI9PT2jd3wPDw+bcgFmlfnz55etAQAAgMpouABMoK+vL4aGhiIiYmhoKPr6+hInArhwzp/aM8UHANTSnDlzytYAUE80XAAm0NbWFs3NzRER0dzcHG1tbYkTAQAAM8273/3ukvoXfuEXEiWpL86TA2A20XABmEBHR0c0NZ35ctnU1BRr1qxJnAjgwrnmmmvK1sDM8NBDD8WqVavikUceSR2lruzevTtWrVrlEO5J2rNnT6xevTq++93vpo5SVy666KKSeu7cuYmSAACpaLgATKBQKER7e3tkWRbt7e3ONwBmlbe97W0l9T/4B/8gURKgnAceeCAiIj73uc8lTlJftm7dGhERn/70pxMnqS8bN26M4eHhuO+++1JHqStPPPFE2ZqxjdzcNl4NAPXEdzGACnR0dMSyZctMtwCzzp49e0rq73znO4mSAON56KGHIs/ziIjI89yUS4V2795dcg6fKZfK7NmzJ44fPx4REcePHzflMgkjz9Pxasa2YsWKsjUA1BMNF4AKFAqF2Lp1q+kWYNa56aabytZAeiPTLSNMuVRmZLplhCmXymzcuLGkNuVSOZMa1Vm7dm1kWRYRZ67Zhz/84cSJAKB6vvsDADSwYrFYUh89ejRREmA87pqvzsh0y3g1YxuZbhmvZnznT2a0tbUlSlJfCoVC3HLLLRERcfPNN7vJDYC6puECUIFisRjr1q3zQiQw65y/Vcxf//VfJ0oCjGfkzu/xasbW3NxctmZs8+bNK1szvrVr145OtTQ1NcXatWsTJ6ofp06dioiI119/PXESAJgaDReACvT09MS+ffti+/btqaMAAA3mrrvuKqk/+tGPJkpSX9atW1dSf+ITn0iUpL5s2LChpL733nsTJak/hUJhdKpl5cqVJjUqVCwW46mnnoqIiG9961tucgOgrmm4AEygWCxGb29v5Hkevb29fgAAAGrqjjvuGJ1qybIsbrvttsSJ6sOKFStGp1qam5vj1ltvTZyoPixfvnx0qmXevHlxww03JE5UX9auXRtvf/vbTbdMQnd3d0n9B3/wB4mSAMDUabgATKCnpyeGh4cjImJ4eNiUCzCrLFy4sGwNzAwjUy6mWyZnZMrFdMvkbNiwIZqamky3VKFQKMTWrVtNt0zC448/XrYGgHqi4QIwgb6+vtFDVoeGhqKvry9xIoAL52Mf+1hJ/a/+1b9KEwQo64477oidO3eabpmkFStWxM6dO023TNLy5ctjx44dplsAACZJwwVgAm1tbSXbUYzsywwwG5zfRN61a1eiJAAAAFDfmlMHAJjpOjo6ore3NyIimpqaYs2aNYkTAVw4u3fvLqn7+vrecNA0ADSK7u7u6O/vr/rxBw8ejIiIxYsXV/X41tbW6OzsrHr9ejR//vySczILhULCNAAwNSZcACZQKBSivb09siyL9vZ2+zEDs8rp06fL1gBA5U6ePBknT55MHaOunNtsiYgoFouJkgDA1JlwAahAR0dHDAwMmG4BAIBZbKrTJV1dXRERsWXLlgsRBwCoMyZcACpQKBRi69atplsAAAAuoEWLFpWtAaCeaLhAgykWi7Fu3bo3jG1TnutWHdcNAC6MPXv2xOrVq+O73/1u6ih1Zf/+/XHnnXdO6UyORuS6UUvXXXdd2RoA6omGCzSYnp6e2LdvX2zfvj11lLriulXHdYOZb+HChWVrYGbYuHFjDA8Px3333Zc6Sl3ZvHlznDhxIjZt2pQ6Sl1x3ailb3/722VrAKgnGi7QQIrFYvT29kae59Hb22vqoEKuW3VcN6gPL7/8ctkaSG/Pnj1x/PjxiIg4fvy4KZcK7d+/PwYGBiIiYmBgwLRGhVw3ai3P87I1XEhNTU1la4Cp8lUFGkhPT08MDw9HRMTw8LCpgwq5btVx3aA+rFy5MrIsi4iILMti5cqViRMB59u4cWNJbcqlMps3by6pTWtUxnWj1rwATi2N/Iw6Xg0wVb6LQQPp6+uLoaGhiIgYGhqKvr6+xInqg+tWHdcN6kNHR0dJw2XNmjWJEwHnG5luGa9mbCNTGuPVjM11o9auueaaknrRokWJkgDA1Gm4QANpa2uL5ubmiIhobm6Otra2xInqg+tWHdcN6se502jAzDNv3ryyNWNbsmRJ2ZqxuW7UWrFYLKmff/75REkAYOo0XKCBuIu5Oh0dHaNj7U1NTa5bhc79fHPdYObatm1bSf2lL30pURJgPBs2bCip77333kRJ6sv69etL6nvuuSdRkvriulFrhUKhpL7qqqsSJQGAqdNwgQZSKBSipaUlIiJaWlpi/vz5iRPVh0KhEO3t7ZFlWbS3t7tuFTr3823RokWuG8xQu3fvLlsD6S1fvnx0qmXevHlxww03JE5UH5YuXTo6nbFkyZJobW1NnKg+uG7U2rPPPltSHzp0KFESAJg6DRdoIMViMQYHByPizH9ijx49mjhR/ejo6Ihly5aZ0piEcz/fBgcHfb7BDHX69OmyNTAzbNiwIZqamky3TNL69evj0ksvNaUxSa4bAEB1NFyggfT09ESe5xFxZp/+7du3J05UPwqFQmzdutWUxiSc+/mW57nPNwCYguXLl8eOHTtMt0zS0qVL48EHHzSlMUmuG7V08cUXl60BoJ5ouEAD6evri6GhoYiIGBoair6+vsSJmM18vgEAABM5ceJE2RoA6omGCzSQtra2aG5ujoiI5ubmaGtrS5yI2cznGwAAMJGRM4PGqwGgnmi4QAPp6OiIpqYzT/umpibnkUxCsViMdevWOYdkEny+AQAAE1m/fn1J7ewgAOpZc+oAQO0UCoVob2+Phx9+ONrb251HMgk9PT2xb9++2L59e9x9992p49QFn28AANAYuru7o7+/v+rHNzU1xfDwcFx88cXR3d096ce3trZGZ2dn1esDwIViwgUaTEdHRyxbtsy0wSQUi8Xo7e2NPM+jt7fXlMsk+HyDmW/ZsmUl9dvf/vZESQCARnXRRRdFRMR1112XOAkATI0JF2gwhUIhtm7dmjpGXenp6Ynh4eGIiBgeHjblMgk+32DmO3DgQEm9f//+REmAcvbs2RP33ntvbNy4MW644YbUcerG7t27Y9OmTfHJT34ybr311tRx6sYDDzwQX/nKV+LOO++M3/zN30wdhzow1emSrq6uiIjYsmXLhYgDAMmYcAGYQF9fXwwNDUVExNDQUPT19SVOBHDhXH755SX1FVdckSYIUNbGjRtjeHg47rvvvtRR6srIjR+f/vSnEyepL1/5ylciIuLBBx9MnAQAoL5ouABMoK2tLZqbzwwENjc3R1tbW+JEABfOs88+W1IfOnQoURJgPHv27Injx49HRMTx48fju9/9buJE9WH37t0lN8089thjiRPVhwceeKCk/vznP58oCQBA/dFwAZhAR0dHNDWd+XLZ1NTkPBIAoKY2btxYUptyqcz525qacqnMyHTLCFMuAACV03ABmEChUIj29vbIsiza29tj/vz5qSMBXDBZlpWtgfRGplvGqxnbyHTLeDUAAFxoGi4AFejo6Ihly5aZbgFmnVtuuaWkfs973pMoCTCeefPmla0Z28iWsOPVAABwoWm4AFSgUCjE1q1bTbcAs84HP/jBkvpDH/pQoiTAeDZs2FBS33vvvYmS1Jd169aV1J/4xCcSJakvv/RLv1RS33nnnYmSAADUHw0XAIAGtmPHjpL64YcfTpQEGM/y5ctHp1rmzZsXN9xwQ+JE9WHFihWjUy3Nzc1x6623Jk5UH+66666S+jd/8zcTJQEAqD8aLgAVKBaLsW7dujh69GjqKAAXVF9fX9kamBk2bNgQTU1NplsmaWTKxXTL5IxMuZhuAQCYHJvYAlSgp6cn9u3bF9u3b4+77747dRyAEt3d3dHf31/VY9/0pjfFiRMnRutLLrkkurq6JvV3tLa2RmdnZ1XrA5VZvnz5GybSmNiKFStixYoVqWPUnbvuuusNky4AAEzMhAvABIrFYvT29kae59Hb22vKBZhVrr766tE/Z1kWCxcuTJgGAAAA6pcJF4AJ9PT0xPDwcEREDA8Pm3IBZpypTpd86EMfiqNHj8btt9/u6xsAAABUyYQLwAT6+vpiaGgoIiKGhoacbwDMOldffXVcdtllsWbNmtRRAAAAoG5puABM4Kabbiqpb7755kRJAKbH3Llz4/rrr4/58+enjgIAAAB1S8MFYJLyPE8dAQBoMLt3745Vq1bFY489ljpKXfnyl78cq1atij/7sz9LHaWubNu2LVatWhV//Md/nDoKAEBd0XABmMCTTz5ZtgYAmG5bt26NiIhPf/rTiZPUlz/6oz+KiIg//MM/TJykvvzpn/5pRJw5yxAAgMppuECDKRaLsW7dujh69GjqKHWjra0t5syZExERc+bMiba2tsSJAIBGsnv37pLz5Ey5VObLX/5ySW3KpTLbtm0rqU25AABUTsMFGkxPT0/s27cvtm/fnjpK3ejo6IimpjNfLpuamhwqDQDU1Mh0ywhTLpUZmW4ZYcqlMiPTLSNMuQAAVE7DBRpIsViM3t7eyPM8ent7TblUqFAoREtLS0REtLS0OFQaAKipkemW8WoAAGBm0HCBBtLT0xPDw8MRETE8PGzKpULFYjEGBwcjImJwcFCjCgCoqebm5rI1AAAwM2i4QAPp6+sr2f+7r68vcaL60NPTU3LdNKoq58wgAJi6devWldSf+MQnEiWpL7/2a79WUv/6r/96oiT15Z//839eUnd0dCRKAgBQfzRcoIG0tbVFlmUREZFlmcPfK7Rr167I8zwiIvI8j127diVOVD+cGQQAU7dixYrRqZbm5ua49dZbEyeqDx/84AdL6l/+5V9OlKS+rF27tqT+1V/91URJAADqj4YLNJDVq1eXNA5uv/32xInqw8KFC8vWjK1YLMbOnTudGQQAF8DIlIvplskZmXIx3TI5I1MuplsAACYnacMly7L3Z1n2t1mW7c+y7J4x3v++LMuOZVn2N2d//etKHwu80Y4dO0omXB5++OHEierD4cOHy9aM7dyt2F5//XVTLgAwBStWrIidO3eabpmkD37wg7Fz507TLZO0du3a2Llzp+kWAIBJStZwybJsTkQ8EBGrI+KnI+JDWZb99Bgf+nie5+88++vfTvKxwDn6+vpKJlyc4VKZm2++uaS+5ZZbEiWpL7ZiAwAAAKCRpJxw+fmI2J/neX+e56ci4ssR8YEaPBYaVltbW8n+385wqc5IE4HybMUGAAAAQCNJ2XBZHBH/85z6mbNvO98/yrLsv2dZtiPLsp+Z5GMjy7LfyLLsO1mWfefIkSMXIjfUrY6OjmhqOvO0b2pqijVr1iROVB+efPLJsjVjsxUbAAAAAI0kZcMlG+Nt5982/tcRsSTP85+NiH8XEf9lEo8988Y8/2Ke5zfmeX7jggULqs0Ks0KhUIj29vbIsiza29tj/vz5qSPVhfMngUwGVeb8rdjOrwEAAABgNknZcHkmIn7inPraiBg89wPyPH8pz/PjZ//8SETMzbLsqkoeC4yto6Mjli1bZrplElavXl1S33777YmS1JfXXnutpD516lSiJAAAAAAw/VI2XL4dEW/Lsuwnsyy7KCI+GBFfOfcDsiy7Jsuy7Oyffz7O5C1W8lhgbIVCIbZu3Wq6ZRIefPDBkvov/uIvEiWpL3/1V39VUtuKDQCq95nPfCZWrVoVn/3sZ1NHqSuuW3U+8pGPxKpVq6KzszN1FACAupKs4ZLn+VBE/FZE7IyIH0bEf87z/PtZlv1mlmW/efbD/reI2Jdl2X+PiM9FxAfzM8Z8bO3/FVB/isVirFu3Lo4ePZo6St34+te/XlLv3r07TZA6k+d52RoAqFxvb29EROzYsSNxkvriulXn7//+7yMior+/P20QAIA6k3LCJfI8fyTP83+Q5/n1eZ7ff/Ztn8/z/PNn//z7eZ7/TJ7nP5vn+S/kef5kuccCE+vp6Yl9+/bF9u3bU0epGxoH1VmxYkXZGgCozGc+85mS2rRGZVy36nzkIx8pqU25AABULmnDBaitYrEYvb29ked59Pb2mnKpkMZBddauXRtNTWe+zTQ1NcWHP/zhxIkAoD6NTGmMMK1RGdetOiPTLSNMuQAAVE7DBRpIT09PnD59OiIiTp8+bcqlQhoH1SkUCtHW1hYREW1tbc4NAgAAAGBW03CBBtLX11fScOnr60ucqD5oHFRv7dq18fa3v12TCgAAAIBZT8MFGshNN91UtmZ8GgfVKRQKsXXrVk0qAJiC9vb2knr16tWJktQX1606b33rW0vq1tbWNEEAAOqQhgs0sCzLUkeoGxoHAEAqH//4x0vqj33sY2mC1BnXrTpf+MIXSuru7u5ESQAA6o+GCzSQJ598sqR+4oknEiUBAGAyRqY1TGlMjutWnZEpF9MtAACT05w6AFA7bW1t8bWvfS2Ghoaiubl59FwSAABmto9//ONvmNhgYq5bdc6fcgEAoDImXKCBdHR0RFPTmad9U1NTrFmzJnEiAAAAAIDZQcMFGkihUIj29vbIsiza29udR8K0KxaLsW7dujh69GjqKAAAAAAwrTRcoMF0dHTEsmXLTLdQEz09PbFv377Yvn176igAAAAAMK00XKDBFAqF2Lp1q+mWSTKpMXnFYjF27twZeZ5Hb2+vawcAAADArKbhAlABkxqT19PTE0NDQxER8frrr7t2AAAAAMxqGi4AEzh3UmPnzp0mNSq0a9euyPM8IiLyPI9du3YlTgQA9Wvjxo2xatWq+PSnP506Sl35zGc+E6tWrYrPfvazqaMAANAANFygwdgaa/LOndQYGhoyqVGhhQsXlq0BgMp94xvfiIiIvr6+xEnqS29vb0RE7NixI3ESAAAagYYLNBhbY02eSY3qHD58uGwNAFRm48aNJbUpl8p85jOfKalNuQAAMN2aUwcAaqdYLEZvb+/oIeZr1qyJ+fPnp4414y1cuDAGBgZKaiZ28803x6OPPlpSAwCTNzLdMqKvry8+8YlPJEpTP0amW0bs2LEjPvaxj6UJk0B3d3f09/dX9diDBw9GRMTixYurXr+1tTU6OzurfjwAQD0y4QINpKenJ4aHhyMiYnh42JRLhUxqVOe1114rqU+dOpUoCQAAk3Hy5Mk4efJk6hgAAHXHhAs0kL6+vpKzSPr6+uLuu+9OnGrmO39S45ZbbkmYpn781V/9VUn95JNPJkoCANB4pjJd0tXVFRERW7ZsuVBxAAAaggkXaCBtbW3R3Hymz9rc3BxtbW2JE9WnkfNcKO/86+S6AUB13vve95bU/g9Xmfb29pJ69erViZIAANAoNFyggXR0dERT05mnfVNTU6xZsyZxovrwxBNPlK0Z24oVK8rWAEBlNmzYUFI7v6UyH//4x0vqRjq/BQCANDRcoIEUCoVob2+PLMuivb095s+fnzpSXTj/OhUKhURJ6svatWsjy7KIiMiyLD784Q8nTgQA9WtkysV0y+SMTLmYbgEAoBac4QINpqOjIwYGBky3TMKzzz5bUh86dChRkvpSKBTi6quvjmeffTauueYaDT4AmIINGza8YdKFiX384x9/w6QLAABMFxMu0GAKhUJs3brVi9+TMDKlMV7N2IrFYjz33HMREfHcc8/F0aNHEycCAAAAgOmj4QIwgfe9730ltbNIKrNt27bI8zwiIoaHh+NLX/pS4kQAAAAAMH00XAAmsHbt2mhqOvPlsqmpKdauXZs4UX3YvXt32RoAAAAAZhMNF4AJFAqFeNe73hURET//8z9vO7YK2YoNAAAAgEai4QJQgWeeeabkdyZmKzYAAAAAGomGC8AE9u/fHwcPHoyIMw2X/v7+xInqw9q1a0enWrIssxUbAFBzv/IrvxKrVq2KX/3VX00dBQCABqDhAjCBzZs3l9SbNm1KlKS+FAqFaGlpiYiIxYsX24oNAKi5w4cPR0TEs88+mzgJAACNQMMFGsz+/fvjzjvvNKUxCQMDA2VrxlYsFkdf5Dh8+HAcPXo0cSIAoJH8yq/8SkltygUAgOnWnDoAUFubN2+OEydOxKZNm+KLX/xi6jh1obm5OYaGhkpqJtbT0xN5nkdExPDwcGzfvj3uvvvuxKkAIJ3u7u6qb3oZ2d508eLFVa/f2toanZ2dVT++3ozc+DHClAsAANPNhAs0kP37949OZwwMDJhyqdC5zZaxasbW19c3eq2Ghoair68vcSIAqF8nT56MkydPpo4BAACU4TZtaCBjnUViyoXpctNNN8Wjjz46Wt98880J0wBAelOZLunq6oqIiC1btlyoOAAAwAVmwgUaiLNIqpNlWdmasb322mtlawCA6bRw4cKS+pprrkmUBACARqHhAg1kyZIlZWvGdsstt5StGdtf/dVfldRPPvlkoiQAQCP6D//hP5TUf/zHf5woCQAAjULDBRrI+vXrS+p77rknUZL6cvHFF5fUb3rTmxIlqS/Dw8NlawCA6TYy5WK6BQCAWnCGCzSQpUuXxrx58+L48eMxb968aG1tTR2pLjzxxBMl9Te/+c1Yt25dojT1w1ZsAEBq50+5AADAdNJwgQZSLBbj5MmTERFx8uTJOHr0aMyfPz9xqplv4cKFJefdnL8fOGM7ffp02RoAAABgunV3d0d/f3+StQ8cOBAREV1dXUnWb21tjc7OziRrNyoNF2ggPT09JfX27dvj7rvvTpSmfhw+fLhszdhGpqnOrQEAAABqqb+/P/b/4H/EdW9ZVPO1LxqaExERp555ueZrP/3SoZqviYYLNJS+vr4YGhqKiIihoaHo6+vTcKnAypUr46tf/WpJzcQ2bNgQGzZsGK3vvffehGkAAACARnXdWxbFhnf/76lj1NTGp/596ggNqSl1AKB22traRs/RyLIs2traEieqD6tXry6pb7/99kRJ6svll19etgYAAACA2UTDBRrI6tWrI8/ziIjI81zjoEI7duwoqR9++OFESerL5s2bS+pNmzYlSgIAAAAA00/DBRrIjh07SiZcNA4qs2vXrrI1YxsYGChbAwBMt9/5nd+JVatW2doUAICa0HCBBtLX11cy4dLX15c4UX1YuHBh2ZqxLVmypGwNADD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",
-                        "text/plain": [
-                            "<Figure size 2016x1080 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
+            [
+             0.8888888888888888,
+             "#fdca26"
             ],
-            "source": [
-                "plt.figure(figsize=(28,15))\n",
-                "boxplot = sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(data_with_important_features.iloc[:,19:]))\n",
-                "boxplot.xaxis.set_ticklabels(boxplot.xaxis.get_ticklabels(), rotation=90, ha='right', fontsize=15)\n",
-                "plt.ylabel('values')\n",
-                "plt.xlabel('Feautures')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Zusammenfassung des Datenverst\u00e4ndnisses\n",
-                "* 1 Spalte mit der Subjekt-ID 'subject'-->nur zur Filterung, muss entfernt werden\n",
-                "* 1 Zielspalte 'Aktivit\u00e4t'-->4 Klassen (GEHEN, SITZEN, LIEGEN, STEHEN), etwas unausgewogen\n",
-                "* wir haben ein Klassifizierungsproblem mit mehreren Klassen ***\u00fcberwachtes maschinelles Lernen***\n",
-                "* 1 \"support column\" source-->is the observation from the train or test group-->train test split along this value\n",
-                "* 1 \"Unterst\u00fctzungsspalte\"-Quelle-->ist die Beobachtung aus der Trainings- oder Testgruppe-->Train-Test-Aufteilung entlang dieses Wertes \n",
-                "* Diese wurden nach M\u00f6glichkeit f\u00fcr jede der drei Achsen x, y und z berechnet\n",
-                "* In der Summe sind das 561 Merkmale\n",
-                "-->Die Analyse der einzelnen Merkmale im Detail ist aufgrund der gro\u00dfen Anzahl von Merkmalen sehr schwierig. Schlie\u00dflich haben wir Multilinerarit\u00e4t in den Daten\n",
-                "\n",
-                "* Nach der Multicollineraty-Pr\u00fcfung sind nur noch 39 Merkmale \u00fcbrig. In den Boxplots sind bei einigen Merkmalen Ausrei\u00dfer zu sehen, aber es ist schwierig, diese Ausrei\u00dfer zu analysieren und zu interpretieren, da die Daten anscheinend bereits teilweise standardisiert sind und wir keine Messeinheiten haben. Daher akzeptieren wir im Moment diese Ausrei\u00dfer\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Trainings Test Split"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 75,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "data = df.copy()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 76,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "data_train = data[data['type']=='train']\n",
-                "data_test = data[data['type']=='test']"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 78,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "X_train = data_train.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)\n",
-                "X_train = X_train.drop(features_to_drop, axis =1)\n",
-                "y_train = data_train['Activity']\n",
-                "\n",
-                "X_test = data_test.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)\n",
-                "X_test = X_test.drop(features_to_drop, axis =1)\n",
-                "y_test = data_test['Activity']"
+            [
+             1,
+             "#f0f921"
             ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 79,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "X_train: (5293, 39)\n",
-                        "y_train: (5293,)\n",
-                        "X_test: (2056, 39)\n",
-                        "y_test: (2056,)\n"
-                    ]
-                }
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
             ],
-            "source": [
-                "print('X_train: ' + str(X_train.shape))\n",
-                "print('y_train: ' + str(y_train.shape))\n",
-                "print('X_test: ' + str(X_test.shape))\n",
-                "print('y_test: ' + str(y_test.shape))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Oversampling von Daten"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 80,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "ros = RandomOverSampler(random_state=0)\n",
-                "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 81,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:>"
-                        ]
-                    },
-                    "execution_count": 81,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": "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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
+            [
+             0.1111111111111111,
+             "#46039f"
             ],
-            "source": [
-                "y_train_resampled.value_counts().plot.bar()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Modellierung"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Um verschiedene Modelle zu erstellen, ist es eine gute Praxis, Pipelines und GridSearchCV zu verwenden. Diese beiden Tools sind ein einfacher Weg, um Hyperparamter-Tuning und k-fold Cross-Validation auf verschiedenen Modellen durchzuf\u00fchren. In diesem Fall bestehen die Pipelines aus einem StandardScaler und einem One vs Rest-Klassifikator"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "__Erstellen & Evaluieren verschiedener Modelle__"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## KNN\n",
-                "K-Nearest Neighbors"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 82,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Pipeline for KNN\n",
-                "pipeline1 = Pipeline([\n",
-                "    (\"scaler\", StandardScaler()),\n",
-                "    (\"knn\", OneVsRestClassifier(KNeighborsClassifier()))\n",
-                "])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 83,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "dict_keys(['memory', 'steps', 'verbose', 'scaler', 'knn', 'scaler__copy', 'scaler__with_mean', 'scaler__with_std', 'knn__estimator__algorithm', 'knn__estimator__leaf_size', 'knn__estimator__metric', 'knn__estimator__metric_params', 'knn__estimator__n_jobs', 'knn__estimator__n_neighbors', 'knn__estimator__p', 'knn__estimator__weights', 'knn__estimator', 'knn__n_jobs'])"
-                        ]
-                    },
-                    "execution_count": 83,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "pipeline1.get_params().keys()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 84,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
-                        "[CV] knn__estimator__n_neighbors=8 ...................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ....... knn__estimator__n_neighbors=8, score=0.769, total=   3.5s\n",
-                        "[CV] knn__estimator__n_neighbors=8 ...................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.4s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ....... knn__estimator__n_neighbors=8, score=0.754, total=   3.3s\n",
-                        "[CV] knn__estimator__n_neighbors=8 ...................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    6.7s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ....... knn__estimator__n_neighbors=8, score=0.801, total=   3.3s\n",
-                        "[CV] knn__estimator__n_neighbors=8 ...................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   10.0s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ....... knn__estimator__n_neighbors=8, score=0.832, total=   3.5s\n",
-                        "[CV] knn__estimator__n_neighbors=8 ...................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   13.4s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ....... knn__estimator__n_neighbors=8, score=0.818, total=   3.3s\n",
-                        "[CV] knn__estimator__n_neighbors=10 ..................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   16.7s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ...... knn__estimator__n_neighbors=10, score=0.769, total=   3.4s\n",
-                        "[CV] knn__estimator__n_neighbors=10 ..................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:   20.1s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ...... knn__estimator__n_neighbors=10, score=0.749, total=   3.5s\n",
-                        "[CV] knn__estimator__n_neighbors=10 ..................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:   23.6s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ...... knn__estimator__n_neighbors=10, score=0.815, total=   3.4s\n",
-                        "[CV] knn__estimator__n_neighbors=10 ..................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:   27.1s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ...... knn__estimator__n_neighbors=10, score=0.828, total=   3.5s\n",
-                        "[CV] knn__estimator__n_neighbors=10 ..................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:   30.6s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ...... knn__estimator__n_neighbors=10, score=0.824, total=   3.5s\n",
-                        "[CV] knn__estimator__n_neighbors=20 ..................................\n",
-                        "[CV] ...... knn__estimator__n_neighbors=20, score=0.782, total=   3.5s\n",
-                        "[CV] knn__estimator__n_neighbors=20 ..................................\n",
-                        "[CV] ...... knn__estimator__n_neighbors=20, score=0.734, total=   3.5s\n",
-                        "[CV] knn__estimator__n_neighbors=20 ..................................\n",
-                        "[CV] ...... knn__estimator__n_neighbors=20, score=0.828, total=   3.5s\n",
-                        "[CV] knn__estimator__n_neighbors=20 ..................................\n",
-                        "[CV] ...... knn__estimator__n_neighbors=20, score=0.820, total=   3.3s\n",
-                        "[CV] knn__estimator__n_neighbors=20 ..................................\n",
-                        "[CV] ...... knn__estimator__n_neighbors=20, score=0.832, total=   3.4s\n",
-                        "[CV] knn__estimator__n_neighbors=30 ..................................\n",
-                        "[CV] ...... knn__estimator__n_neighbors=30, score=0.768, total=   3.6s\n",
-                        "[CV] knn__estimator__n_neighbors=30 ..................................\n",
-                        "[CV] ...... knn__estimator__n_neighbors=30, score=0.726, total=   3.6s\n",
-                        "[CV] knn__estimator__n_neighbors=30 ..................................\n",
-                        "[CV] ...... knn__estimator__n_neighbors=30, score=0.820, total=   3.6s\n",
-                        "[CV] knn__estimator__n_neighbors=30 ..................................\n",
-                        "[CV] ...... knn__estimator__n_neighbors=30, score=0.820, total=   3.7s\n",
-                        "[CV] knn__estimator__n_neighbors=30 ..................................\n",
-                        "[CV] ...... knn__estimator__n_neighbors=30, score=0.826, total=   3.5s\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:  1.2min finished\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "GridSearchCV(estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
-                            "                                       ('knn',\n",
-                            "                                        OneVsRestClassifier(estimator=KNeighborsClassifier()))]),\n",
-                            "             param_grid={'knn__estimator__n_neighbors': [8, 10, 20, 30]},\n",
-                            "             verbose=10)"
-                        ]
-                    },
-                    "execution_count": 84,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "#GridSearchCV-Object\n",
-                "clf = GridSearchCV(pipeline1, verbose=10, param_grid = {\n",
-                "    \"knn__estimator__n_neighbors\": [8, 10, 20, 30]\n",
-                "})\n",
-                "clf.fit(X_train_resampled, y_train_resampled)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 85,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Best parameters {'knn__estimator__n_neighbors': 20}\n",
-                        "Best model score: 0.7988723899743437\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print('Best parameters ' + str(clf.best_params_))\n",
-                "print('Best model score: ' + str(clf.best_score_))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 86,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>mean_fit_time</th>\n",
-                            "      <th>std_fit_time</th>\n",
-                            "      <th>mean_score_time</th>\n",
-                            "      <th>std_score_time</th>\n",
-                            "      <th>param_knn__estimator__n_neighbors</th>\n",
-                            "      <th>params</th>\n",
-                            "      <th>split0_test_score</th>\n",
-                            "      <th>split1_test_score</th>\n",
-                            "      <th>split2_test_score</th>\n",
-                            "      <th>split3_test_score</th>\n",
-                            "      <th>split4_test_score</th>\n",
-                            "      <th>mean_test_score</th>\n",
-                            "      <th>std_test_score</th>\n",
-                            "      <th>rank_test_score</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>0.291415</td>\n",
-                            "      <td>0.020557</td>\n",
-                            "      <td>3.064704</td>\n",
-                            "      <td>0.088606</td>\n",
-                            "      <td>8</td>\n",
-                            "      <td>{'knn__estimator__n_neighbors': 8}</td>\n",
-                            "      <td>0.769094</td>\n",
-                            "      <td>0.753996</td>\n",
-                            "      <td>0.801066</td>\n",
-                            "      <td>0.832000</td>\n",
-                            "      <td>0.817778</td>\n",
-                            "      <td>0.794787</td>\n",
-                            "      <td>0.029247</td>\n",
-                            "      <td>3</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>0.305789</td>\n",
-                            "      <td>0.014108</td>\n",
-                            "      <td>3.177124</td>\n",
-                            "      <td>0.047759</td>\n",
-                            "      <td>10</td>\n",
-                            "      <td>{'knn__estimator__n_neighbors': 10}</td>\n",
-                            "      <td>0.769094</td>\n",
-                            "      <td>0.748668</td>\n",
-                            "      <td>0.815275</td>\n",
-                            "      <td>0.827556</td>\n",
-                            "      <td>0.824000</td>\n",
-                            "      <td>0.796919</td>\n",
-                            "      <td>0.031973</td>\n",
-                            "      <td>2</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>0.291104</td>\n",
-                            "      <td>0.008228</td>\n",
-                            "      <td>3.165741</td>\n",
-                            "      <td>0.071301</td>\n",
-                            "      <td>20</td>\n",
-                            "      <td>{'knn__estimator__n_neighbors': 20}</td>\n",
-                            "      <td>0.781528</td>\n",
-                            "      <td>0.733570</td>\n",
-                            "      <td>0.827709</td>\n",
-                            "      <td>0.819556</td>\n",
-                            "      <td>0.832000</td>\n",
-                            "      <td>0.798872</td>\n",
-                            "      <td>0.037207</td>\n",
-                            "      <td>1</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>0.291344</td>\n",
-                            "      <td>0.011009</td>\n",
-                            "      <td>3.311857</td>\n",
-                            "      <td>0.029414</td>\n",
-                            "      <td>30</td>\n",
-                            "      <td>{'knn__estimator__n_neighbors': 30}</td>\n",
-                            "      <td>0.768206</td>\n",
-                            "      <td>0.726465</td>\n",
-                            "      <td>0.819716</td>\n",
-                            "      <td>0.819556</td>\n",
-                            "      <td>0.825778</td>\n",
-                            "      <td>0.791944</td>\n",
-                            "      <td>0.038806</td>\n",
-                            "      <td>4</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
-                            "0       0.291415      0.020557         3.064704        0.088606   \n",
-                            "1       0.305789      0.014108         3.177124        0.047759   \n",
-                            "2       0.291104      0.008228         3.165741        0.071301   \n",
-                            "3       0.291344      0.011009         3.311857        0.029414   \n",
-                            "\n",
-                            "  param_knn__estimator__n_neighbors                               params  \\\n",
-                            "0                                 8   {'knn__estimator__n_neighbors': 8}   \n",
-                            "1                                10  {'knn__estimator__n_neighbors': 10}   \n",
-                            "2                                20  {'knn__estimator__n_neighbors': 20}   \n",
-                            "3                                30  {'knn__estimator__n_neighbors': 30}   \n",
-                            "\n",
-                            "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
-                            "0           0.769094           0.753996           0.801066           0.832000   \n",
-                            "1           0.769094           0.748668           0.815275           0.827556   \n",
-                            "2           0.781528           0.733570           0.827709           0.819556   \n",
-                            "3           0.768206           0.726465           0.819716           0.819556   \n",
-                            "\n",
-                            "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
-                            "0           0.817778         0.794787        0.029247                3  \n",
-                            "1           0.824000         0.796919        0.031973                2  \n",
-                            "2           0.832000         0.798872        0.037207                1  \n",
-                            "3           0.825778         0.791944        0.038806                4  "
-                        ]
-                    },
-                    "execution_count": 86,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_KNN = pd.DataFrame(clf.cv_results_)\n",
-                "results_KNN"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 87,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "classification_report_test = classification_report(y_test, clf.best_estimator_.predict(X_test), output_dict=True)\n",
-                "classification_report_train = classification_report(y_train_resampled, clf.best_estimator_.predict(X_train_resampled), output_dict=True)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 88,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "#helper function\n",
-                "def make_results(classification_report_train, classification_report_test, model_type):\n",
-                "    df_rep1 = pd.DataFrame(classification_report_train)\n",
-                "    df_rep1['source'] ='train'\n",
-                "    df_rep1 = df_rep1.set_index([df_rep1.index,'source'])\n",
-                "    df_rep2 = pd.DataFrame(classification_report_test)\n",
-                "    df_rep2['source'] ='test'\n",
-                "    df_rep2 = df_rep2.set_index([df_rep2.index,'source'])\n",
-                "    frames = [df_rep1, df_rep2]\n",
-                "    df_rep = pd.concat(frames)\n",
-                "    df_rep['model_type'] = model_type\n",
-                "    df_rep = df_rep.set_index([df_rep.index,'model_type'])\n",
-                "    return df_rep.transpose()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 89,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead tr th {\n",
-                            "        text-align: left;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>support</th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>support</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>source</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model_type</th>\n",
-                            "      <th>KNN</th>\n",
-                            "      <th>KNN</th>\n",
-                            "      <th>KNN</th>\n",
-                            "      <th>KNN</th>\n",
-                            "      <th>KNN</th>\n",
-                            "      <th>KNN</th>\n",
-                            "      <th>KNN</th>\n",
-                            "      <th>KNN</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>LAYING</th>\n",
-                            "      <td>0.912238</td>\n",
-                            "      <td>0.805259</td>\n",
-                            "      <td>0.855417</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.850000</td>\n",
-                            "      <td>0.633147</td>\n",
-                            "      <td>0.725720</td>\n",
-                            "      <td>537.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SITTING</th>\n",
-                            "      <td>0.810458</td>\n",
-                            "      <td>0.793177</td>\n",
-                            "      <td>0.801724</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.631295</td>\n",
-                            "      <td>0.714868</td>\n",
-                            "      <td>0.670487</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STANDING</th>\n",
-                            "      <td>0.808673</td>\n",
-                            "      <td>0.901208</td>\n",
-                            "      <td>0.852437</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.757679</td>\n",
-                            "      <td>0.834586</td>\n",
-                            "      <td>0.794275</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WALKING</th>\n",
-                            "      <td>0.976405</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>0.988062</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.957198</td>\n",
-                            "      <td>0.991935</td>\n",
-                            "      <td>0.974257</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>accuracy</th>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>macro avg</th>\n",
-                            "      <td>0.876944</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.874410</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>0.799043</td>\n",
-                            "      <td>0.793634</td>\n",
-                            "      <td>0.791185</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>weighted avg</th>\n",
-                            "      <td>0.876944</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.874410</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>0.799743</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "      <td>0.790227</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "             precision    recall  f1-score      support precision    recall  \\\n",
-                            "source           train     train     train        train      test      test   \n",
-                            "model_type         KNN       KNN       KNN          KNN       KNN       KNN   \n",
-                            "LAYING        0.912238  0.805259  0.855417  1407.000000  0.850000  0.633147   \n",
-                            "SITTING       0.810458  0.793177  0.801724  1407.000000  0.631295  0.714868   \n",
-                            "STANDING      0.808673  0.901208  0.852437  1407.000000  0.757679  0.834586   \n",
-                            "WALKING       0.976405  1.000000  0.988062  1407.000000  0.957198  0.991935   \n",
-                            "accuracy      0.874911  0.874911  0.874911     0.874911  0.791342  0.791342   \n",
-                            "macro avg     0.876944  0.874911  0.874410  5628.000000  0.799043  0.793634   \n",
-                            "weighted avg  0.876944  0.874911  0.874410  5628.000000  0.799743  0.791342   \n",
-                            "\n",
-                            "              f1-score      support  \n",
-                            "source            test         test  \n",
-                            "model_type         KNN          KNN  \n",
-                            "LAYING        0.725720   537.000000  \n",
-                            "SITTING       0.670487   491.000000  \n",
-                            "STANDING      0.794275   532.000000  \n",
-                            "WALKING       0.974257   496.000000  \n",
-                            "accuracy      0.791342     0.791342  \n",
-                            "macro avg     0.791185  2056.000000  \n",
-                            "weighted avg  0.790227  2056.000000  "
-                        ]
-                    },
-                    "execution_count": 89,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_KNN = make_results(classification_report_train, classification_report_test, 'KNN')\n",
-                "results_KNN"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Random Forest"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 90,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Pipeline\n",
-                "pipeline2 = Pipeline([\n",
-                "    (\"scaler\", StandardScaler()),\n",
-                "    (\"ranfo\", OneVsRestClassifier(RandomForestClassifier()))\n",
-                "])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 91,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "param_grid = {'ranfo__estimator__max_depth': [5, 15, 30]}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 92,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n",
-                        "[CV] ranfo__estimator__max_depth=5 ...................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ....... ranfo__estimator__max_depth=5, score=0.793, total=   7.9s\n",
-                        "[CV] ranfo__estimator__max_depth=5 ...................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.8s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ....... ranfo__estimator__max_depth=5, score=0.752, total=   7.6s\n",
-                        "[CV] ranfo__estimator__max_depth=5 ...................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   15.4s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ....... ranfo__estimator__max_depth=5, score=0.850, total=   7.7s\n",
-                        "[CV] ranfo__estimator__max_depth=5 ...................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   23.1s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ....... ranfo__estimator__max_depth=5, score=0.843, total=   7.7s\n",
-                        "[CV] ranfo__estimator__max_depth=5 ...................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   30.7s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ....... ranfo__estimator__max_depth=5, score=0.830, total=   8.0s\n",
-                        "[CV] ranfo__estimator__max_depth=15 ..................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   38.8s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ...... ranfo__estimator__max_depth=15, score=0.827, total=  13.2s\n",
-                        "[CV] ranfo__estimator__max_depth=15 ..................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:   52.0s remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ...... ranfo__estimator__max_depth=15, score=0.813, total=  13.3s\n",
-                        "[CV] ranfo__estimator__max_depth=15 ..................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:  1.1min remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ...... ranfo__estimator__max_depth=15, score=0.877, total=  13.3s\n",
-                        "[CV] ranfo__estimator__max_depth=15 ..................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:  1.3min remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ...... ranfo__estimator__max_depth=15, score=0.876, total=  13.4s\n",
-                        "[CV] ranfo__estimator__max_depth=15 ..................................\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:  1.5min remaining:    0.0s\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[CV] ...... ranfo__estimator__max_depth=15, score=0.886, total=  13.6s\n",
-                        "[CV] ranfo__estimator__max_depth=30 ..................................\n",
-                        "[CV] ...... ranfo__estimator__max_depth=30, score=0.831, total=  13.3s\n",
-                        "[CV] ranfo__estimator__max_depth=30 ..................................\n",
-                        "[CV] ...... ranfo__estimator__max_depth=30, score=0.821, total=  13.6s\n",
-                        "[CV] ranfo__estimator__max_depth=30 ..................................\n",
-                        "[CV] ...... ranfo__estimator__max_depth=30, score=0.877, total=  13.8s\n",
-                        "[CV] ranfo__estimator__max_depth=30 ..................................\n",
-                        "[CV] ...... ranfo__estimator__max_depth=30, score=0.876, total=  13.8s\n",
-                        "[CV] ranfo__estimator__max_depth=30 ..................................\n",
-                        "[CV] ...... ranfo__estimator__max_depth=30, score=0.888, total=  14.3s\n"
-                    ]
-                },
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:  2.9min finished\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "GridSearchCV(estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
-                            "                                       ('ranfo',\n",
-                            "                                        OneVsRestClassifier(estimator=RandomForestClassifier()))]),\n",
-                            "             param_grid={'ranfo__estimator__max_depth': [5, 15, 30]},\n",
-                            "             verbose=10)"
-                        ]
-                    },
-                    "execution_count": 92,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "#GridSearchCV-Object\n",
-                "clf2 = GridSearchCV(pipeline2, param_grid = param_grid, verbose=10)\n",
-                "clf2.fit(X_train_resampled, y_train_resampled)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 93,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Best parameters {'ranfo__estimator__max_depth': 30}\n",
-                        "Best model score: 0.8583949477008093\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print('Best parameters ' + str(clf2.best_params_))\n",
-                "print('Best model score: ' + str(clf2.best_score_))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 94,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "classification_report_test = classification_report(y_test, clf2.best_estimator_.predict(X_test), output_dict=True)\n",
-                "classification_report_train = classification_report(y_train_resampled, clf2.best_estimator_.predict(X_train_resampled), output_dict=True)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 95,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead tr th {\n",
-                            "        text-align: left;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>support</th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>support</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>source</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model_type</th>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <th>RandomForest</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>LAYING</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1407.0</td>\n",
-                            "      <td>0.821705</td>\n",
-                            "      <td>0.789572</td>\n",
-                            "      <td>0.805318</td>\n",
-                            "      <td>537.00000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SITTING</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1407.0</td>\n",
-                            "      <td>0.784519</td>\n",
-                            "      <td>0.763747</td>\n",
-                            "      <td>0.773994</td>\n",
-                            "      <td>491.00000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STANDING</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1407.0</td>\n",
-                            "      <td>0.829710</td>\n",
-                            "      <td>0.860902</td>\n",
-                            "      <td>0.845018</td>\n",
-                            "      <td>532.00000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WALKING</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1407.0</td>\n",
-                            "      <td>0.970588</td>\n",
-                            "      <td>0.997984</td>\n",
-                            "      <td>0.984095</td>\n",
-                            "      <td>496.00000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>accuracy</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "      <td>0.85214</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>macro avg</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>5628.0</td>\n",
-                            "      <td>0.851631</td>\n",
-                            "      <td>0.853051</td>\n",
-                            "      <td>0.852106</td>\n",
-                            "      <td>2056.00000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>weighted avg</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>5628.0</td>\n",
-                            "      <td>0.850813</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "      <td>0.851239</td>\n",
-                            "      <td>2056.00000</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                precision       recall     f1-score      support    precision  \\\n",
-                            "source              train        train        train        train         test   \n",
-                            "model_type   RandomForest RandomForest RandomForest RandomForest RandomForest   \n",
-                            "LAYING                1.0          1.0          1.0       1407.0     0.821705   \n",
-                            "SITTING               1.0          1.0          1.0       1407.0     0.784519   \n",
-                            "STANDING              1.0          1.0          1.0       1407.0     0.829710   \n",
-                            "WALKING               1.0          1.0          1.0       1407.0     0.970588   \n",
-                            "accuracy              1.0          1.0          1.0          1.0     0.852140   \n",
-                            "macro avg             1.0          1.0          1.0       5628.0     0.851631   \n",
-                            "weighted avg          1.0          1.0          1.0       5628.0     0.850813   \n",
-                            "\n",
-                            "                   recall     f1-score      support  \n",
-                            "source               test         test         test  \n",
-                            "model_type   RandomForest RandomForest RandomForest  \n",
-                            "LAYING           0.789572     0.805318    537.00000  \n",
-                            "SITTING          0.763747     0.773994    491.00000  \n",
-                            "STANDING         0.860902     0.845018    532.00000  \n",
-                            "WALKING          0.997984     0.984095    496.00000  \n",
-                            "accuracy         0.852140     0.852140      0.85214  \n",
-                            "macro avg        0.853051     0.852106   2056.00000  \n",
-                            "weighted avg     0.852140     0.851239   2056.00000  "
-                        ]
-                    },
-                    "execution_count": 95,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_RandomForest = make_results(classification_report_train, classification_report_test, 'RandomForest')\n",
-                "results_RandomForest"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Logistische Regression"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 96,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Pipeline\n",
-                "pipeline3 = Pipeline([\n",
-                "    (\"scaler\", StandardScaler()),\n",
-                "    (\"logreg\", OneVsRestClassifier(LogisticRegression(solver='newton-cg')))\n",
-                "])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 97,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "GridSearchCV(estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
-                            "                                       ('logreg',\n",
-                            "                                        OneVsRestClassifier(estimator=LogisticRegression(solver='newton-cg')))]),\n",
-                            "             param_grid={})"
-                        ]
-                    },
-                    "execution_count": 97,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "#GridSearchCV-Object\n",
-                "clf3 = GridSearchCV(pipeline3, param_grid = {})\n",
-                "clf3.fit(X_train_resampled, y_train_resampled)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 98,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Best parameters {}\n",
-                        "Best model score: 0.7738230905861456\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print('Best parameters ' + str(clf3.best_params_))\n",
-                "print('Best model score: ' + str(clf3.best_score_))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 99,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "classification_report_test = classification_report(y_test, clf3.best_estimator_.predict(X_test), output_dict=True)\n",
-                "classification_report_train = classification_report(y_train_resampled, clf3.best_estimator_.predict(X_train_resampled), output_dict=True)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 100,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead tr th {\n",
-                            "        text-align: left;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>support</th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>support</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>source</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model_type</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>LAYING</th>\n",
-                            "      <td>0.761204</td>\n",
-                            "      <td>0.772566</td>\n",
-                            "      <td>0.766843</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.690702</td>\n",
-                            "      <td>0.677840</td>\n",
-                            "      <td>0.684211</td>\n",
-                            "      <td>537.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SITTING</th>\n",
-                            "      <td>0.747636</td>\n",
-                            "      <td>0.730633</td>\n",
-                            "      <td>0.739037</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.687970</td>\n",
-                            "      <td>0.745418</td>\n",
-                            "      <td>0.715543</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STANDING</th>\n",
-                            "      <td>0.791356</td>\n",
-                            "      <td>0.754797</td>\n",
-                            "      <td>0.772645</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.833684</td>\n",
-                            "      <td>0.744361</td>\n",
-                            "      <td>0.786495</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WALKING</th>\n",
-                            "      <td>0.924477</td>\n",
-                            "      <td>0.974414</td>\n",
-                            "      <td>0.948789</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.873563</td>\n",
-                            "      <td>0.919355</td>\n",
-                            "      <td>0.895874</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>accuracy</th>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>macro avg</th>\n",
-                            "      <td>0.806169</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.806828</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>0.771480</td>\n",
-                            "      <td>0.771743</td>\n",
-                            "      <td>0.770530</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>weighted avg</th>\n",
-                            "      <td>0.806169</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.806828</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>0.771161</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>0.769222</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "             precision    recall  f1-score      support precision    recall  \\\n",
-                            "source           train     train     train        train      test      test   \n",
-                            "model_type      LogReg    LogReg    LogReg       LogReg    LogReg    LogReg   \n",
-                            "LAYING        0.761204  0.772566  0.766843  1407.000000  0.690702  0.677840   \n",
-                            "SITTING       0.747636  0.730633  0.739037  1407.000000  0.687970  0.745418   \n",
-                            "STANDING      0.791356  0.754797  0.772645  1407.000000  0.833684  0.744361   \n",
-                            "WALKING       0.924477  0.974414  0.948789  1407.000000  0.873563  0.919355   \n",
-                            "accuracy      0.808102  0.808102  0.808102     0.808102  0.769455  0.769455   \n",
-                            "macro avg     0.806169  0.808102  0.806828  5628.000000  0.771480  0.771743   \n",
-                            "weighted avg  0.806169  0.808102  0.806828  5628.000000  0.771161  0.769455   \n",
-                            "\n",
-                            "              f1-score      support  \n",
-                            "source            test         test  \n",
-                            "model_type      LogReg       LogReg  \n",
-                            "LAYING        0.684211   537.000000  \n",
-                            "SITTING       0.715543   491.000000  \n",
-                            "STANDING      0.786495   532.000000  \n",
-                            "WALKING       0.895874   496.000000  \n",
-                            "accuracy      0.769455     0.769455  \n",
-                            "macro avg     0.770530  2056.000000  \n",
-                            "weighted avg  0.769222  2056.000000  "
-                        ]
-                    },
-                    "execution_count": 100,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_LogisticRegression = make_results(classification_report_train, classification_report_test, 'LogReg')\n",
-                "results_LogisticRegression"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Entscheidungsbaum"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 101,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Pipeline\n",
-                "pipeline4 = Pipeline([\n",
-                "    (\"scaler\", StandardScaler()),\n",
-                "    (\"decisiontree\", OneVsRestClassifier(DecisionTreeClassifier()))\n",
-                "])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 102,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "GridSearchCV(estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
-                            "                                       ('decisiontree',\n",
-                            "                                        OneVsRestClassifier(estimator=DecisionTreeClassifier()))]),\n",
-                            "             param_grid={})"
-                        ]
-                    },
-                    "execution_count": 102,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "#GridSearchCV-Object\n",
-                "clf4 = GridSearchCV(pipeline4, param_grid = {})\n",
-                "clf4.fit(X_train_resampled, y_train_resampled)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 103,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Best parameters {}\n",
-                        "Best model score: 0.6778698243536609\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print('Best parameters ' + str(clf4.best_params_))\n",
-                "print('Best model score: ' + str(clf4.best_score_))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 104,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "classification_report_test = classification_report(y_test, clf4.best_estimator_.predict(X_test), output_dict=True)\n",
-                "classification_report_train = classification_report(y_train_resampled, clf4.best_estimator_.predict(X_train_resampled), output_dict=True)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 105,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead tr th {\n",
-                            "        text-align: left;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>support</th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>support</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>source</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model_type</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>LAYING</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1407.0</td>\n",
-                            "      <td>0.803797</td>\n",
-                            "      <td>0.472998</td>\n",
-                            "      <td>0.595545</td>\n",
-                            "      <td>537.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SITTING</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1407.0</td>\n",
-                            "      <td>0.642132</td>\n",
-                            "      <td>0.515275</td>\n",
-                            "      <td>0.571751</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STANDING</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1407.0</td>\n",
-                            "      <td>0.695391</td>\n",
-                            "      <td>0.652256</td>\n",
-                            "      <td>0.673133</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WALKING</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1407.0</td>\n",
-                            "      <td>0.578512</td>\n",
-                            "      <td>0.987903</td>\n",
-                            "      <td>0.729710</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>accuracy</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>macro avg</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>5628.0</td>\n",
-                            "      <td>0.679958</td>\n",
-                            "      <td>0.657108</td>\n",
-                            "      <td>0.642535</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>weighted avg</th>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>1.0</td>\n",
-                            "      <td>5628.0</td>\n",
-                            "      <td>0.682790</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "      <td>0.642306</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                precision       recall     f1-score      support    precision  \\\n",
-                            "source              train        train        train        train         test   \n",
-                            "model_type   DecisionTree DecisionTree DecisionTree DecisionTree DecisionTree   \n",
-                            "LAYING                1.0          1.0          1.0       1407.0     0.803797   \n",
-                            "SITTING               1.0          1.0          1.0       1407.0     0.642132   \n",
-                            "STANDING              1.0          1.0          1.0       1407.0     0.695391   \n",
-                            "WALKING               1.0          1.0          1.0       1407.0     0.578512   \n",
-                            "accuracy              1.0          1.0          1.0          1.0     0.653696   \n",
-                            "macro avg             1.0          1.0          1.0       5628.0     0.679958   \n",
-                            "weighted avg          1.0          1.0          1.0       5628.0     0.682790   \n",
-                            "\n",
-                            "                   recall     f1-score      support  \n",
-                            "source               test         test         test  \n",
-                            "model_type   DecisionTree DecisionTree DecisionTree  \n",
-                            "LAYING           0.472998     0.595545   537.000000  \n",
-                            "SITTING          0.515275     0.571751   491.000000  \n",
-                            "STANDING         0.652256     0.673133   532.000000  \n",
-                            "WALKING          0.987903     0.729710   496.000000  \n",
-                            "accuracy         0.653696     0.653696     0.653696  \n",
-                            "macro avg        0.657108     0.642535  2056.000000  \n",
-                            "weighted avg     0.653696     0.642306  2056.000000  "
-                        ]
-                    },
-                    "execution_count": 105,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_DecisionTree = make_results(classification_report_train, classification_report_test, 'DecisionTree')\n",
-                "results_DecisionTree"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## SVM\n",
-                "Support Vector Machine"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 106,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Pipeline\n",
-                "pipeline5 = Pipeline([\n",
-                "    (\"scaler\", StandardScaler()),\n",
-                "    (\"svm\", OneVsRestClassifier(svm.SVC()))\n",
-                "])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 107,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "param_grid = {\n",
-                "    'svm__estimator__kernel': ['linear']}"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 108,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "GridSearchCV(estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
-                            "                                       ('svm',\n",
-                            "                                        OneVsRestClassifier(estimator=SVC()))]),\n",
-                            "             param_grid={'svm__estimator__kernel': ['linear']})"
-                        ]
-                    },
-                    "execution_count": 108,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "#GridSearchCV-Object\n",
-                "clf5 = GridSearchCV(pipeline5, param_grid)\n",
-                "clf5.fit(X_train_resampled, y_train_resampled)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 109,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Best parameters {'svm__estimator__kernel': 'linear'}\n",
-                        "Best model score: 0.776133570159858\n"
-                    ]
-                }
-            ],
-            "source": [
-                "print('Best parameters ' + str(clf5.best_params_))\n",
-                "print('Best model score: ' + str(clf5.best_score_))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 110,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>mean_fit_time</th>\n",
-                            "      <th>std_fit_time</th>\n",
-                            "      <th>mean_score_time</th>\n",
-                            "      <th>std_score_time</th>\n",
-                            "      <th>param_svm__estimator__kernel</th>\n",
-                            "      <th>params</th>\n",
-                            "      <th>split0_test_score</th>\n",
-                            "      <th>split1_test_score</th>\n",
-                            "      <th>split2_test_score</th>\n",
-                            "      <th>split3_test_score</th>\n",
-                            "      <th>split4_test_score</th>\n",
-                            "      <th>mean_test_score</th>\n",
-                            "      <th>std_test_score</th>\n",
-                            "      <th>rank_test_score</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>6.828282</td>\n",
-                            "      <td>0.334218</td>\n",
-                            "      <td>0.329354</td>\n",
-                            "      <td>0.019774</td>\n",
-                            "      <td>linear</td>\n",
-                            "      <td>{'svm__estimator__kernel': 'linear'}</td>\n",
-                            "      <td>0.740675</td>\n",
-                            "      <td>0.723801</td>\n",
-                            "      <td>0.784192</td>\n",
-                            "      <td>0.818667</td>\n",
-                            "      <td>0.813333</td>\n",
-                            "      <td>0.776134</td>\n",
-                            "      <td>0.038089</td>\n",
-                            "      <td>1</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
-                            "0       6.828282      0.334218         0.329354        0.019774   \n",
-                            "\n",
-                            "  param_svm__estimator__kernel                                params  \\\n",
-                            "0                       linear  {'svm__estimator__kernel': 'linear'}   \n",
-                            "\n",
-                            "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
-                            "0           0.740675           0.723801           0.784192           0.818667   \n",
-                            "\n",
-                            "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
-                            "0           0.813333         0.776134        0.038089                1  "
-                        ]
-                    },
-                    "execution_count": 110,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_SVM= pd.DataFrame(clf5.cv_results_)\n",
-                "results_SVM"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 111,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "classification_report_test = classification_report(y_test, clf5.best_estimator_.predict(X_test), output_dict=True)\n",
-                "classification_report_train = classification_report(y_train_resampled, clf5.best_estimator_.predict(X_train_resampled), output_dict=True)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 112,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead tr th {\n",
-                            "        text-align: left;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>support</th>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>support</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>source</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>train</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>test</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model_type</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <th>SVM</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>LAYING</th>\n",
-                            "      <td>0.773826</td>\n",
-                            "      <td>0.773276</td>\n",
-                            "      <td>0.773551</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.689394</td>\n",
-                            "      <td>0.677840</td>\n",
-                            "      <td>0.683568</td>\n",
-                            "      <td>537.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SITTING</th>\n",
-                            "      <td>0.753613</td>\n",
-                            "      <td>0.741294</td>\n",
-                            "      <td>0.747402</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.675978</td>\n",
-                            "      <td>0.739308</td>\n",
-                            "      <td>0.706226</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>STANDING</th>\n",
-                            "      <td>0.790169</td>\n",
-                            "      <td>0.765458</td>\n",
-                            "      <td>0.777617</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.837607</td>\n",
-                            "      <td>0.736842</td>\n",
-                            "      <td>0.784000</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>WALKING</th>\n",
-                            "      <td>0.931525</td>\n",
-                            "      <td>0.976546</td>\n",
-                            "      <td>0.953505</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.871893</td>\n",
-                            "      <td>0.919355</td>\n",
-                            "      <td>0.894995</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>accuracy</th>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>macro avg</th>\n",
-                            "      <td>0.812283</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.813019</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>0.768718</td>\n",
-                            "      <td>0.768336</td>\n",
-                            "      <td>0.767197</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>weighted avg</th>\n",
-                            "      <td>0.812283</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.813019</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>0.768568</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>0.765972</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "             precision    recall  f1-score      support precision    recall  \\\n",
-                            "source           train     train     train        train      test      test   \n",
-                            "model_type         SVM       SVM       SVM          SVM       SVM       SVM   \n",
-                            "LAYING        0.773826  0.773276  0.773551  1407.000000  0.689394  0.677840   \n",
-                            "SITTING       0.753613  0.741294  0.747402  1407.000000  0.675978  0.739308   \n",
-                            "STANDING      0.790169  0.765458  0.777617  1407.000000  0.837607  0.736842   \n",
-                            "WALKING       0.931525  0.976546  0.953505  1407.000000  0.871893  0.919355   \n",
-                            "accuracy      0.814144  0.814144  0.814144     0.814144  0.766051  0.766051   \n",
-                            "macro avg     0.812283  0.814144  0.813019  5628.000000  0.768718  0.768336   \n",
-                            "weighted avg  0.812283  0.814144  0.813019  5628.000000  0.768568  0.766051   \n",
-                            "\n",
-                            "              f1-score      support  \n",
-                            "source            test         test  \n",
-                            "model_type         SVM          SVM  \n",
-                            "LAYING        0.683568   537.000000  \n",
-                            "SITTING       0.706226   491.000000  \n",
-                            "STANDING      0.784000   532.000000  \n",
-                            "WALKING       0.894995   496.000000  \n",
-                            "accuracy      0.766051     0.766051  \n",
-                            "macro avg     0.767197  2056.000000  \n",
-                            "weighted avg  0.765972  2056.000000  "
-                        ]
-                    },
-                    "execution_count": 112,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "results_SVM = make_results(classification_report_train, classification_report_test, 'SVM')\n",
-                "results_SVM"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Zusammenfassung"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 113,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "frames = [results_KNN, \n",
-                "           results_RandomForest,\n",
-                "           results_LogisticRegression,\n",
-                "           results_DecisionTree,\n",
-                "           results_SVM,\n",
-                "           ]\n",
-                "final_results = pd.concat(frames, axis=1).reindex(frames[0].index).transpose()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 114,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
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-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th>LAYING</th>\n",
-                            "      <th>SITTING</th>\n",
-                            "      <th>STANDING</th>\n",
-                            "      <th>WALKING</th>\n",
-                            "      <th>accuracy</th>\n",
-                            "      <th>macro avg</th>\n",
-                            "      <th>weighted avg</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th>source</th>\n",
-                            "      <th>model_type</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">f1-score</th>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">test</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <td>0.595545</td>\n",
-                            "      <td>0.571751</td>\n",
-                            "      <td>0.673133</td>\n",
-                            "      <td>0.729710</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "      <td>0.642535</td>\n",
-                            "      <td>0.642306</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>KNN</th>\n",
-                            "      <td>0.725720</td>\n",
-                            "      <td>0.670487</td>\n",
-                            "      <td>0.794275</td>\n",
-                            "      <td>0.974257</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "      <td>0.791185</td>\n",
-                            "      <td>0.790227</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>0.684211</td>\n",
-                            "      <td>0.715543</td>\n",
-                            "      <td>0.786495</td>\n",
-                            "      <td>0.895874</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>0.770530</td>\n",
-                            "      <td>0.769222</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <td>0.805318</td>\n",
-                            "      <td>0.773994</td>\n",
-                            "      <td>0.845018</td>\n",
-                            "      <td>0.984095</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "      <td>0.852106</td>\n",
-                            "      <td>0.851239</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>0.683568</td>\n",
-                            "      <td>0.706226</td>\n",
-                            "      <td>0.784000</td>\n",
-                            "      <td>0.894995</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>0.767197</td>\n",
-                            "      <td>0.765972</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">precision</th>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">test</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <td>0.803797</td>\n",
-                            "      <td>0.642132</td>\n",
-                            "      <td>0.695391</td>\n",
-                            "      <td>0.578512</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "      <td>0.679958</td>\n",
-                            "      <td>0.682790</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>KNN</th>\n",
-                            "      <td>0.850000</td>\n",
-                            "      <td>0.631295</td>\n",
-                            "      <td>0.757679</td>\n",
-                            "      <td>0.957198</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "      <td>0.799043</td>\n",
-                            "      <td>0.799743</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>0.690702</td>\n",
-                            "      <td>0.687970</td>\n",
-                            "      <td>0.833684</td>\n",
-                            "      <td>0.873563</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>0.771480</td>\n",
-                            "      <td>0.771161</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <td>0.821705</td>\n",
-                            "      <td>0.784519</td>\n",
-                            "      <td>0.829710</td>\n",
-                            "      <td>0.970588</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "      <td>0.851631</td>\n",
-                            "      <td>0.850813</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>0.689394</td>\n",
-                            "      <td>0.675978</td>\n",
-                            "      <td>0.837607</td>\n",
-                            "      <td>0.871893</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>0.768718</td>\n",
-                            "      <td>0.768568</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">recall</th>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">test</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <td>0.472998</td>\n",
-                            "      <td>0.515275</td>\n",
-                            "      <td>0.652256</td>\n",
-                            "      <td>0.987903</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "      <td>0.657108</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>KNN</th>\n",
-                            "      <td>0.633147</td>\n",
-                            "      <td>0.714868</td>\n",
-                            "      <td>0.834586</td>\n",
-                            "      <td>0.991935</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "      <td>0.793634</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>0.677840</td>\n",
-                            "      <td>0.745418</td>\n",
-                            "      <td>0.744361</td>\n",
-                            "      <td>0.919355</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>0.771743</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <td>0.789572</td>\n",
-                            "      <td>0.763747</td>\n",
-                            "      <td>0.860902</td>\n",
-                            "      <td>0.997984</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "      <td>0.853051</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>0.677840</td>\n",
-                            "      <td>0.739308</td>\n",
-                            "      <td>0.736842</td>\n",
-                            "      <td>0.919355</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>0.768336</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">support</th>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">test</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <td>537.000000</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>KNN</th>\n",
-                            "      <td>537.000000</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>537.000000</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <td>537.000000</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>537.000000</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">f1-score</th>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">train</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>KNN</th>\n",
-                            "      <td>0.855417</td>\n",
-                            "      <td>0.801724</td>\n",
-                            "      <td>0.852437</td>\n",
-                            "      <td>0.988062</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.874410</td>\n",
-                            "      <td>0.874410</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>0.766843</td>\n",
-                            "      <td>0.739037</td>\n",
-                            "      <td>0.772645</td>\n",
-                            "      <td>0.948789</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.806828</td>\n",
-                            "      <td>0.806828</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>0.773551</td>\n",
-                            "      <td>0.747402</td>\n",
-                            "      <td>0.777617</td>\n",
-                            "      <td>0.953505</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.813019</td>\n",
-                            "      <td>0.813019</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">precision</th>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">train</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>KNN</th>\n",
-                            "      <td>0.912238</td>\n",
-                            "      <td>0.810458</td>\n",
-                            "      <td>0.808673</td>\n",
-                            "      <td>0.976405</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.876944</td>\n",
-                            "      <td>0.876944</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>0.761204</td>\n",
-                            "      <td>0.747636</td>\n",
-                            "      <td>0.791356</td>\n",
-                            "      <td>0.924477</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.806169</td>\n",
-                            "      <td>0.806169</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>0.773826</td>\n",
-                            "      <td>0.753613</td>\n",
-                            "      <td>0.790169</td>\n",
-                            "      <td>0.931525</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.812283</td>\n",
-                            "      <td>0.812283</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">recall</th>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">train</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>KNN</th>\n",
-                            "      <td>0.805259</td>\n",
-                            "      <td>0.793177</td>\n",
-                            "      <td>0.901208</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>0.772566</td>\n",
-                            "      <td>0.730633</td>\n",
-                            "      <td>0.754797</td>\n",
-                            "      <td>0.974414</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>0.773276</td>\n",
-                            "      <td>0.741294</td>\n",
-                            "      <td>0.765458</td>\n",
-                            "      <td>0.976546</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">support</th>\n",
-                            "      <th rowspan=\"5\" valign=\"top\">train</th>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>KNN</th>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>1407.000000</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "      <td>5628.000000</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                    LAYING      SITTING     STANDING  \\\n",
-                            "          source model_type                                            \n",
-                            "f1-score  test   DecisionTree     0.595545     0.571751     0.673133   \n",
-                            "                 KNN              0.725720     0.670487     0.794275   \n",
-                            "                 LogReg           0.684211     0.715543     0.786495   \n",
-                            "                 RandomForest     0.805318     0.773994     0.845018   \n",
-                            "                 SVM              0.683568     0.706226     0.784000   \n",
-                            "precision test   DecisionTree     0.803797     0.642132     0.695391   \n",
-                            "                 KNN              0.850000     0.631295     0.757679   \n",
-                            "                 LogReg           0.690702     0.687970     0.833684   \n",
-                            "                 RandomForest     0.821705     0.784519     0.829710   \n",
-                            "                 SVM              0.689394     0.675978     0.837607   \n",
-                            "recall    test   DecisionTree     0.472998     0.515275     0.652256   \n",
-                            "                 KNN              0.633147     0.714868     0.834586   \n",
-                            "                 LogReg           0.677840     0.745418     0.744361   \n",
-                            "                 RandomForest     0.789572     0.763747     0.860902   \n",
-                            "                 SVM              0.677840     0.739308     0.736842   \n",
-                            "support   test   DecisionTree   537.000000   491.000000   532.000000   \n",
-                            "                 KNN            537.000000   491.000000   532.000000   \n",
-                            "                 LogReg         537.000000   491.000000   532.000000   \n",
-                            "                 RandomForest   537.000000   491.000000   532.000000   \n",
-                            "                 SVM            537.000000   491.000000   532.000000   \n",
-                            "f1-score  train  DecisionTree     1.000000     1.000000     1.000000   \n",
-                            "                 KNN              0.855417     0.801724     0.852437   \n",
-                            "                 LogReg           0.766843     0.739037     0.772645   \n",
-                            "                 RandomForest     1.000000     1.000000     1.000000   \n",
-                            "                 SVM              0.773551     0.747402     0.777617   \n",
-                            "precision train  DecisionTree     1.000000     1.000000     1.000000   \n",
-                            "                 KNN              0.912238     0.810458     0.808673   \n",
-                            "                 LogReg           0.761204     0.747636     0.791356   \n",
-                            "                 RandomForest     1.000000     1.000000     1.000000   \n",
-                            "                 SVM              0.773826     0.753613     0.790169   \n",
-                            "recall    train  DecisionTree     1.000000     1.000000     1.000000   \n",
-                            "                 KNN              0.805259     0.793177     0.901208   \n",
-                            "                 LogReg           0.772566     0.730633     0.754797   \n",
-                            "                 RandomForest     1.000000     1.000000     1.000000   \n",
-                            "                 SVM              0.773276     0.741294     0.765458   \n",
-                            "support   train  DecisionTree  1407.000000  1407.000000  1407.000000   \n",
-                            "                 KNN           1407.000000  1407.000000  1407.000000   \n",
-                            "                 LogReg        1407.000000  1407.000000  1407.000000   \n",
-                            "                 RandomForest  1407.000000  1407.000000  1407.000000   \n",
-                            "                 SVM           1407.000000  1407.000000  1407.000000   \n",
-                            "\n",
-                            "                                   WALKING  accuracy    macro avg  \\\n",
-                            "          source model_type                                         \n",
-                            "f1-score  test   DecisionTree     0.729710  0.653696     0.642535   \n",
-                            "                 KNN              0.974257  0.791342     0.791185   \n",
-                            "                 LogReg           0.895874  0.769455     0.770530   \n",
-                            "                 RandomForest     0.984095  0.852140     0.852106   \n",
-                            "                 SVM              0.894995  0.766051     0.767197   \n",
-                            "precision test   DecisionTree     0.578512  0.653696     0.679958   \n",
-                            "                 KNN              0.957198  0.791342     0.799043   \n",
-                            "                 LogReg           0.873563  0.769455     0.771480   \n",
-                            "                 RandomForest     0.970588  0.852140     0.851631   \n",
-                            "                 SVM              0.871893  0.766051     0.768718   \n",
-                            "recall    test   DecisionTree     0.987903  0.653696     0.657108   \n",
-                            "                 KNN              0.991935  0.791342     0.793634   \n",
-                            "                 LogReg           0.919355  0.769455     0.771743   \n",
-                            "                 RandomForest     0.997984  0.852140     0.853051   \n",
-                            "                 SVM              0.919355  0.766051     0.768336   \n",
-                            "support   test   DecisionTree   496.000000  0.653696  2056.000000   \n",
-                            "                 KNN            496.000000  0.791342  2056.000000   \n",
-                            "                 LogReg         496.000000  0.769455  2056.000000   \n",
-                            "                 RandomForest   496.000000  0.852140  2056.000000   \n",
-                            "                 SVM            496.000000  0.766051  2056.000000   \n",
-                            "f1-score  train  DecisionTree     1.000000  1.000000     1.000000   \n",
-                            "                 KNN              0.988062  0.874911     0.874410   \n",
-                            "                 LogReg           0.948789  0.808102     0.806828   \n",
-                            "                 RandomForest     1.000000  1.000000     1.000000   \n",
-                            "                 SVM              0.953505  0.814144     0.813019   \n",
-                            "precision train  DecisionTree     1.000000  1.000000     1.000000   \n",
-                            "                 KNN              0.976405  0.874911     0.876944   \n",
-                            "                 LogReg           0.924477  0.808102     0.806169   \n",
-                            "                 RandomForest     1.000000  1.000000     1.000000   \n",
-                            "                 SVM              0.931525  0.814144     0.812283   \n",
-                            "recall    train  DecisionTree     1.000000  1.000000     1.000000   \n",
-                            "                 KNN              1.000000  0.874911     0.874911   \n",
-                            "                 LogReg           0.974414  0.808102     0.808102   \n",
-                            "                 RandomForest     1.000000  1.000000     1.000000   \n",
-                            "                 SVM              0.976546  0.814144     0.814144   \n",
-                            "support   train  DecisionTree  1407.000000  1.000000  5628.000000   \n",
-                            "                 KNN           1407.000000  0.874911  5628.000000   \n",
-                            "                 LogReg        1407.000000  0.808102  5628.000000   \n",
-                            "                 RandomForest  1407.000000  1.000000  5628.000000   \n",
-                            "                 SVM           1407.000000  0.814144  5628.000000   \n",
-                            "\n",
-                            "                               weighted avg  \n",
-                            "          source model_type                  \n",
-                            "f1-score  test   DecisionTree      0.642306  \n",
-                            "                 KNN               0.790227  \n",
-                            "                 LogReg            0.769222  \n",
-                            "                 RandomForest      0.851239  \n",
-                            "                 SVM               0.765972  \n",
-                            "precision test   DecisionTree      0.682790  \n",
-                            "                 KNN               0.799743  \n",
-                            "                 LogReg            0.771161  \n",
-                            "                 RandomForest      0.850813  \n",
-                            "                 SVM               0.768568  \n",
-                            "recall    test   DecisionTree      0.653696  \n",
-                            "                 KNN               0.791342  \n",
-                            "                 LogReg            0.769455  \n",
-                            "                 RandomForest      0.852140  \n",
-                            "                 SVM               0.766051  \n",
-                            "support   test   DecisionTree   2056.000000  \n",
-                            "                 KNN            2056.000000  \n",
-                            "                 LogReg         2056.000000  \n",
-                            "                 RandomForest   2056.000000  \n",
-                            "                 SVM            2056.000000  \n",
-                            "f1-score  train  DecisionTree      1.000000  \n",
-                            "                 KNN               0.874410  \n",
-                            "                 LogReg            0.806828  \n",
-                            "                 RandomForest      1.000000  \n",
-                            "                 SVM               0.813019  \n",
-                            "precision train  DecisionTree      1.000000  \n",
-                            "                 KNN               0.876944  \n",
-                            "                 LogReg            0.806169  \n",
-                            "                 RandomForest      1.000000  \n",
-                            "                 SVM               0.812283  \n",
-                            "recall    train  DecisionTree      1.000000  \n",
-                            "                 KNN               0.874911  \n",
-                            "                 LogReg            0.808102  \n",
-                            "                 RandomForest      1.000000  \n",
-                            "                 SVM               0.814144  \n",
-                            "support   train  DecisionTree   5628.000000  \n",
-                            "                 KNN            5628.000000  \n",
-                            "                 LogReg         5628.000000  \n",
-                            "                 RandomForest   5628.000000  \n",
-                            "                 SVM            5628.000000  "
-                        ]
-                    },
-                    "execution_count": 114,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "final_results.sort_index(level=1)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 115,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "final_results.sort_index(level=1).to_csv('Results_VIF_lower10.csv')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# 5.  Evaluation"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 116,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "#helper function\n",
-                "def make_confusion_matrix(y_test, X_test, the_clf):\n",
-                "    y_pred = the_clf.best_estimator_.predict(X_test)\n",
-                "    cf_matrix = confusion_matrix(y_test,y_pred)\n",
-                "    df_cf_matrix = pd.DataFrame(cf_matrix,columns=the_clf.best_estimator_.steps[1][1].classes_)\n",
-                "    df_cf_matrix.index = the_clf.best_estimator_.steps[1][1].classes_\n",
-                "    \n",
-                "    plt.figure(figsize=(28,7))\n",
-                "\n",
-                "    plt.subplot(1,3,1) # first heatmap\n",
-                "    heatmap1 = sns.heatmap(df_cf_matrix, annot=True,fmt='d');\n",
-                "    heatmap1.yaxis.set_ticklabels(heatmap1.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=10)\n",
-                "    heatmap1.xaxis.set_ticklabels(heatmap1.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=10)\n",
-                "    plt.ylabel('True label')\n",
-                "    plt.xlabel('Predicted label')\n",
-                "\n",
-                "    plt.subplot(1,3,2) # second heatmap\n",
-                "    heatmap2 = sns.heatmap(df_cf_matrix/np.sum(df_cf_matrix), annot=True, fmt='.2%')\n",
-                "    heatmap2.yaxis.set_ticklabels(heatmap2.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=10)\n",
-                "    heatmap2.xaxis.set_ticklabels(heatmap2.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=10)\n",
-                "    plt.ylabel('True label')\n",
-                "    plt.xlabel('Predicted label')\n",
-                "\n",
-                "    fig_name = str(the_clf.best_estimator_.steps[1][1].estimators_[0]) + '_confusionMatrix.png'\n",
-                "    plt.savefig(fig_name, dpi=150, format='png')\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Erzielte Genauigkeit"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 117,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>accuracy_train</th>\n",
-                            "      <th>accuracy_test</th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>diff_train_test</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>model_type</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>KNN</th>\n",
-                            "      <td>0.874911</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "      <td>KNN</td>\n",
-                            "      <td>0.083569</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>RandomForest</th>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "      <td>RandomForest</td>\n",
-                            "      <td>0.147860</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>0.808102</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>LogReg</td>\n",
-                            "      <td>0.038647</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>DecisionTree</th>\n",
-                            "      <td>1.000000</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "      <td>DecisionTree</td>\n",
-                            "      <td>0.346304</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>0.814144</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>SVM</td>\n",
-                            "      <td>0.048093</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "              accuracy_train  accuracy_test         model  diff_train_test\n",
-                            "model_type                                                                \n",
-                            "KNN                 0.874911       0.791342           KNN         0.083569\n",
-                            "RandomForest        1.000000       0.852140  RandomForest         0.147860\n",
-                            "LogReg              0.808102       0.769455        LogReg         0.038647\n",
-                            "DecisionTree        1.000000       0.653696  DecisionTree         0.346304\n",
-                            "SVM                 0.814144       0.766051           SVM         0.048093"
-                        ]
-                    },
-                    "execution_count": 117,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "accuracy_train_test = pd.DataFrame()\n",
-                "accuracy_train_test['accuracy_train'] = final_results.xs(\"train\", level=1).xs(\"precision\", level=0)['accuracy']\n",
-                "accuracy_train_test['accuracy_test'] = final_results.xs(\"test\", level=1).xs(\"precision\", level=0)['accuracy']\n",
-                "accuracy_train_test['model'] = accuracy_train_test.index\n",
-                "\n",
-                "accuracy_train_test['diff_train_test'] = accuracy_train_test['accuracy_train'] - accuracy_train_test['accuracy_test']\n",
-                "accuracy_train_test"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 118,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 720x504 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "plt.figure(figsize=(10,7))\n",
-                "df_temp = accuracy_train_test.melt('model', var_name='cols',  value_name='vals')\n",
-                "ax = sns.barplot(x=\"model\", y=\"vals\", hue=\"cols\", data=df_temp)\n",
-                "#for index, row in df_temp:\n",
-                "#    ax.text(row.name,row.vals, round(row.vals,2), color='black', ha=\"center\")\n",
-                "for p in ax.patches:\n",
-                "    ax.annotate(format(p.get_height(), '.3f'), \n",
-                "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
-                "                   ha = 'center', va = 'center', \n",
-                "                   xytext = (0, 9), \n",
-                "                   textcoords = 'offset points')\n",
-                "plt.ylabel('Accuracy')\n",
-                "plt.xlabel('Model')\n",
-                "\n",
-                "fig_name = 'Accuracy_performance.png'\n",
-                "plt.savefig(fig_name, dpi=150, format='png')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 119,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>model</th>\n",
-                            "      <th>cols</th>\n",
-                            "      <th>vals</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>KNN</td>\n",
-                            "      <td>accuracy_train</td>\n",
-                            "      <td>0.874911</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>RandomForest</td>\n",
-                            "      <td>accuracy_train</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>LogReg</td>\n",
-                            "      <td>accuracy_train</td>\n",
-                            "      <td>0.808102</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>DecisionTree</td>\n",
-                            "      <td>accuracy_train</td>\n",
-                            "      <td>1.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>SVM</td>\n",
-                            "      <td>accuracy_train</td>\n",
-                            "      <td>0.814144</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5</th>\n",
-                            "      <td>KNN</td>\n",
-                            "      <td>accuracy_test</td>\n",
-                            "      <td>0.791342</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>6</th>\n",
-                            "      <td>RandomForest</td>\n",
-                            "      <td>accuracy_test</td>\n",
-                            "      <td>0.852140</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>7</th>\n",
-                            "      <td>LogReg</td>\n",
-                            "      <td>accuracy_test</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>8</th>\n",
-                            "      <td>DecisionTree</td>\n",
-                            "      <td>accuracy_test</td>\n",
-                            "      <td>0.653696</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>9</th>\n",
-                            "      <td>SVM</td>\n",
-                            "      <td>accuracy_test</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>10</th>\n",
-                            "      <td>KNN</td>\n",
-                            "      <td>diff_train_test</td>\n",
-                            "      <td>0.083569</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>11</th>\n",
-                            "      <td>RandomForest</td>\n",
-                            "      <td>diff_train_test</td>\n",
-                            "      <td>0.147860</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>12</th>\n",
-                            "      <td>LogReg</td>\n",
-                            "      <td>diff_train_test</td>\n",
-                            "      <td>0.038647</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>13</th>\n",
-                            "      <td>DecisionTree</td>\n",
-                            "      <td>diff_train_test</td>\n",
-                            "      <td>0.346304</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>14</th>\n",
-                            "      <td>SVM</td>\n",
-                            "      <td>diff_train_test</td>\n",
-                            "      <td>0.048093</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "           model             cols      vals\n",
-                            "0            KNN   accuracy_train  0.874911\n",
-                            "1   RandomForest   accuracy_train  1.000000\n",
-                            "2         LogReg   accuracy_train  0.808102\n",
-                            "3   DecisionTree   accuracy_train  1.000000\n",
-                            "4            SVM   accuracy_train  0.814144\n",
-                            "5            KNN    accuracy_test  0.791342\n",
-                            "6   RandomForest    accuracy_test  0.852140\n",
-                            "7         LogReg    accuracy_test  0.769455\n",
-                            "8   DecisionTree    accuracy_test  0.653696\n",
-                            "9            SVM    accuracy_test  0.766051\n",
-                            "10           KNN  diff_train_test  0.083569\n",
-                            "11  RandomForest  diff_train_test  0.147860\n",
-                            "12        LogReg  diff_train_test  0.038647\n",
-                            "13  DecisionTree  diff_train_test  0.346304\n",
-                            "14           SVM  diff_train_test  0.048093"
-                        ]
-                    },
-                    "execution_count": 119,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "df_temp"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "* **KNN** --> Die Leistung auf Testdaten ist ca. 8% niedriger als auf Trainingsdaten. Wahrscheinlich passt sich das Modell zu sehr an die Trainingsdaten an und l\u00e4sst sich nicht gut auf ungesehene Testdaten verallgemeinern.\n",
-                "* **Random Forest** --> Die Leistung bei den Testdaten ist 15% geringer als bei den Trainingsdaten. Das Modell **\u00fcbererf\u00fcllt** die Trainingsdaten und verallgemeinert sich nicht gut auf die ungesehenen Testdaten.\n",
-                "* **Logistische Regression** --> Trainings- und Testleistung sind sehr \u00e4hnlich (diff=3.9%). Dies bedeutet wahrscheinlich, dass das erstellte Modell **gut** auf neue Daten verallgemeinert.\n",
-                "* **Entscheidungsbaum** --> Die Leistung bei den Testdaten ist 34% niedriger als bei den Trainingsdaten. Das Modell passt sich zu sehr an die Trainingsdaten an und verallgemeinert **nicht gut** auf die unbekannten Testdaten.\n",
-                "* **Support Vector Machine** --> Trainings- und Testleistung sind sehr \u00e4hnlich (diff=4.8%). Das bedeutet wahrscheinlich, dass das erstellte Modell **gut** auf neue Daten verallgemeinert.\n",
-                "-->\u00dcberpr\u00fcfen wir die Konfusionsmatrix der beiden besten Modelle"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Konfusionsmatrix-Logistische Regression"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 120,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 2016x504 with 4 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "make_confusion_matrix(y_test,X_test, clf3)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Konfusionsmatrix-SVM"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 121,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 2016x504 with 4 Axes>"
-                        ]
-                    },
-                    "metadata": {
-                        "needs_background": "light"
-                    },
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "make_confusion_matrix(y_test,X_test, clf5)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 122,
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th>LAYING</th>\n",
-                            "      <th>SITTING</th>\n",
-                            "      <th>STANDING</th>\n",
-                            "      <th>WALKING</th>\n",
-                            "      <th>accuracy</th>\n",
-                            "      <th>macro avg</th>\n",
-                            "      <th>weighted avg</th>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th></th>\n",
-                            "      <th>source</th>\n",
-                            "      <th>model_type</th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "      <th></th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>0.690702</td>\n",
-                            "      <td>0.687970</td>\n",
-                            "      <td>0.833684</td>\n",
-                            "      <td>0.873563</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>0.771480</td>\n",
-                            "      <td>0.771161</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>0.677840</td>\n",
-                            "      <td>0.745418</td>\n",
-                            "      <td>0.744361</td>\n",
-                            "      <td>0.919355</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>0.771743</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>0.684211</td>\n",
-                            "      <td>0.715543</td>\n",
-                            "      <td>0.786495</td>\n",
-                            "      <td>0.895874</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>0.770530</td>\n",
-                            "      <td>0.769222</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>support</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>LogReg</th>\n",
-                            "      <td>537.000000</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "      <td>0.769455</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>precision</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>0.689394</td>\n",
-                            "      <td>0.675978</td>\n",
-                            "      <td>0.837607</td>\n",
-                            "      <td>0.871893</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>0.768718</td>\n",
-                            "      <td>0.768568</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>recall</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>0.677840</td>\n",
-                            "      <td>0.739308</td>\n",
-                            "      <td>0.736842</td>\n",
-                            "      <td>0.919355</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>0.768336</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>f1-score</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>0.683568</td>\n",
-                            "      <td>0.706226</td>\n",
-                            "      <td>0.784000</td>\n",
-                            "      <td>0.894995</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>0.767197</td>\n",
-                            "      <td>0.765972</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>support</th>\n",
-                            "      <th>test</th>\n",
-                            "      <th>SVM</th>\n",
-                            "      <td>537.000000</td>\n",
-                            "      <td>491.000000</td>\n",
-                            "      <td>532.000000</td>\n",
-                            "      <td>496.000000</td>\n",
-                            "      <td>0.766051</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "      <td>2056.000000</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                                 LAYING     SITTING    STANDING     WALKING  \\\n",
-                            "          source model_type                                                   \n",
-                            "precision test   LogReg        0.690702    0.687970    0.833684    0.873563   \n",
-                            "recall    test   LogReg        0.677840    0.745418    0.744361    0.919355   \n",
-                            "f1-score  test   LogReg        0.684211    0.715543    0.786495    0.895874   \n",
-                            "support   test   LogReg      537.000000  491.000000  532.000000  496.000000   \n",
-                            "precision test   SVM           0.689394    0.675978    0.837607    0.871893   \n",
-                            "recall    test   SVM           0.677840    0.739308    0.736842    0.919355   \n",
-                            "f1-score  test   SVM           0.683568    0.706226    0.784000    0.894995   \n",
-                            "support   test   SVM         537.000000  491.000000  532.000000  496.000000   \n",
-                            "\n",
-                            "                             accuracy    macro avg  weighted avg  \n",
-                            "          source model_type                                       \n",
-                            "precision test   LogReg      0.769455     0.771480      0.771161  \n",
-                            "recall    test   LogReg      0.769455     0.771743      0.769455  \n",
-                            "f1-score  test   LogReg      0.769455     0.770530      0.769222  \n",
-                            "support   test   LogReg      0.769455  2056.000000   2056.000000  \n",
-                            "precision test   SVM         0.766051     0.768718      0.768568  \n",
-                            "recall    test   SVM         0.766051     0.768336      0.766051  \n",
-                            "f1-score  test   SVM         0.766051     0.767197      0.765972  \n",
-                            "support   test   SVM         0.766051  2056.000000   2056.000000  "
-                        ]
-                    },
-                    "execution_count": 122,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "final_results.loc[(slice(None), 'test', ('LogReg','SVM')), :].to_csv('LogReg_SVM_results.csv')\n",
-                "final_results.loc[(slice(None), 'test', ('LogReg','SVM')), :]"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Wichtigkeit der Merkmale\n",
-                "\n",
-                "* \n",
-                "Ein One vs Rest Classifier besteht aus n einzelnen Klassifikatoren (mit n=Anzahl der Klassen). Das macht es schwierig, die Merkmalsbedeutung f\u00fcr den gesamten OvsR-Klassifikator zu interpretieren. Eine M\u00f6glichkeit besteht darin, die Koeffizienten f\u00fcr jedes einzelne Modell zu bestimmen und den Mittelwert \u00fcber jeden Koeffizienten zu bilden.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 123,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "def get_the_feature_importances(clfobject,est_name, functype):\n",
-                "    feat_impts = []\n",
-                "    #estimator have feature_importances_ attribute\n",
-                "    if functype ==1:\n",
-                "        for esti in clfobject.best_estimator_.named_steps[est_name].estimators_:\n",
-                "            feat_impts.append(np.abs(esti.feature_importances_))\n",
-                "    #estimator have coef_ attribute\n",
-                "    if functype ==2:\n",
-                "        for esti in clfobject.best_estimator_.named_steps[est_name].estimators_:\n",
-                "            feat_impts.append(np.abs(esti.coef_[0]))\n",
-                "        \n",
-                "    return np.mean(feat_impts, axis=0)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 124,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "feature_importance = pd.DataFrame({},[])\n",
-                "feature_importance['Features'] = X_test.columns\n",
-                "feature_importance['Importance'] = get_the_feature_importances(clf3,'logreg',2)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Ergebnisse"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "* Das beste Modell ist der OvsR-Klassifikator mit logistischer Regression. \n",
-                "* Es hat eine gute Genauigkeit (0.77:test, 0.81:train) und eine gute Pr\u00e4zision (0.87:test) f\u00fcr die Klasse 'WALKING'.\n",
-                "* Die 10 wichtigsten Eigenschaften dieses OvsR-Klassifikators sind die folgenden:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 125,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>Features</th>\n",
-                            "      <th>Importance</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>15</th>\n",
-                            "      <td>tBodyGyro-entropy()-X</td>\n",
-                            "      <td>1.170518</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>16</th>\n",
-                            "      <td>tBodyGyro-entropy()-Y</td>\n",
-                            "      <td>1.070693</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>tBodyAcc-correlation()-Y,Z</td>\n",
-                            "      <td>0.791616</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>17</th>\n",
-                            "      <td>tBodyGyro-entropy()-Z</td>\n",
-                            "      <td>0.790347</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>14</th>\n",
-                            "      <td>tBodyGyro-mean()-Y</td>\n",
-                            "      <td>0.643740</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>32</th>\n",
-                            "      <td>fBodyGyro-meanFreq()-X</td>\n",
-                            "      <td>0.537990</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>29</th>\n",
-                            "      <td>fBodyAccJerk-maxInds-X</td>\n",
-                            "      <td>0.510996</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>18</th>\n",
-                            "      <td>tBodyGyro-correlation()-X,Y</td>\n",
-                            "      <td>0.501925</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>13</th>\n",
-                            "      <td>tBodyGyro-mean()-X</td>\n",
-                            "      <td>0.490046</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>19</th>\n",
-                            "      <td>tBodyGyro-correlation()-X,Z</td>\n",
-                            "      <td>0.468363</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "                       Features  Importance\n",
-                            "15        tBodyGyro-entropy()-X    1.170518\n",
-                            "16        tBodyGyro-entropy()-Y    1.070693\n",
-                            "3    tBodyAcc-correlation()-Y,Z    0.791616\n",
-                            "17        tBodyGyro-entropy()-Z    0.790347\n",
-                            "14           tBodyGyro-mean()-Y    0.643740\n",
-                            "32       fBodyGyro-meanFreq()-X    0.537990\n",
-                            "29       fBodyAccJerk-maxInds-X    0.510996\n",
-                            "18  tBodyGyro-correlation()-X,Y    0.501925\n",
-                            "13           tBodyGyro-mean()-X    0.490046\n",
-                            "19  tBodyGyro-correlation()-X,Z    0.468363"
-                        ]
-                    },
-                    "execution_count": 125,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "feature_importance.sort_values('Importance',ascending=0).head(10).to_csv('FeatureImportanceTop10.csv')\n",
-                "feature_importance.sort_values('Importance',ascending=0).to_csv('FeatureImportanceAll.csv')\n",
-                "feature_importance.sort_values('Importance',ascending=0).head(10)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "-->Die meisten dieser wichtigen Funktionen stammen vom Gyroskopsensor im Smartphone."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Endg\u00fcltiges Modell f\u00fcr das Deployment"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 126,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "final_model = clf3.best_estimator_"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# 6. Deployment\n",
-                "Teste die Prognose lokal"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 127,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>tBodyAcc-mean()-Y</th>\n",
-                            "      <th>tBodyAcc-correlation()-X,Y</th>\n",
-                            "      <th>tBodyAcc-correlation()-X,Z</th>\n",
-                            "      <th>tBodyAcc-correlation()-Y,Z</th>\n",
-                            "      <th>tGravityAcc-correlation()-X,Y</th>\n",
-                            "      <th>tGravityAcc-correlation()-X,Z</th>\n",
-                            "      <th>tGravityAcc-correlation()-Y,Z</th>\n",
-                            "      <th>tBodyAccJerk-mean()-X</th>\n",
-                            "      <th>tBodyAccJerk-mean()-Y</th>\n",
-                            "      <th>tBodyAccJerk-mean()-Z</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>fBodyAccJerk-maxInds-X</th>\n",
-                            "      <th>fBodyAccJerk-maxInds-Y</th>\n",
-                            "      <th>fBodyAccJerk-maxInds-Z</th>\n",
-                            "      <th>fBodyGyro-meanFreq()-X</th>\n",
-                            "      <th>fBodyGyro-meanFreq()-Y</th>\n",
-                            "      <th>fBodyGyro-meanFreq()-Z</th>\n",
-                            "      <th>angle(tBodyAccMean,gravity)</th>\n",
-                            "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
-                            "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
-                            "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>5293</th>\n",
-                            "      <td>-0.023285</td>\n",
-                            "      <td>0.076989</td>\n",
-                            "      <td>-0.490546</td>\n",
-                            "      <td>-0.709003</td>\n",
-                            "      <td>0.980580</td>\n",
-                            "      <td>-0.996352</td>\n",
-                            "      <td>-0.960117</td>\n",
-                            "      <td>0.072046</td>\n",
-                            "      <td>0.045754</td>\n",
-                            "      <td>-0.106043</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>-0.52</td>\n",
-                            "      <td>0.08</td>\n",
-                            "      <td>0.32</td>\n",
-                            "      <td>0.184035</td>\n",
-                            "      <td>-0.059323</td>\n",
-                            "      <td>0.438107</td>\n",
-                            "      <td>0.006462</td>\n",
-                            "      <td>0.162920</td>\n",
-                            "      <td>-0.825886</td>\n",
-                            "      <td>0.271151</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5294</th>\n",
-                            "      <td>-0.013163</td>\n",
-                            "      <td>-0.104983</td>\n",
-                            "      <td>-0.429134</td>\n",
-                            "      <td>0.399177</td>\n",
-                            "      <td>0.945233</td>\n",
-                            "      <td>-0.911415</td>\n",
-                            "      <td>-0.738535</td>\n",
-                            "      <td>0.070181</td>\n",
-                            "      <td>-0.017876</td>\n",
-                            "      <td>-0.001721</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>-0.16</td>\n",
-                            "      <td>-0.32</td>\n",
-                            "      <td>-0.40</td>\n",
-                            "      <td>0.018109</td>\n",
-                            "      <td>-0.227266</td>\n",
-                            "      <td>-0.151698</td>\n",
-                            "      <td>-0.083495</td>\n",
-                            "      <td>0.017500</td>\n",
-                            "      <td>-0.434375</td>\n",
-                            "      <td>0.920593</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5295</th>\n",
-                            "      <td>-0.026050</td>\n",
-                            "      <td>0.305653</td>\n",
-                            "      <td>-0.323848</td>\n",
-                            "      <td>0.279786</td>\n",
-                            "      <td>0.548432</td>\n",
-                            "      <td>-0.334864</td>\n",
-                            "      <td>0.590302</td>\n",
-                            "      <td>0.069368</td>\n",
-                            "      <td>-0.004908</td>\n",
-                            "      <td>-0.013673</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>-0.64</td>\n",
-                            "      <td>-0.40</td>\n",
-                            "      <td>-0.44</td>\n",
-                            "      <td>-0.479145</td>\n",
-                            "      <td>-0.210084</td>\n",
-                            "      <td>0.049310</td>\n",
-                            "      <td>-0.034956</td>\n",
-                            "      <td>0.202302</td>\n",
-                            "      <td>0.064103</td>\n",
-                            "      <td>0.145068</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>5296</th>\n",
-                            "      <td>-0.032614</td>\n",
-                            "      <td>-0.063792</td>\n",
-                            "      <td>-0.167111</td>\n",
-                            "      <td>0.544916</td>\n",
-                            "      <td>0.985534</td>\n",
-                            "      <td>0.653169</td>\n",
-                            "      <td>0.746518</td>\n",
-                            "      <td>0.074853</td>\n",
-                            "      <td>0.032274</td>\n",
-                            "      <td>0.012141</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>-0.44</td>\n",
-                            "      <td>-0.56</td>\n",
-                            "      <td>-0.48</td>\n",
-                            "      <td>-0.496954</td>\n",
-                            "      <td>-0.499906</td>\n",
-                            "      <td>-0.258896</td>\n",
-                            "      <td>-0.017067</td>\n",
-                            "      <td>0.154438</td>\n",
-                            "      <td>0.340134</td>\n",
-                            "      <td>0.296407</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>4 rows \u00d7 39 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "      tBodyAcc-mean()-Y  tBodyAcc-correlation()-X,Y  \\\n",
-                            "5293          -0.023285                    0.076989   \n",
-                            "5294          -0.013163                   -0.104983   \n",
-                            "5295          -0.026050                    0.305653   \n",
-                            "5296          -0.032614                   -0.063792   \n",
-                            "\n",
-                            "      tBodyAcc-correlation()-X,Z  tBodyAcc-correlation()-Y,Z  \\\n",
-                            "5293                   -0.490546                   -0.709003   \n",
-                            "5294                   -0.429134                    0.399177   \n",
-                            "5295                   -0.323848                    0.279786   \n",
-                            "5296                   -0.167111                    0.544916   \n",
-                            "\n",
-                            "      tGravityAcc-correlation()-X,Y  tGravityAcc-correlation()-X,Z  \\\n",
-                            "5293                       0.980580                      -0.996352   \n",
-                            "5294                       0.945233                      -0.911415   \n",
-                            "5295                       0.548432                      -0.334864   \n",
-                            "5296                       0.985534                       0.653169   \n",
-                            "\n",
-                            "      tGravityAcc-correlation()-Y,Z  tBodyAccJerk-mean()-X  \\\n",
-                            "5293                      -0.960117               0.072046   \n",
-                            "5294                      -0.738535               0.070181   \n",
-                            "5295                       0.590302               0.069368   \n",
-                            "5296                       0.746518               0.074853   \n",
-                            "\n",
-                            "      tBodyAccJerk-mean()-Y  tBodyAccJerk-mean()-Z  ...  \\\n",
-                            "5293               0.045754              -0.106043  ...   \n",
-                            "5294              -0.017876              -0.001721  ...   \n",
-                            "5295              -0.004908              -0.013673  ...   \n",
-                            "5296               0.032274               0.012141  ...   \n",
-                            "\n",
-                            "      fBodyAccJerk-maxInds-X  fBodyAccJerk-maxInds-Y  fBodyAccJerk-maxInds-Z  \\\n",
-                            "5293                   -0.52                    0.08                    0.32   \n",
-                            "5294                   -0.16                   -0.32                   -0.40   \n",
-                            "5295                   -0.64                   -0.40                   -0.44   \n",
-                            "5296                   -0.44                   -0.56                   -0.48   \n",
-                            "\n",
-                            "      fBodyGyro-meanFreq()-X  fBodyGyro-meanFreq()-Y  fBodyGyro-meanFreq()-Z  \\\n",
-                            "5293                0.184035               -0.059323                0.438107   \n",
-                            "5294                0.018109               -0.227266               -0.151698   \n",
-                            "5295               -0.479145               -0.210084                0.049310   \n",
-                            "5296               -0.496954               -0.499906               -0.258896   \n",
-                            "\n",
-                            "      angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
-                            "5293                     0.006462                              0.162920   \n",
-                            "5294                    -0.083495                              0.017500   \n",
-                            "5295                    -0.034956                              0.202302   \n",
-                            "5296                    -0.017067                              0.154438   \n",
-                            "\n",
-                            "      angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \n",
-                            "5293                         -0.825886                              0.271151  \n",
-                            "5294                         -0.434375                              0.920593  \n",
-                            "5295                          0.064103                              0.145068  \n",
-                            "5296                          0.340134                              0.296407  \n",
-                            "\n",
-                            "[4 rows x 39 columns]"
-                        ]
-                    },
-                    "execution_count": 127,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# recap: print first rows of predictors (here: training data without predicted column \"Survived\")\n",
-                "X_test.head(4)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 128,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "5293    STANDING\n",
-                            "5294    STANDING\n",
-                            "5295    STANDING\n",
-                            "5296    STANDING\n",
-                            "Name: Activity, dtype: object"
-                        ]
-                    },
-                    "execution_count": 128,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# recap: print first rows of training data\n",
-                "data_test['Activity'].head(4)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 129,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "array(['LAYING', 'STANDING', 'STANDING', 'STANDING'], dtype='<U8')"
-                        ]
-                    },
-                    "execution_count": 129,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "final_model.predict(X_test.head(4))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "-->final_model works and can be deplyoed"
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-    "metadata": {
-        "interpreter": {
-            "hash": "aab7ff84f4433dd8b68de441cd3c658d57659112bcb62d3bd6aa325045009f13"
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-            "pygments_lexer": "ipython3",
-            "version": "3.7.4"
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-        "title": "Analysis of the movement and activity of free-ranging cattle",
-        "category": "Agriculture"
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+      },
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{\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('cbd10e54-a31d-4eaa-b7df-8736d9d2a85f');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### Interactive 3D-plot with plotly\n",
+    "\n",
+    "# representations require a lot of computing power**\n",
+    "\n",
+    "si = np.ones(7349)-0.7\n",
+    "fig = px.scatter_3d(data_visualisation_transformed, \n",
+    "                    x=data_visualisation_transformed[:, 0], \n",
+    "                    y=data_visualisation_transformed[:, 1], \n",
+    "                    z=data_visualisation_transformed[:, 2],\n",
+    "                    color=df['Activity'],\n",
+    "                    #size=si\n",
+    "                    )\n",
+    "fig.update_traces(marker=dict(size=2.5,line=dict(width=0.05,color='azure')),selector=dict(mode='markers'))\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Ergebnisse Visualisierung mit PCA\n",
+    "\n",
+    "* Nach der PCA/Transformation in 3 Hauptkomponenten kÃ¶nnen wir die 3 Klassen in angemessener Weise visuell trennen. \n",
+    "* Allerdings beschreiben die 3 Hauptkomponenten nur 62% der Varianz der ursprÃ¼nglichen Daten. Das bedeutet, dass wir einen relativ groÃŸen Informationsverlust haben. \n",
+    "* Ein zweites Problem ist, dass es schwierig ist, Modelle zu interpretieren, die auf dem Ergebnis einer PCA basieren\n",
+    "\n",
+    "-->wir verwenden die PCA nur zur Visualisierung und nicht zur Modellierung"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feature Ãœbersicht"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aus dem Originalpapier Ã¼ber die Daten/Quelle der Daten kÃ¶nnen wir die Information erhalten, dass es die folgenden 17 Hauptmerkmale im Zeit- und Frequenzbereich des Signals gibt:"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "|Name|Time|Freq.|\n",
+    "| --- | --- | --- |\n",
+    "Body Acc |1| 1\n",
+    "Gravity Acc |1| 0\n",
+    "Body Acc Jerk |1 |1\n",
+    "Body Angular Speed |1 |1\n",
+    "Body Angular Acc |1 |0\n",
+    "Body Acc Magnitude |1 |1\n",
+    "Gravity Acc Mag |1 |0\n",
+    "Body Acc Jerk Mag |1 |1\n",
+    "Body Angular Speed Mag |1 |1\n",
+    "Body Angular Acc Mag |1 |1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_temp = df.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features: 561\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Features: ' + str(data_temp.shape[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7349 entries, 0 to 7348\n",
+      "Columns: 561 entries, tBodyAcc-mean()-X to angle(Z,gravityMean)\n",
+      "dtypes: float64(561)\n",
+      "memory usage: 31.5 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_temp.info()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### MultikollinearitÃ¤t prÃ¼fen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variables = data_temp\n",
+    "vif = pd.DataFrame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\eebal\\Anaconda3\\lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:193: RuntimeWarning:\n",
+      "\n",
+      "divide by zero encountered in double_scalars\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "556  out of  560\n",
+      "557  out of  560\n",
+      "558  out of  560\n",
+      "559  out of  560\n",
+      "560  out of  560\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This takes many minutes to compute \n",
+    "# This is very cpu intensive\n",
+    "\n",
+    "tempList = list()\n",
+    "total = variables.shape[1]\n",
+    "for i in range(total):\n",
+    "    print(i, ' out of ', total-1)\n",
+    "    x = variance_inflation_factor(variables.values, i)\n",
+    "    tempList.append(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add name of features to vif dataframe\n",
+    "vif[\"Features\"] = variables.columns\n",
+    "\n",
+    "# add the computed VIF Values to the vif dataframe\n",
+    "vif[\"VIF\"] = tempList"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VIF</th>\n",
+       "      <th>Features</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.673655e+02</td>\n",
+       "      <td>tBodyAcc-mean()-X</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.458447e+01</td>\n",
+       "      <td>tBodyAcc-mean()-Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.000913e+06</td>\n",
+       "      <td>tBodyAcc-std()-X</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.503812e+05</td>\n",
+       "      <td>tBodyAcc-std()-Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5.281580e+05</td>\n",
+       "      <td>tBodyAcc-std()-Z</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>552</th>\n",
+       "      <td>7.882126e+01</td>\n",
+       "      <td>fBodyBodyGyroJerkMag-skewness()</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>553</th>\n",
+       "      <td>1.882061e+02</td>\n",
+       "      <td>fBodyBodyGyroJerkMag-kurtosis()</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>1.310881e+03</td>\n",
+       "      <td>angle(X,gravityMean)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>3.877562e+02</td>\n",
+       "      <td>angle(Y,gravityMean)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>560</th>\n",
+       "      <td>2.950325e+02</td>\n",
+       "      <td>angle(Z,gravityMean)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>522 rows Ã— 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              VIF                         Features\n",
+       "0    1.673655e+02                tBodyAcc-mean()-X\n",
+       "2    2.458447e+01                tBodyAcc-mean()-Z\n",
+       "3    2.000913e+06                 tBodyAcc-std()-X\n",
+       "4    5.503812e+05                 tBodyAcc-std()-Y\n",
+       "5    5.281580e+05                 tBodyAcc-std()-Z\n",
+       "..            ...                              ...\n",
+       "552  7.882126e+01  fBodyBodyGyroJerkMag-skewness()\n",
+       "553  1.882061e+02  fBodyBodyGyroJerkMag-kurtosis()\n",
+       "558  1.310881e+03             angle(X,gravityMean)\n",
+       "559  3.877562e+02             angle(Y,gravityMean)\n",
+       "560  2.950325e+02             angle(Z,gravityMean)\n",
+       "\n",
+       "[522 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vif[vif['VIF']>10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop all features with a VIF > 10, as they are correlating to much with other features\n",
+    "features_to_drop = vif[vif['VIF']>10].Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_with_important_features = data_temp.drop(features_to_drop, axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tBodyAcc-mean()-Y</th>\n",
+       "      <th>tBodyAcc-correlation()-X,Y</th>\n",
+       "      <th>tBodyAcc-correlation()-X,Z</th>\n",
+       "      <th>tBodyAcc-correlation()-Y,Z</th>\n",
+       "      <th>tGravityAcc-correlation()-X,Y</th>\n",
+       "      <th>tGravityAcc-correlation()-X,Z</th>\n",
+       "      <th>tGravityAcc-correlation()-Y,Z</th>\n",
+       "      <th>tBodyAccJerk-mean()-X</th>\n",
+       "      <th>tBodyAccJerk-mean()-Y</th>\n",
+       "      <th>tBodyAccJerk-mean()-Z</th>\n",
+       "      <th>...</th>\n",
+       "      <th>fBodyAccJerk-maxInds-X</th>\n",
+       "      <th>fBodyAccJerk-maxInds-Y</th>\n",
+       "      <th>fBodyAccJerk-maxInds-Z</th>\n",
+       "      <th>fBodyGyro-meanFreq()-X</th>\n",
+       "      <th>fBodyGyro-meanFreq()-Y</th>\n",
+       "      <th>fBodyGyro-meanFreq()-Z</th>\n",
+       "      <th>angle(tBodyAccMean,gravity)</th>\n",
+       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
+       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
+       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.020294</td>\n",
+       "      <td>0.376314</td>\n",
+       "      <td>0.435129</td>\n",
+       "      <td>0.660790</td>\n",
+       "      <td>0.570222</td>\n",
+       "      <td>0.439027</td>\n",
+       "      <td>0.986913</td>\n",
+       "      <td>0.077996</td>\n",
+       "      <td>0.005001</td>\n",
+       "      <td>-0.067831</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>-0.24</td>\n",
+       "      <td>-1.00</td>\n",
+       "      <td>-0.257549</td>\n",
+       "      <td>0.097947</td>\n",
+       "      <td>0.547151</td>\n",
+       "      <td>-0.112754</td>\n",
+       "      <td>0.030400</td>\n",
+       "      <td>-0.464761</td>\n",
+       "      <td>-0.018446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.016411</td>\n",
+       "      <td>-0.013429</td>\n",
+       "      <td>-0.072692</td>\n",
+       "      <td>0.579382</td>\n",
+       "      <td>-0.831284</td>\n",
+       "      <td>-0.865711</td>\n",
+       "      <td>0.974386</td>\n",
+       "      <td>0.074007</td>\n",
+       "      <td>0.005771</td>\n",
+       "      <td>0.029377</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.32</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>-0.32</td>\n",
+       "      <td>-0.048167</td>\n",
+       "      <td>-0.401608</td>\n",
+       "      <td>-0.068178</td>\n",
+       "      <td>0.053477</td>\n",
+       "      <td>-0.007435</td>\n",
+       "      <td>-0.732626</td>\n",
+       "      <td>0.703511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.019467</td>\n",
+       "      <td>-0.124698</td>\n",
+       "      <td>-0.181105</td>\n",
+       "      <td>0.608900</td>\n",
+       "      <td>-0.181090</td>\n",
+       "      <td>0.337936</td>\n",
+       "      <td>0.643417</td>\n",
+       "      <td>0.073636</td>\n",
+       "      <td>0.003104</td>\n",
+       "      <td>-0.009046</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.16</td>\n",
+       "      <td>-0.48</td>\n",
+       "      <td>-0.28</td>\n",
+       "      <td>-0.216685</td>\n",
+       "      <td>-0.017264</td>\n",
+       "      <td>-0.110720</td>\n",
+       "      <td>-0.118559</td>\n",
+       "      <td>0.177899</td>\n",
+       "      <td>0.100699</td>\n",
+       "      <td>0.808529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.026201</td>\n",
+       "      <td>-0.305693</td>\n",
+       "      <td>-0.362654</td>\n",
+       "      <td>0.507459</td>\n",
+       "      <td>-0.991309</td>\n",
+       "      <td>-0.968821</td>\n",
+       "      <td>0.984256</td>\n",
+       "      <td>0.077321</td>\n",
+       "      <td>0.020058</td>\n",
+       "      <td>-0.009865</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.12</td>\n",
+       "      <td>-0.56</td>\n",
+       "      <td>-0.28</td>\n",
+       "      <td>0.216862</td>\n",
+       "      <td>-0.135245</td>\n",
+       "      <td>-0.049728</td>\n",
+       "      <td>-0.036788</td>\n",
+       "      <td>-0.012892</td>\n",
+       "      <td>0.640011</td>\n",
+       "      <td>-0.485366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.016570</td>\n",
+       "      <td>-0.155804</td>\n",
+       "      <td>-0.189763</td>\n",
+       "      <td>0.599213</td>\n",
+       "      <td>-0.408330</td>\n",
+       "      <td>-0.184840</td>\n",
+       "      <td>0.964797</td>\n",
+       "      <td>0.073444</td>\n",
+       "      <td>0.019122</td>\n",
+       "      <td>0.016780</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.32</td>\n",
+       "      <td>-0.08</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>-0.153343</td>\n",
+       "      <td>-0.088403</td>\n",
+       "      <td>-0.162230</td>\n",
+       "      <td>0.123320</td>\n",
+       "      <td>0.122542</td>\n",
+       "      <td>0.693578</td>\n",
+       "      <td>-0.615971</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   tBodyAcc-mean()-Y  tBodyAcc-correlation()-X,Y  tBodyAcc-correlation()-X,Z  \\\n",
+       "0          -0.020294                    0.376314                    0.435129   \n",
+       "1          -0.016411                   -0.013429                   -0.072692   \n",
+       "2          -0.019467                   -0.124698                   -0.181105   \n",
+       "3          -0.026201                   -0.305693                   -0.362654   \n",
+       "4          -0.016570                   -0.155804                   -0.189763   \n",
+       "\n",
+       "   tBodyAcc-correlation()-Y,Z  tGravityAcc-correlation()-X,Y  \\\n",
+       "0                    0.660790                       0.570222   \n",
+       "1                    0.579382                      -0.831284   \n",
+       "2                    0.608900                      -0.181090   \n",
+       "3                    0.507459                      -0.991309   \n",
+       "4                    0.599213                      -0.408330   \n",
+       "\n",
+       "   tGravityAcc-correlation()-X,Z  tGravityAcc-correlation()-Y,Z  \\\n",
+       "0                       0.439027                       0.986913   \n",
+       "1                      -0.865711                       0.974386   \n",
+       "2                       0.337936                       0.643417   \n",
+       "3                      -0.968821                       0.984256   \n",
+       "4                      -0.184840                       0.964797   \n",
+       "\n",
+       "   tBodyAccJerk-mean()-X  tBodyAccJerk-mean()-Y  tBodyAccJerk-mean()-Z  ...  \\\n",
+       "0               0.077996               0.005001              -0.067831  ...   \n",
+       "1               0.074007               0.005771               0.029377  ...   \n",
+       "2               0.073636               0.003104              -0.009046  ...   \n",
+       "3               0.077321               0.020058              -0.009865  ...   \n",
+       "4               0.073444               0.019122               0.016780  ...   \n",
+       "\n",
+       "   fBodyAccJerk-maxInds-X  fBodyAccJerk-maxInds-Y  fBodyAccJerk-maxInds-Z  \\\n",
+       "0                    1.00                   -0.24                   -1.00   \n",
+       "1                   -0.32                   -0.12                   -0.32   \n",
+       "2                   -0.16                   -0.48                   -0.28   \n",
+       "3                   -0.12                   -0.56                   -0.28   \n",
+       "4                   -0.32                   -0.08                    0.04   \n",
+       "\n",
+       "   fBodyGyro-meanFreq()-X  fBodyGyro-meanFreq()-Y  fBodyGyro-meanFreq()-Z  \\\n",
+       "0               -0.257549                0.097947                0.547151   \n",
+       "1               -0.048167               -0.401608               -0.068178   \n",
+       "2               -0.216685               -0.017264               -0.110720   \n",
+       "3                0.216862               -0.135245               -0.049728   \n",
+       "4               -0.153343               -0.088403               -0.162230   \n",
+       "\n",
+       "   angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
+       "0                    -0.112754                              0.030400   \n",
+       "1                     0.053477                             -0.007435   \n",
+       "2                    -0.118559                              0.177899   \n",
+       "3                    -0.036788                             -0.012892   \n",
+       "4                     0.123320                              0.122542   \n",
+       "\n",
+       "   angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \n",
+       "0                         -0.464761                             -0.018446  \n",
+       "1                         -0.732626                              0.703511  \n",
+       "2                          0.100699                              0.808529  \n",
+       "3                          0.640011                             -0.485366  \n",
+       "4                          0.693578                             -0.615971  \n",
+       "\n",
+       "[5 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_important_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>tBodyAcc-mean()-Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.016299</td>\n",
+       "      <td>0.038629</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.021403</td>\n",
+       "      <td>-0.017029</td>\n",
+       "      <td>-0.012643</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyAcc-correlation()-X,Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.024801</td>\n",
+       "      <td>0.360763</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.242409</td>\n",
+       "      <td>-0.058502</td>\n",
+       "      <td>0.172579</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyAcc-correlation()-X,Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.185987</td>\n",
+       "      <td>0.338450</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.395440</td>\n",
+       "      <td>-0.171765</td>\n",
+       "      <td>0.020009</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyAcc-correlation()-Y,Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.096760</td>\n",
+       "      <td>0.415218</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.187455</td>\n",
+       "      <td>0.138724</td>\n",
+       "      <td>0.405517</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tGravityAcc-correlation()-X,Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.103601</td>\n",
+       "      <td>0.735996</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.665946</td>\n",
+       "      <td>0.231863</td>\n",
+       "      <td>0.851334</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tGravityAcc-correlation()-X,Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.166366</td>\n",
+       "      <td>0.726580</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.879864</td>\n",
+       "      <td>-0.367198</td>\n",
+       "      <td>0.567089</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tGravityAcc-correlation()-Y,Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.075266</td>\n",
+       "      <td>0.735481</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.688232</td>\n",
+       "      <td>0.185269</td>\n",
+       "      <td>0.823623</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyAccJerk-mean()-X</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.077434</td>\n",
+       "      <td>0.108154</td>\n",
+       "      <td>-0.581496</td>\n",
+       "      <td>0.071259</td>\n",
+       "      <td>0.075963</td>\n",
+       "      <td>0.081112</td>\n",
+       "      <td>0.855403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyAccJerk-mean()-Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.009156</td>\n",
+       "      <td>0.124891</td>\n",
+       "      <td>-0.948714</td>\n",
+       "      <td>0.000186</td>\n",
+       "      <td>0.010928</td>\n",
+       "      <td>0.021387</td>\n",
+       "      <td>0.835172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyAccJerk-mean()-Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.003619</td>\n",
+       "      <td>0.113880</td>\n",
+       "      <td>-0.807273</td>\n",
+       "      <td>-0.015246</td>\n",
+       "      <td>-0.001097</td>\n",
+       "      <td>0.012727</td>\n",
+       "      <td>0.735045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyAccJerk-correlation()-X,Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.074377</td>\n",
+       "      <td>0.242017</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.228501</td>\n",
+       "      <td>-0.078196</td>\n",
+       "      <td>0.075621</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyAccJerk-correlation()-X,Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.039575</td>\n",
+       "      <td>0.284099</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.145351</td>\n",
+       "      <td>0.062285</td>\n",
+       "      <td>0.232011</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyAccJerk-correlation()-Y,Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.054372</td>\n",
+       "      <td>0.274488</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.127156</td>\n",
+       "      <td>0.042153</td>\n",
+       "      <td>0.222467</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyro-mean()-X</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.028776</td>\n",
+       "      <td>0.074857</td>\n",
+       "      <td>-0.757669</td>\n",
+       "      <td>-0.034259</td>\n",
+       "      <td>-0.027690</td>\n",
+       "      <td>-0.021722</td>\n",
+       "      <td>0.524123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyro-mean()-Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.075976</td>\n",
+       "      <td>0.086467</td>\n",
+       "      <td>-0.851973</td>\n",
+       "      <td>-0.089776</td>\n",
+       "      <td>-0.074841</td>\n",
+       "      <td>-0.062999</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyro-entropy()-X</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.238753</td>\n",
+       "      <td>0.401771</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.550684</td>\n",
+       "      <td>-0.290427</td>\n",
+       "      <td>0.102805</td>\n",
+       "      <td>0.944059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyro-entropy()-Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.205995</td>\n",
+       "      <td>0.364969</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.457364</td>\n",
+       "      <td>-0.209115</td>\n",
+       "      <td>0.067220</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyro-entropy()-Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.196045</td>\n",
+       "      <td>0.459841</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.563556</td>\n",
+       "      <td>-0.272572</td>\n",
+       "      <td>0.228766</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyro-correlation()-X,Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.188702</td>\n",
+       "      <td>0.405215</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.483125</td>\n",
+       "      <td>-0.208589</td>\n",
+       "      <td>0.084417</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyro-correlation()-X,Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.041870</td>\n",
+       "      <td>0.408883</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.220456</td>\n",
+       "      <td>0.018146</td>\n",
+       "      <td>0.302452</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyro-correlation()-Y,Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.168283</td>\n",
+       "      <td>0.440983</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.530728</td>\n",
+       "      <td>-0.177793</td>\n",
+       "      <td>0.150636</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyroJerk-mean()-X</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.098036</td>\n",
+       "      <td>0.086125</td>\n",
+       "      <td>-0.968976</td>\n",
+       "      <td>-0.104611</td>\n",
+       "      <td>-0.098272</td>\n",
+       "      <td>-0.092254</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyroJerk-mean()-Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.041384</td>\n",
+       "      <td>0.083025</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.047188</td>\n",
+       "      <td>-0.040452</td>\n",
+       "      <td>-0.033379</td>\n",
+       "      <td>0.720665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyroJerk-mean()-Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.055329</td>\n",
+       "      <td>0.087333</td>\n",
+       "      <td>-0.749480</td>\n",
+       "      <td>-0.064313</td>\n",
+       "      <td>-0.054572</td>\n",
+       "      <td>-0.047179</td>\n",
+       "      <td>0.600600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyroJerk-correlation()-X,Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.017183</td>\n",
+       "      <td>0.278593</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.169257</td>\n",
+       "      <td>0.013469</td>\n",
+       "      <td>0.198487</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyroJerk-correlation()-X,Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.063215</td>\n",
+       "      <td>0.266236</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.106320</td>\n",
+       "      <td>0.057007</td>\n",
+       "      <td>0.226712</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tBodyGyroJerk-correlation()-Y,Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.111144</td>\n",
+       "      <td>0.262790</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.279851</td>\n",
+       "      <td>-0.112190</td>\n",
+       "      <td>0.051061</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fBodyAcc-meanFreq()-Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.057152</td>\n",
+       "      <td>0.247243</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.099065</td>\n",
+       "      <td>0.053560</td>\n",
+       "      <td>0.228640</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fBodyAcc-meanFreq()-Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.104336</td>\n",
+       "      <td>0.265157</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.069322</td>\n",
+       "      <td>0.118173</td>\n",
+       "      <td>0.288123</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fBodyAccJerk-maxInds-X</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.294016</td>\n",
+       "      <td>0.275268</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.480000</td>\n",
+       "      <td>-0.280000</td>\n",
+       "      <td>-0.120000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fBodyAccJerk-maxInds-Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.354530</td>\n",
+       "      <td>0.273795</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.560000</td>\n",
+       "      <td>-0.360000</td>\n",
+       "      <td>-0.200000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fBodyAccJerk-maxInds-Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.261875</td>\n",
+       "      <td>0.274175</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.440000</td>\n",
+       "      <td>-0.280000</td>\n",
+       "      <td>-0.080000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fBodyGyro-meanFreq()-X</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.064359</td>\n",
+       "      <td>0.263692</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.229611</td>\n",
+       "      <td>-0.054360</td>\n",
+       "      <td>0.112420</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fBodyGyro-meanFreq()-Y</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.168671</td>\n",
+       "      <td>0.274139</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.357676</td>\n",
+       "      <td>-0.174484</td>\n",
+       "      <td>0.018202</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fBodyGyro-meanFreq()-Z</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.013731</td>\n",
+       "      <td>0.263507</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.191156</td>\n",
+       "      <td>-0.019524</td>\n",
+       "      <td>0.161677</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>angle(tBodyAccMean,gravity)</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.010961</td>\n",
+       "      <td>0.267908</td>\n",
+       "      <td>-0.977973</td>\n",
+       "      <td>-0.074345</td>\n",
+       "      <td>0.008115</td>\n",
+       "      <td>0.096642</td>\n",
+       "      <td>0.977810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.005884</td>\n",
+       "      <td>0.369485</td>\n",
+       "      <td>-0.979326</td>\n",
+       "      <td>-0.206153</td>\n",
+       "      <td>0.010343</td>\n",
+       "      <td>0.211965</td>\n",
+       "      <td>0.991899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>0.013123</td>\n",
+       "      <td>0.488843</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.344634</td>\n",
+       "      <td>0.010699</td>\n",
+       "      <td>0.381334</td>\n",
+       "      <td>0.994532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
+       "      <td>7349.0</td>\n",
+       "      <td>-0.006028</td>\n",
+       "      <td>0.460188</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>-0.359519</td>\n",
+       "      <td>0.001035</td>\n",
+       "      <td>0.335377</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       count      mean       std       min  \\\n",
+       "tBodyAcc-mean()-Y                     7349.0 -0.016299  0.038629 -1.000000   \n",
+       "tBodyAcc-correlation()-X,Y            7349.0 -0.024801  0.360763 -1.000000   \n",
+       "tBodyAcc-correlation()-X,Z            7349.0 -0.185987  0.338450 -1.000000   \n",
+       "tBodyAcc-correlation()-Y,Z            7349.0  0.096760  0.415218 -1.000000   \n",
+       "tGravityAcc-correlation()-X,Y         7349.0  0.103601  0.735996 -1.000000   \n",
+       "tGravityAcc-correlation()-X,Z         7349.0 -0.166366  0.726580 -1.000000   \n",
+       "tGravityAcc-correlation()-Y,Z         7349.0  0.075266  0.735481 -1.000000   \n",
+       "tBodyAccJerk-mean()-X                 7349.0  0.077434  0.108154 -0.581496   \n",
+       "tBodyAccJerk-mean()-Y                 7349.0  0.009156  0.124891 -0.948714   \n",
+       "tBodyAccJerk-mean()-Z                 7349.0 -0.003619  0.113880 -0.807273   \n",
+       "tBodyAccJerk-correlation()-X,Y        7349.0 -0.074377  0.242017 -1.000000   \n",
+       "tBodyAccJerk-correlation()-X,Z        7349.0  0.039575  0.284099 -1.000000   \n",
+       "tBodyAccJerk-correlation()-Y,Z        7349.0  0.054372  0.274488 -1.000000   \n",
+       "tBodyGyro-mean()-X                    7349.0 -0.028776  0.074857 -0.757669   \n",
+       "tBodyGyro-mean()-Y                    7349.0 -0.075976  0.086467 -0.851973   \n",
+       "tBodyGyro-entropy()-X                 7349.0 -0.238753  0.401771 -1.000000   \n",
+       "tBodyGyro-entropy()-Y                 7349.0 -0.205995  0.364969 -1.000000   \n",
+       "tBodyGyro-entropy()-Z                 7349.0 -0.196045  0.459841 -1.000000   \n",
+       "tBodyGyro-correlation()-X,Y           7349.0 -0.188702  0.405215 -1.000000   \n",
+       "tBodyGyro-correlation()-X,Z           7349.0  0.041870  0.408883 -1.000000   \n",
+       "tBodyGyro-correlation()-Y,Z           7349.0 -0.168283  0.440983 -1.000000   \n",
+       "tBodyGyroJerk-mean()-X                7349.0 -0.098036  0.086125 -0.968976   \n",
+       "tBodyGyroJerk-mean()-Y                7349.0 -0.041384  0.083025 -1.000000   \n",
+       "tBodyGyroJerk-mean()-Z                7349.0 -0.055329  0.087333 -0.749480   \n",
+       "tBodyGyroJerk-correlation()-X,Y       7349.0  0.017183  0.278593 -1.000000   \n",
+       "tBodyGyroJerk-correlation()-X,Z       7349.0  0.063215  0.266236 -1.000000   \n",
+       "tBodyGyroJerk-correlation()-Y,Z       7349.0 -0.111144  0.262790 -1.000000   \n",
+       "fBodyAcc-meanFreq()-Y                 7349.0  0.057152  0.247243 -1.000000   \n",
+       "fBodyAcc-meanFreq()-Z                 7349.0  0.104336  0.265157 -1.000000   \n",
+       "fBodyAccJerk-maxInds-X                7349.0 -0.294016  0.275268 -1.000000   \n",
+       "fBodyAccJerk-maxInds-Y                7349.0 -0.354530  0.273795 -1.000000   \n",
+       "fBodyAccJerk-maxInds-Z                7349.0 -0.261875  0.274175 -1.000000   \n",
+       "fBodyGyro-meanFreq()-X                7349.0 -0.064359  0.263692 -1.000000   \n",
+       "fBodyGyro-meanFreq()-Y                7349.0 -0.168671  0.274139 -1.000000   \n",
+       "fBodyGyro-meanFreq()-Z                7349.0 -0.013731  0.263507 -1.000000   \n",
+       "angle(tBodyAccMean,gravity)           7349.0  0.010961  0.267908 -0.977973   \n",
+       "angle(tBodyAccJerkMean),gravityMean)  7349.0  0.005884  0.369485 -0.979326   \n",
+       "angle(tBodyGyroMean,gravityMean)      7349.0  0.013123  0.488843 -1.000000   \n",
+       "angle(tBodyGyroJerkMean,gravityMean)  7349.0 -0.006028  0.460188 -1.000000   \n",
+       "\n",
+       "                                           25%       50%       75%       max  \n",
+       "tBodyAcc-mean()-Y                    -0.021403 -0.017029 -0.012643  1.000000  \n",
+       "tBodyAcc-correlation()-X,Y           -0.242409 -0.058502  0.172579  1.000000  \n",
+       "tBodyAcc-correlation()-X,Z           -0.395440 -0.171765  0.020009  1.000000  \n",
+       "tBodyAcc-correlation()-Y,Z           -0.187455  0.138724  0.405517  1.000000  \n",
+       "tGravityAcc-correlation()-X,Y        -0.665946  0.231863  0.851334  1.000000  \n",
+       "tGravityAcc-correlation()-X,Z        -0.879864 -0.367198  0.567089  1.000000  \n",
+       "tGravityAcc-correlation()-Y,Z        -0.688232  0.185269  0.823623  1.000000  \n",
+       "tBodyAccJerk-mean()-X                 0.071259  0.075963  0.081112  0.855403  \n",
+       "tBodyAccJerk-mean()-Y                 0.000186  0.010928  0.021387  0.835172  \n",
+       "tBodyAccJerk-mean()-Z                -0.015246 -0.001097  0.012727  0.735045  \n",
+       "tBodyAccJerk-correlation()-X,Y       -0.228501 -0.078196  0.075621  1.000000  \n",
+       "tBodyAccJerk-correlation()-X,Z       -0.145351  0.062285  0.232011  1.000000  \n",
+       "tBodyAccJerk-correlation()-Y,Z       -0.127156  0.042153  0.222467  1.000000  \n",
+       "tBodyGyro-mean()-X                   -0.034259 -0.027690 -0.021722  0.524123  \n",
+       "tBodyGyro-mean()-Y                   -0.089776 -0.074841 -0.062999  1.000000  \n",
+       "tBodyGyro-entropy()-X                -0.550684 -0.290427  0.102805  0.944059  \n",
+       "tBodyGyro-entropy()-Y                -0.457364 -0.209115  0.067220  1.000000  \n",
+       "tBodyGyro-entropy()-Z                -0.563556 -0.272572  0.228766  1.000000  \n",
+       "tBodyGyro-correlation()-X,Y          -0.483125 -0.208589  0.084417  1.000000  \n",
+       "tBodyGyro-correlation()-X,Z          -0.220456  0.018146  0.302452  1.000000  \n",
+       "tBodyGyro-correlation()-Y,Z          -0.530728 -0.177793  0.150636  1.000000  \n",
+       "tBodyGyroJerk-mean()-X               -0.104611 -0.098272 -0.092254  1.000000  \n",
+       "tBodyGyroJerk-mean()-Y               -0.047188 -0.040452 -0.033379  0.720665  \n",
+       "tBodyGyroJerk-mean()-Z               -0.064313 -0.054572 -0.047179  0.600600  \n",
+       "tBodyGyroJerk-correlation()-X,Y      -0.169257  0.013469  0.198487  1.000000  \n",
+       "tBodyGyroJerk-correlation()-X,Z      -0.106320  0.057007  0.226712  1.000000  \n",
+       "tBodyGyroJerk-correlation()-Y,Z      -0.279851 -0.112190  0.051061  1.000000  \n",
+       "fBodyAcc-meanFreq()-Y                -0.099065  0.053560  0.228640  1.000000  \n",
+       "fBodyAcc-meanFreq()-Z                -0.069322  0.118173  0.288123  1.000000  \n",
+       "fBodyAccJerk-maxInds-X               -0.480000 -0.280000 -0.120000  1.000000  \n",
+       "fBodyAccJerk-maxInds-Y               -0.560000 -0.360000 -0.200000  1.000000  \n",
+       "fBodyAccJerk-maxInds-Z               -0.440000 -0.280000 -0.080000  1.000000  \n",
+       "fBodyGyro-meanFreq()-X               -0.229611 -0.054360  0.112420  1.000000  \n",
+       "fBodyGyro-meanFreq()-Y               -0.357676 -0.174484  0.018202  1.000000  \n",
+       "fBodyGyro-meanFreq()-Z               -0.191156 -0.019524  0.161677  1.000000  \n",
+       "angle(tBodyAccMean,gravity)          -0.074345  0.008115  0.096642  0.977810  \n",
+       "angle(tBodyAccJerkMean),gravityMean) -0.206153  0.010343  0.211965  0.991899  \n",
+       "angle(tBodyGyroMean,gravityMean)     -0.344634  0.010699  0.381334  0.994532  \n",
+       "angle(tBodyGyroJerkMean,gravityMean) -0.359519  0.001035  0.335377  1.000000  "
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_with_important_features.describe(include='all').transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Feautures')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2016x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(28,15))\n",
+    "boxplot = sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(data_with_important_features.iloc[:,:19]))\n",
+    "boxplot.xaxis.set_ticklabels(boxplot.xaxis.get_ticklabels(), rotation=90, ha='right', fontsize=15)\n",
+    "plt.ylabel('values')\n",
+    "plt.xlabel('Feautures')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Feautures')"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2016x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(28,15))\n",
+    "boxplot = sns.boxplot(x=\"variable\", y=\"value\", data=pd.melt(data_with_important_features.iloc[:,19:]))\n",
+    "boxplot.xaxis.set_ticklabels(boxplot.xaxis.get_ticklabels(), rotation=90, ha='right', fontsize=15)\n",
+    "plt.ylabel('values')\n",
+    "plt.xlabel('Feautures')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Zusammenfassung des DatenverstÃ¤ndnisses\n",
+    "* 1 Spalte mit der Subjekt-ID 'subject'-->nur zur Filterung, muss entfernt werden\n",
+    "* 1 Zielspalte 'AktivitÃ¤t'-->4 Klassen (GEHEN, SITZEN, LIEGEN, STEHEN), etwas unausgewogen\n",
+    "* wir haben ein Klassifizierungsproblem mit mehreren Klassen ***Ã¼berwachtes maschinelles Lernen***\n",
+    "* 1 \"support column\" source-->is the observation from the train or test group-->train test split along this value\n",
+    "* 1 \"UnterstÃ¼tzungsspalte\"-Quelle-->ist die Beobachtung aus der Trainings- oder Testgruppe-->Train-Test-Aufteilung entlang dieses Wertes \n",
+    "* Diese wurden nach MÃ¶glichkeit fÃ¼r jede der drei Achsen x, y und z berechnet\n",
+    "* In der Summe sind das 561 Merkmale\n",
+    "-->Die Analyse der einzelnen Merkmale im Detail ist aufgrund der groÃŸen Anzahl von Merkmalen sehr schwierig. SchlieÃŸlich haben wir MultilineraritÃ¤t in den Daten\n",
+    "\n",
+    "* Nach der Multicollineraty-PrÃ¼fung sind nur noch 39 Merkmale Ã¼brig. In den Boxplots sind bei einigen Merkmalen AusreiÃŸer zu sehen, aber es ist schwierig, diese AusreiÃŸer zu analysieren und zu interpretieren, da die Daten anscheinend bereits teilweise standardisiert sind und wir keine Messeinheiten haben. Daher akzeptieren wir im Moment diese AusreiÃŸer\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trainings Test Split"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_train = data[data['type']=='train']\n",
+    "data_test = data[data['type']=='test']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = data_train.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)\n",
+    "X_train = X_train.drop(features_to_drop, axis =1)\n",
+    "y_train = data_train['Activity']\n",
+    "\n",
+    "X_test = data_test.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)\n",
+    "X_test = X_test.drop(features_to_drop, axis =1)\n",
+    "y_test = data_test['Activity']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_train: (5293, 39)\n",
+      "y_train: (5293,)\n",
+      "X_test: (2056, 39)\n",
+      "y_test: (2056,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('X_train: ' + str(X_train.shape))\n",
+    "print('y_train: ' + str(y_train.shape))\n",
+    "print('X_test: ' + str(X_test.shape))\n",
+    "print('y_test: ' + str(y_test.shape))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Oversampling von Daten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ros = RandomOverSampler(random_state=0)\n",
+    "X_train_resampled, y_train_resampled = ros.fit_resample(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_train_resampled.value_counts().plot.bar()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modellierung"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Um verschiedene Modelle zu erstellen, ist es eine gute Praxis, Pipelines und GridSearchCV zu verwenden. Diese beiden Tools sind ein einfacher Weg, um Hyperparamter-Tuning und k-fold Cross-Validation auf verschiedenen Modellen durchzufÃ¼hren. In diesem Fall bestehen die Pipelines aus einem StandardScaler und einem One vs Rest-Klassifikator"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Erstellen & Evaluieren verschiedener Modelle__"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## KNN\n",
+    "K-Nearest Neighbors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pipeline for KNN\n",
+    "pipeline1 = Pipeline([\n",
+    "    (\"scaler\", StandardScaler()),\n",
+    "    (\"knn\", OneVsRestClassifier(KNeighborsClassifier()))\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['memory', 'steps', 'verbose', 'scaler', 'knn', 'scaler__copy', 'scaler__with_mean', 'scaler__with_std', 'knn__estimator__algorithm', 'knn__estimator__leaf_size', 'knn__estimator__metric', 'knn__estimator__metric_params', 'knn__estimator__n_jobs', 'knn__estimator__n_neighbors', 'knn__estimator__p', 'knn__estimator__weights', 'knn__estimator', 'knn__n_jobs'])"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipeline1.get_params().keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
+      "[CV] knn__estimator__n_neighbors=8 ...................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ....... knn__estimator__n_neighbors=8, score=0.769, total=   3.5s\n",
+      "[CV] knn__estimator__n_neighbors=8 ...................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    3.4s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ....... knn__estimator__n_neighbors=8, score=0.754, total=   3.3s\n",
+      "[CV] knn__estimator__n_neighbors=8 ...................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    6.7s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ....... knn__estimator__n_neighbors=8, score=0.801, total=   3.3s\n",
+      "[CV] knn__estimator__n_neighbors=8 ...................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   10.0s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ....... knn__estimator__n_neighbors=8, score=0.832, total=   3.5s\n",
+      "[CV] knn__estimator__n_neighbors=8 ...................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   13.4s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ....... knn__estimator__n_neighbors=8, score=0.818, total=   3.3s\n",
+      "[CV] knn__estimator__n_neighbors=10 ..................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   16.7s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ...... knn__estimator__n_neighbors=10, score=0.769, total=   3.4s\n",
+      "[CV] knn__estimator__n_neighbors=10 ..................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:   20.1s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ...... knn__estimator__n_neighbors=10, score=0.749, total=   3.5s\n",
+      "[CV] knn__estimator__n_neighbors=10 ..................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:   23.6s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ...... knn__estimator__n_neighbors=10, score=0.815, total=   3.4s\n",
+      "[CV] knn__estimator__n_neighbors=10 ..................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:   27.1s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ...... knn__estimator__n_neighbors=10, score=0.828, total=   3.5s\n",
+      "[CV] knn__estimator__n_neighbors=10 ..................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:   30.6s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ...... knn__estimator__n_neighbors=10, score=0.824, total=   3.5s\n",
+      "[CV] knn__estimator__n_neighbors=20 ..................................\n",
+      "[CV] ...... knn__estimator__n_neighbors=20, score=0.782, total=   3.5s\n",
+      "[CV] knn__estimator__n_neighbors=20 ..................................\n",
+      "[CV] ...... knn__estimator__n_neighbors=20, score=0.734, total=   3.5s\n",
+      "[CV] knn__estimator__n_neighbors=20 ..................................\n",
+      "[CV] ...... knn__estimator__n_neighbors=20, score=0.828, total=   3.5s\n",
+      "[CV] knn__estimator__n_neighbors=20 ..................................\n",
+      "[CV] ...... knn__estimator__n_neighbors=20, score=0.820, total=   3.3s\n",
+      "[CV] knn__estimator__n_neighbors=20 ..................................\n",
+      "[CV] ...... knn__estimator__n_neighbors=20, score=0.832, total=   3.4s\n",
+      "[CV] knn__estimator__n_neighbors=30 ..................................\n",
+      "[CV] ...... knn__estimator__n_neighbors=30, score=0.768, total=   3.6s\n",
+      "[CV] knn__estimator__n_neighbors=30 ..................................\n",
+      "[CV] ...... knn__estimator__n_neighbors=30, score=0.726, total=   3.6s\n",
+      "[CV] knn__estimator__n_neighbors=30 ..................................\n",
+      "[CV] ...... knn__estimator__n_neighbors=30, score=0.820, total=   3.6s\n",
+      "[CV] knn__estimator__n_neighbors=30 ..................................\n",
+      "[CV] ...... knn__estimator__n_neighbors=30, score=0.820, total=   3.7s\n",
+      "[CV] knn__estimator__n_neighbors=30 ..................................\n",
+      "[CV] ...... knn__estimator__n_neighbors=30, score=0.826, total=   3.5s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:  1.2min finished\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                                       ('knn',\n",
+       "                                        OneVsRestClassifier(estimator=KNeighborsClassifier()))]),\n",
+       "             param_grid={'knn__estimator__n_neighbors': [8, 10, 20, 30]},\n",
+       "             verbose=10)"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#GridSearchCV-Object\n",
+    "clf = GridSearchCV(pipeline1, verbose=10, param_grid = {\n",
+    "    \"knn__estimator__n_neighbors\": [8, 10, 20, 30]\n",
+    "})\n",
+    "clf.fit(X_train_resampled, y_train_resampled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters {'knn__estimator__n_neighbors': 20}\n",
+      "Best model score: 0.7988723899743437\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Best parameters ' + str(clf.best_params_))\n",
+    "print('Best model score: ' + str(clf.best_score_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_knn__estimator__n_neighbors</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.291415</td>\n",
+       "      <td>0.020557</td>\n",
+       "      <td>3.064704</td>\n",
+       "      <td>0.088606</td>\n",
+       "      <td>8</td>\n",
+       "      <td>{'knn__estimator__n_neighbors': 8}</td>\n",
+       "      <td>0.769094</td>\n",
+       "      <td>0.753996</td>\n",
+       "      <td>0.801066</td>\n",
+       "      <td>0.832000</td>\n",
+       "      <td>0.817778</td>\n",
+       "      <td>0.794787</td>\n",
+       "      <td>0.029247</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.305789</td>\n",
+       "      <td>0.014108</td>\n",
+       "      <td>3.177124</td>\n",
+       "      <td>0.047759</td>\n",
+       "      <td>10</td>\n",
+       "      <td>{'knn__estimator__n_neighbors': 10}</td>\n",
+       "      <td>0.769094</td>\n",
+       "      <td>0.748668</td>\n",
+       "      <td>0.815275</td>\n",
+       "      <td>0.827556</td>\n",
+       "      <td>0.824000</td>\n",
+       "      <td>0.796919</td>\n",
+       "      <td>0.031973</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.291104</td>\n",
+       "      <td>0.008228</td>\n",
+       "      <td>3.165741</td>\n",
+       "      <td>0.071301</td>\n",
+       "      <td>20</td>\n",
+       "      <td>{'knn__estimator__n_neighbors': 20}</td>\n",
+       "      <td>0.781528</td>\n",
+       "      <td>0.733570</td>\n",
+       "      <td>0.827709</td>\n",
+       "      <td>0.819556</td>\n",
+       "      <td>0.832000</td>\n",
+       "      <td>0.798872</td>\n",
+       "      <td>0.037207</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.291344</td>\n",
+       "      <td>0.011009</td>\n",
+       "      <td>3.311857</td>\n",
+       "      <td>0.029414</td>\n",
+       "      <td>30</td>\n",
+       "      <td>{'knn__estimator__n_neighbors': 30}</td>\n",
+       "      <td>0.768206</td>\n",
+       "      <td>0.726465</td>\n",
+       "      <td>0.819716</td>\n",
+       "      <td>0.819556</td>\n",
+       "      <td>0.825778</td>\n",
+       "      <td>0.791944</td>\n",
+       "      <td>0.038806</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0       0.291415      0.020557         3.064704        0.088606   \n",
+       "1       0.305789      0.014108         3.177124        0.047759   \n",
+       "2       0.291104      0.008228         3.165741        0.071301   \n",
+       "3       0.291344      0.011009         3.311857        0.029414   \n",
+       "\n",
+       "  param_knn__estimator__n_neighbors                               params  \\\n",
+       "0                                 8   {'knn__estimator__n_neighbors': 8}   \n",
+       "1                                10  {'knn__estimator__n_neighbors': 10}   \n",
+       "2                                20  {'knn__estimator__n_neighbors': 20}   \n",
+       "3                                30  {'knn__estimator__n_neighbors': 30}   \n",
+       "\n",
+       "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0           0.769094           0.753996           0.801066           0.832000   \n",
+       "1           0.769094           0.748668           0.815275           0.827556   \n",
+       "2           0.781528           0.733570           0.827709           0.819556   \n",
+       "3           0.768206           0.726465           0.819716           0.819556   \n",
+       "\n",
+       "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
+       "0           0.817778         0.794787        0.029247                3  \n",
+       "1           0.824000         0.796919        0.031973                2  \n",
+       "2           0.832000         0.798872        0.037207                1  \n",
+       "3           0.825778         0.791944        0.038806                4  "
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_KNN = pd.DataFrame(clf.cv_results_)\n",
+    "results_KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classification_report_test = classification_report(y_test, clf.best_estimator_.predict(X_test), output_dict=True)\n",
+    "classification_report_train = classification_report(y_train_resampled, clf.best_estimator_.predict(X_train_resampled), output_dict=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#helper function\n",
+    "def make_results(classification_report_train, classification_report_test, model_type):\n",
+    "    df_rep1 = pd.DataFrame(classification_report_train)\n",
+    "    df_rep1['source'] ='train'\n",
+    "    df_rep1 = df_rep1.set_index([df_rep1.index,'source'])\n",
+    "    df_rep2 = pd.DataFrame(classification_report_test)\n",
+    "    df_rep2['source'] ='test'\n",
+    "    df_rep2 = df_rep2.set_index([df_rep2.index,'source'])\n",
+    "    frames = [df_rep1, df_rep2]\n",
+    "    df_rep = pd.concat(frames)\n",
+    "    df_rep['model_type'] = model_type\n",
+    "    df_rep = df_rep.set_index([df_rep.index,'model_type'])\n",
+    "    return df_rep.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>support</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>support</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>source</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_type</th>\n",
+       "      <th>KNN</th>\n",
+       "      <th>KNN</th>\n",
+       "      <th>KNN</th>\n",
+       "      <th>KNN</th>\n",
+       "      <th>KNN</th>\n",
+       "      <th>KNN</th>\n",
+       "      <th>KNN</th>\n",
+       "      <th>KNN</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LAYING</th>\n",
+       "      <td>0.912238</td>\n",
+       "      <td>0.805259</td>\n",
+       "      <td>0.855417</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.850000</td>\n",
+       "      <td>0.633147</td>\n",
+       "      <td>0.725720</td>\n",
+       "      <td>537.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SITTING</th>\n",
+       "      <td>0.810458</td>\n",
+       "      <td>0.793177</td>\n",
+       "      <td>0.801724</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.631295</td>\n",
+       "      <td>0.714868</td>\n",
+       "      <td>0.670487</td>\n",
+       "      <td>491.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STANDING</th>\n",
+       "      <td>0.808673</td>\n",
+       "      <td>0.901208</td>\n",
+       "      <td>0.852437</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.757679</td>\n",
+       "      <td>0.834586</td>\n",
+       "      <td>0.794275</td>\n",
+       "      <td>532.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WALKING</th>\n",
+       "      <td>0.976405</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.988062</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.957198</td>\n",
+       "      <td>0.991935</td>\n",
+       "      <td>0.974257</td>\n",
+       "      <td>496.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.791342</td>\n",
+       "      <td>0.791342</td>\n",
+       "      <td>0.791342</td>\n",
+       "      <td>0.791342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>macro avg</th>\n",
+       "      <td>0.876944</td>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.874410</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>0.799043</td>\n",
+       "      <td>0.793634</td>\n",
+       "      <td>0.791185</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weighted avg</th>\n",
+       "      <td>0.876944</td>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.874410</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>0.799743</td>\n",
+       "      <td>0.791342</td>\n",
+       "      <td>0.790227</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             precision    recall  f1-score      support precision    recall  \\\n",
+       "source           train     train     train        train      test      test   \n",
+       "model_type         KNN       KNN       KNN          KNN       KNN       KNN   \n",
+       "LAYING        0.912238  0.805259  0.855417  1407.000000  0.850000  0.633147   \n",
+       "SITTING       0.810458  0.793177  0.801724  1407.000000  0.631295  0.714868   \n",
+       "STANDING      0.808673  0.901208  0.852437  1407.000000  0.757679  0.834586   \n",
+       "WALKING       0.976405  1.000000  0.988062  1407.000000  0.957198  0.991935   \n",
+       "accuracy      0.874911  0.874911  0.874911     0.874911  0.791342  0.791342   \n",
+       "macro avg     0.876944  0.874911  0.874410  5628.000000  0.799043  0.793634   \n",
+       "weighted avg  0.876944  0.874911  0.874410  5628.000000  0.799743  0.791342   \n",
+       "\n",
+       "              f1-score      support  \n",
+       "source            test         test  \n",
+       "model_type         KNN          KNN  \n",
+       "LAYING        0.725720   537.000000  \n",
+       "SITTING       0.670487   491.000000  \n",
+       "STANDING      0.794275   532.000000  \n",
+       "WALKING       0.974257   496.000000  \n",
+       "accuracy      0.791342     0.791342  \n",
+       "macro avg     0.791185  2056.000000  \n",
+       "weighted avg  0.790227  2056.000000  "
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_KNN = make_results(classification_report_train, classification_report_test, 'KNN')\n",
+    "results_KNN"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pipeline\n",
+    "pipeline2 = Pipeline([\n",
+    "    (\"scaler\", StandardScaler()),\n",
+    "    (\"ranfo\", OneVsRestClassifier(RandomForestClassifier()))\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid = {'ranfo__estimator__max_depth': [5, 15, 30]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n",
+      "[CV] ranfo__estimator__max_depth=5 ...................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ....... ranfo__estimator__max_depth=5, score=0.793, total=   7.9s\n",
+      "[CV] ranfo__estimator__max_depth=5 ...................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.8s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ....... ranfo__estimator__max_depth=5, score=0.752, total=   7.6s\n",
+      "[CV] ranfo__estimator__max_depth=5 ...................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   15.4s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ....... ranfo__estimator__max_depth=5, score=0.850, total=   7.7s\n",
+      "[CV] ranfo__estimator__max_depth=5 ...................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   23.1s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ....... ranfo__estimator__max_depth=5, score=0.843, total=   7.7s\n",
+      "[CV] ranfo__estimator__max_depth=5 ...................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   30.7s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ....... ranfo__estimator__max_depth=5, score=0.830, total=   8.0s\n",
+      "[CV] ranfo__estimator__max_depth=15 ..................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   38.8s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ...... ranfo__estimator__max_depth=15, score=0.827, total=  13.2s\n",
+      "[CV] ranfo__estimator__max_depth=15 ..................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:   52.0s remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ...... ranfo__estimator__max_depth=15, score=0.813, total=  13.3s\n",
+      "[CV] ranfo__estimator__max_depth=15 ..................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:  1.1min remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ...... ranfo__estimator__max_depth=15, score=0.877, total=  13.3s\n",
+      "[CV] ranfo__estimator__max_depth=15 ..................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:  1.3min remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ...... ranfo__estimator__max_depth=15, score=0.876, total=  13.4s\n",
+      "[CV] ranfo__estimator__max_depth=15 ..................................\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:  1.5min remaining:    0.0s\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] ...... ranfo__estimator__max_depth=15, score=0.886, total=  13.6s\n",
+      "[CV] ranfo__estimator__max_depth=30 ..................................\n",
+      "[CV] ...... ranfo__estimator__max_depth=30, score=0.831, total=  13.3s\n",
+      "[CV] ranfo__estimator__max_depth=30 ..................................\n",
+      "[CV] ...... ranfo__estimator__max_depth=30, score=0.821, total=  13.6s\n",
+      "[CV] ranfo__estimator__max_depth=30 ..................................\n",
+      "[CV] ...... ranfo__estimator__max_depth=30, score=0.877, total=  13.8s\n",
+      "[CV] ranfo__estimator__max_depth=30 ..................................\n",
+      "[CV] ...... ranfo__estimator__max_depth=30, score=0.876, total=  13.8s\n",
+      "[CV] ranfo__estimator__max_depth=30 ..................................\n",
+      "[CV] ...... ranfo__estimator__max_depth=30, score=0.888, total=  14.3s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:  2.9min finished\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 4
-}
\ No newline at end of file
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                                       ('ranfo',\n",
+       "                                        OneVsRestClassifier(estimator=RandomForestClassifier()))]),\n",
+       "             param_grid={'ranfo__estimator__max_depth': [5, 15, 30]},\n",
+       "             verbose=10)"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#GridSearchCV-Object\n",
+    "clf2 = GridSearchCV(pipeline2, param_grid = param_grid, verbose=10)\n",
+    "clf2.fit(X_train_resampled, y_train_resampled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters {'ranfo__estimator__max_depth': 30}\n",
+      "Best model score: 0.8583949477008093\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Best parameters ' + str(clf2.best_params_))\n",
+    "print('Best model score: ' + str(clf2.best_score_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classification_report_test = classification_report(y_test, clf2.best_estimator_.predict(X_test), output_dict=True)\n",
+    "classification_report_train = classification_report(y_train_resampled, clf2.best_estimator_.predict(X_train_resampled), output_dict=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>support</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>support</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>source</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_type</th>\n",
+       "      <th>RandomForest</th>\n",
+       "      <th>RandomForest</th>\n",
+       "      <th>RandomForest</th>\n",
+       "      <th>RandomForest</th>\n",
+       "      <th>RandomForest</th>\n",
+       "      <th>RandomForest</th>\n",
+       "      <th>RandomForest</th>\n",
+       "      <th>RandomForest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LAYING</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1407.0</td>\n",
+       "      <td>0.821705</td>\n",
+       "      <td>0.789572</td>\n",
+       "      <td>0.805318</td>\n",
+       "      <td>537.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SITTING</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1407.0</td>\n",
+       "      <td>0.784519</td>\n",
+       "      <td>0.763747</td>\n",
+       "      <td>0.773994</td>\n",
+       "      <td>491.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STANDING</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1407.0</td>\n",
+       "      <td>0.829710</td>\n",
+       "      <td>0.860902</td>\n",
+       "      <td>0.845018</td>\n",
+       "      <td>532.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WALKING</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1407.0</td>\n",
+       "      <td>0.970588</td>\n",
+       "      <td>0.997984</td>\n",
+       "      <td>0.984095</td>\n",
+       "      <td>496.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.852140</td>\n",
+       "      <td>0.852140</td>\n",
+       "      <td>0.852140</td>\n",
+       "      <td>0.85214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>macro avg</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5628.0</td>\n",
+       "      <td>0.851631</td>\n",
+       "      <td>0.853051</td>\n",
+       "      <td>0.852106</td>\n",
+       "      <td>2056.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weighted avg</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5628.0</td>\n",
+       "      <td>0.850813</td>\n",
+       "      <td>0.852140</td>\n",
+       "      <td>0.851239</td>\n",
+       "      <td>2056.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                precision       recall     f1-score      support    precision  \\\n",
+       "source              train        train        train        train         test   \n",
+       "model_type   RandomForest RandomForest RandomForest RandomForest RandomForest   \n",
+       "LAYING                1.0          1.0          1.0       1407.0     0.821705   \n",
+       "SITTING               1.0          1.0          1.0       1407.0     0.784519   \n",
+       "STANDING              1.0          1.0          1.0       1407.0     0.829710   \n",
+       "WALKING               1.0          1.0          1.0       1407.0     0.970588   \n",
+       "accuracy              1.0          1.0          1.0          1.0     0.852140   \n",
+       "macro avg             1.0          1.0          1.0       5628.0     0.851631   \n",
+       "weighted avg          1.0          1.0          1.0       5628.0     0.850813   \n",
+       "\n",
+       "                   recall     f1-score      support  \n",
+       "source               test         test         test  \n",
+       "model_type   RandomForest RandomForest RandomForest  \n",
+       "LAYING           0.789572     0.805318    537.00000  \n",
+       "SITTING          0.763747     0.773994    491.00000  \n",
+       "STANDING         0.860902     0.845018    532.00000  \n",
+       "WALKING          0.997984     0.984095    496.00000  \n",
+       "accuracy         0.852140     0.852140      0.85214  \n",
+       "macro avg        0.853051     0.852106   2056.00000  \n",
+       "weighted avg     0.852140     0.851239   2056.00000  "
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_RandomForest = make_results(classification_report_train, classification_report_test, 'RandomForest')\n",
+    "results_RandomForest"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logistische Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pipeline\n",
+    "pipeline3 = Pipeline([\n",
+    "    (\"scaler\", StandardScaler()),\n",
+    "    (\"logreg\", OneVsRestClassifier(LogisticRegression(solver='newton-cg')))\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                                       ('logreg',\n",
+       "                                        OneVsRestClassifier(estimator=LogisticRegression(solver='newton-cg')))]),\n",
+       "             param_grid={})"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#GridSearchCV-Object\n",
+    "clf3 = GridSearchCV(pipeline3, param_grid = {})\n",
+    "clf3.fit(X_train_resampled, y_train_resampled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters {}\n",
+      "Best model score: 0.7738230905861456\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Best parameters ' + str(clf3.best_params_))\n",
+    "print('Best model score: ' + str(clf3.best_score_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classification_report_test = classification_report(y_test, clf3.best_estimator_.predict(X_test), output_dict=True)\n",
+    "classification_report_train = classification_report(y_train_resampled, clf3.best_estimator_.predict(X_train_resampled), output_dict=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>support</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>support</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>source</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_type</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <th>LogReg</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LAYING</th>\n",
+       "      <td>0.761204</td>\n",
+       "      <td>0.772566</td>\n",
+       "      <td>0.766843</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.690702</td>\n",
+       "      <td>0.677840</td>\n",
+       "      <td>0.684211</td>\n",
+       "      <td>537.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SITTING</th>\n",
+       "      <td>0.747636</td>\n",
+       "      <td>0.730633</td>\n",
+       "      <td>0.739037</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.687970</td>\n",
+       "      <td>0.745418</td>\n",
+       "      <td>0.715543</td>\n",
+       "      <td>491.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STANDING</th>\n",
+       "      <td>0.791356</td>\n",
+       "      <td>0.754797</td>\n",
+       "      <td>0.772645</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.833684</td>\n",
+       "      <td>0.744361</td>\n",
+       "      <td>0.786495</td>\n",
+       "      <td>532.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WALKING</th>\n",
+       "      <td>0.924477</td>\n",
+       "      <td>0.974414</td>\n",
+       "      <td>0.948789</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.873563</td>\n",
+       "      <td>0.919355</td>\n",
+       "      <td>0.895874</td>\n",
+       "      <td>496.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>0.769455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>macro avg</th>\n",
+       "      <td>0.806169</td>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.806828</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>0.771480</td>\n",
+       "      <td>0.771743</td>\n",
+       "      <td>0.770530</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weighted avg</th>\n",
+       "      <td>0.806169</td>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.806828</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>0.771161</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>0.769222</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             precision    recall  f1-score      support precision    recall  \\\n",
+       "source           train     train     train        train      test      test   \n",
+       "model_type      LogReg    LogReg    LogReg       LogReg    LogReg    LogReg   \n",
+       "LAYING        0.761204  0.772566  0.766843  1407.000000  0.690702  0.677840   \n",
+       "SITTING       0.747636  0.730633  0.739037  1407.000000  0.687970  0.745418   \n",
+       "STANDING      0.791356  0.754797  0.772645  1407.000000  0.833684  0.744361   \n",
+       "WALKING       0.924477  0.974414  0.948789  1407.000000  0.873563  0.919355   \n",
+       "accuracy      0.808102  0.808102  0.808102     0.808102  0.769455  0.769455   \n",
+       "macro avg     0.806169  0.808102  0.806828  5628.000000  0.771480  0.771743   \n",
+       "weighted avg  0.806169  0.808102  0.806828  5628.000000  0.771161  0.769455   \n",
+       "\n",
+       "              f1-score      support  \n",
+       "source            test         test  \n",
+       "model_type      LogReg       LogReg  \n",
+       "LAYING        0.684211   537.000000  \n",
+       "SITTING       0.715543   491.000000  \n",
+       "STANDING      0.786495   532.000000  \n",
+       "WALKING       0.895874   496.000000  \n",
+       "accuracy      0.769455     0.769455  \n",
+       "macro avg     0.770530  2056.000000  \n",
+       "weighted avg  0.769222  2056.000000  "
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_LogisticRegression = make_results(classification_report_train, classification_report_test, 'LogReg')\n",
+    "results_LogisticRegression"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Entscheidungsbaum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pipeline\n",
+    "pipeline4 = Pipeline([\n",
+    "    (\"scaler\", StandardScaler()),\n",
+    "    (\"decisiontree\", OneVsRestClassifier(DecisionTreeClassifier()))\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                                       ('decisiontree',\n",
+       "                                        OneVsRestClassifier(estimator=DecisionTreeClassifier()))]),\n",
+       "             param_grid={})"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#GridSearchCV-Object\n",
+    "clf4 = GridSearchCV(pipeline4, param_grid = {})\n",
+    "clf4.fit(X_train_resampled, y_train_resampled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters {}\n",
+      "Best model score: 0.6778698243536609\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Best parameters ' + str(clf4.best_params_))\n",
+    "print('Best model score: ' + str(clf4.best_score_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classification_report_test = classification_report(y_test, clf4.best_estimator_.predict(X_test), output_dict=True)\n",
+    "classification_report_train = classification_report(y_train_resampled, clf4.best_estimator_.predict(X_train_resampled), output_dict=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>support</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>support</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>source</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_type</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LAYING</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1407.0</td>\n",
+       "      <td>0.803797</td>\n",
+       "      <td>0.472998</td>\n",
+       "      <td>0.595545</td>\n",
+       "      <td>537.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SITTING</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1407.0</td>\n",
+       "      <td>0.642132</td>\n",
+       "      <td>0.515275</td>\n",
+       "      <td>0.571751</td>\n",
+       "      <td>491.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STANDING</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1407.0</td>\n",
+       "      <td>0.695391</td>\n",
+       "      <td>0.652256</td>\n",
+       "      <td>0.673133</td>\n",
+       "      <td>532.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WALKING</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1407.0</td>\n",
+       "      <td>0.578512</td>\n",
+       "      <td>0.987903</td>\n",
+       "      <td>0.729710</td>\n",
+       "      <td>496.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.653696</td>\n",
+       "      <td>0.653696</td>\n",
+       "      <td>0.653696</td>\n",
+       "      <td>0.653696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>macro avg</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5628.0</td>\n",
+       "      <td>0.679958</td>\n",
+       "      <td>0.657108</td>\n",
+       "      <td>0.642535</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weighted avg</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5628.0</td>\n",
+       "      <td>0.682790</td>\n",
+       "      <td>0.653696</td>\n",
+       "      <td>0.642306</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                precision       recall     f1-score      support    precision  \\\n",
+       "source              train        train        train        train         test   \n",
+       "model_type   DecisionTree DecisionTree DecisionTree DecisionTree DecisionTree   \n",
+       "LAYING                1.0          1.0          1.0       1407.0     0.803797   \n",
+       "SITTING               1.0          1.0          1.0       1407.0     0.642132   \n",
+       "STANDING              1.0          1.0          1.0       1407.0     0.695391   \n",
+       "WALKING               1.0          1.0          1.0       1407.0     0.578512   \n",
+       "accuracy              1.0          1.0          1.0          1.0     0.653696   \n",
+       "macro avg             1.0          1.0          1.0       5628.0     0.679958   \n",
+       "weighted avg          1.0          1.0          1.0       5628.0     0.682790   \n",
+       "\n",
+       "                   recall     f1-score      support  \n",
+       "source               test         test         test  \n",
+       "model_type   DecisionTree DecisionTree DecisionTree  \n",
+       "LAYING           0.472998     0.595545   537.000000  \n",
+       "SITTING          0.515275     0.571751   491.000000  \n",
+       "STANDING         0.652256     0.673133   532.000000  \n",
+       "WALKING          0.987903     0.729710   496.000000  \n",
+       "accuracy         0.653696     0.653696     0.653696  \n",
+       "macro avg        0.657108     0.642535  2056.000000  \n",
+       "weighted avg     0.653696     0.642306  2056.000000  "
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_DecisionTree = make_results(classification_report_train, classification_report_test, 'DecisionTree')\n",
+    "results_DecisionTree"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SVM\n",
+    "Support Vector Machine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Pipeline\n",
+    "pipeline5 = Pipeline([\n",
+    "    (\"scaler\", StandardScaler()),\n",
+    "    (\"svm\", OneVsRestClassifier(svm.SVC()))\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param_grid = {\n",
+    "    'svm__estimator__kernel': ['linear']}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
+       "                                       ('svm',\n",
+       "                                        OneVsRestClassifier(estimator=SVC()))]),\n",
+       "             param_grid={'svm__estimator__kernel': ['linear']})"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#GridSearchCV-Object\n",
+    "clf5 = GridSearchCV(pipeline5, param_grid)\n",
+    "clf5.fit(X_train_resampled, y_train_resampled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best parameters {'svm__estimator__kernel': 'linear'}\n",
+      "Best model score: 0.776133570159858\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Best parameters ' + str(clf5.best_params_))\n",
+    "print('Best model score: ' + str(clf5.best_score_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>param_svm__estimator__kernel</th>\n",
+       "      <th>params</th>\n",
+       "      <th>split0_test_score</th>\n",
+       "      <th>split1_test_score</th>\n",
+       "      <th>split2_test_score</th>\n",
+       "      <th>split3_test_score</th>\n",
+       "      <th>split4_test_score</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>rank_test_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6.828282</td>\n",
+       "      <td>0.334218</td>\n",
+       "      <td>0.329354</td>\n",
+       "      <td>0.019774</td>\n",
+       "      <td>linear</td>\n",
+       "      <td>{'svm__estimator__kernel': 'linear'}</td>\n",
+       "      <td>0.740675</td>\n",
+       "      <td>0.723801</td>\n",
+       "      <td>0.784192</td>\n",
+       "      <td>0.818667</td>\n",
+       "      <td>0.813333</td>\n",
+       "      <td>0.776134</td>\n",
+       "      <td>0.038089</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
+       "0       6.828282      0.334218         0.329354        0.019774   \n",
+       "\n",
+       "  param_svm__estimator__kernel                                params  \\\n",
+       "0                       linear  {'svm__estimator__kernel': 'linear'}   \n",
+       "\n",
+       "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
+       "0           0.740675           0.723801           0.784192           0.818667   \n",
+       "\n",
+       "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
+       "0           0.813333         0.776134        0.038089                1  "
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_SVM= pd.DataFrame(clf5.cv_results_)\n",
+    "results_SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classification_report_test = classification_report(y_test, clf5.best_estimator_.predict(X_test), output_dict=True)\n",
+    "classification_report_train = classification_report(y_train_resampled, clf5.best_estimator_.predict(X_train_resampled), output_dict=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>support</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>support</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>source</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>train</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "      <th>test</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_type</th>\n",
+       "      <th>SVM</th>\n",
+       "      <th>SVM</th>\n",
+       "      <th>SVM</th>\n",
+       "      <th>SVM</th>\n",
+       "      <th>SVM</th>\n",
+       "      <th>SVM</th>\n",
+       "      <th>SVM</th>\n",
+       "      <th>SVM</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>LAYING</th>\n",
+       "      <td>0.773826</td>\n",
+       "      <td>0.773276</td>\n",
+       "      <td>0.773551</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.689394</td>\n",
+       "      <td>0.677840</td>\n",
+       "      <td>0.683568</td>\n",
+       "      <td>537.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SITTING</th>\n",
+       "      <td>0.753613</td>\n",
+       "      <td>0.741294</td>\n",
+       "      <td>0.747402</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.675978</td>\n",
+       "      <td>0.739308</td>\n",
+       "      <td>0.706226</td>\n",
+       "      <td>491.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>STANDING</th>\n",
+       "      <td>0.790169</td>\n",
+       "      <td>0.765458</td>\n",
+       "      <td>0.777617</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.837607</td>\n",
+       "      <td>0.736842</td>\n",
+       "      <td>0.784000</td>\n",
+       "      <td>532.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>WALKING</th>\n",
+       "      <td>0.931525</td>\n",
+       "      <td>0.976546</td>\n",
+       "      <td>0.953505</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.871893</td>\n",
+       "      <td>0.919355</td>\n",
+       "      <td>0.894995</td>\n",
+       "      <td>496.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>0.766051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>macro avg</th>\n",
+       "      <td>0.812283</td>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.813019</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>0.768718</td>\n",
+       "      <td>0.768336</td>\n",
+       "      <td>0.767197</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weighted avg</th>\n",
+       "      <td>0.812283</td>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.813019</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>0.768568</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>0.765972</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             precision    recall  f1-score      support precision    recall  \\\n",
+       "source           train     train     train        train      test      test   \n",
+       "model_type         SVM       SVM       SVM          SVM       SVM       SVM   \n",
+       "LAYING        0.773826  0.773276  0.773551  1407.000000  0.689394  0.677840   \n",
+       "SITTING       0.753613  0.741294  0.747402  1407.000000  0.675978  0.739308   \n",
+       "STANDING      0.790169  0.765458  0.777617  1407.000000  0.837607  0.736842   \n",
+       "WALKING       0.931525  0.976546  0.953505  1407.000000  0.871893  0.919355   \n",
+       "accuracy      0.814144  0.814144  0.814144     0.814144  0.766051  0.766051   \n",
+       "macro avg     0.812283  0.814144  0.813019  5628.000000  0.768718  0.768336   \n",
+       "weighted avg  0.812283  0.814144  0.813019  5628.000000  0.768568  0.766051   \n",
+       "\n",
+       "              f1-score      support  \n",
+       "source            test         test  \n",
+       "model_type         SVM          SVM  \n",
+       "LAYING        0.683568   537.000000  \n",
+       "SITTING       0.706226   491.000000  \n",
+       "STANDING      0.784000   532.000000  \n",
+       "WALKING       0.894995   496.000000  \n",
+       "accuracy      0.766051     0.766051  \n",
+       "macro avg     0.767197  2056.000000  \n",
+       "weighted avg  0.765972  2056.000000  "
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_SVM = make_results(classification_report_train, classification_report_test, 'SVM')\n",
+    "results_SVM"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Zusammenfassung"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frames = [results_KNN, \n",
+    "           results_RandomForest,\n",
+    "           results_LogisticRegression,\n",
+    "           results_DecisionTree,\n",
+    "           results_SVM,\n",
+    "           ]\n",
+    "final_results = pd.concat(frames, axis=1).reindex(frames[0].index).transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>LAYING</th>\n",
+       "      <th>SITTING</th>\n",
+       "      <th>STANDING</th>\n",
+       "      <th>WALKING</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>macro avg</th>\n",
+       "      <th>weighted avg</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>model_type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">f1-score</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">test</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <td>0.595545</td>\n",
+       "      <td>0.571751</td>\n",
+       "      <td>0.673133</td>\n",
+       "      <td>0.729710</td>\n",
+       "      <td>0.653696</td>\n",
+       "      <td>0.642535</td>\n",
+       "      <td>0.642306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNN</th>\n",
+       "      <td>0.725720</td>\n",
+       "      <td>0.670487</td>\n",
+       "      <td>0.794275</td>\n",
+       "      <td>0.974257</td>\n",
+       "      <td>0.791342</td>\n",
+       "      <td>0.791185</td>\n",
+       "      <td>0.790227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>0.684211</td>\n",
+       "      <td>0.715543</td>\n",
+       "      <td>0.786495</td>\n",
+       "      <td>0.895874</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>0.770530</td>\n",
+       "      <td>0.769222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForest</th>\n",
+       "      <td>0.805318</td>\n",
+       "      <td>0.773994</td>\n",
+       "      <td>0.845018</td>\n",
+       "      <td>0.984095</td>\n",
+       "      <td>0.852140</td>\n",
+       "      <td>0.852106</td>\n",
+       "      <td>0.851239</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVM</th>\n",
+       "      <td>0.683568</td>\n",
+       "      <td>0.706226</td>\n",
+       "      <td>0.784000</td>\n",
+       "      <td>0.894995</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>0.767197</td>\n",
+       "      <td>0.765972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">precision</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">test</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <td>0.803797</td>\n",
+       "      <td>0.642132</td>\n",
+       "      <td>0.695391</td>\n",
+       "      <td>0.578512</td>\n",
+       "      <td>0.653696</td>\n",
+       "      <td>0.679958</td>\n",
+       "      <td>0.682790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNN</th>\n",
+       "      <td>0.850000</td>\n",
+       "      <td>0.631295</td>\n",
+       "      <td>0.757679</td>\n",
+       "      <td>0.957198</td>\n",
+       "      <td>0.791342</td>\n",
+       "      <td>0.799043</td>\n",
+       "      <td>0.799743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>0.690702</td>\n",
+       "      <td>0.687970</td>\n",
+       "      <td>0.833684</td>\n",
+       "      <td>0.873563</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>0.771480</td>\n",
+       "      <td>0.771161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForest</th>\n",
+       "      <td>0.821705</td>\n",
+       "      <td>0.784519</td>\n",
+       "      <td>0.829710</td>\n",
+       "      <td>0.970588</td>\n",
+       "      <td>0.852140</td>\n",
+       "      <td>0.851631</td>\n",
+       "      <td>0.850813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVM</th>\n",
+       "      <td>0.689394</td>\n",
+       "      <td>0.675978</td>\n",
+       "      <td>0.837607</td>\n",
+       "      <td>0.871893</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>0.768718</td>\n",
+       "      <td>0.768568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">recall</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">test</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <td>0.472998</td>\n",
+       "      <td>0.515275</td>\n",
+       "      <td>0.652256</td>\n",
+       "      <td>0.987903</td>\n",
+       "      <td>0.653696</td>\n",
+       "      <td>0.657108</td>\n",
+       "      <td>0.653696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNN</th>\n",
+       "      <td>0.633147</td>\n",
+       "      <td>0.714868</td>\n",
+       "      <td>0.834586</td>\n",
+       "      <td>0.991935</td>\n",
+       "      <td>0.791342</td>\n",
+       "      <td>0.793634</td>\n",
+       "      <td>0.791342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>0.677840</td>\n",
+       "      <td>0.745418</td>\n",
+       "      <td>0.744361</td>\n",
+       "      <td>0.919355</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>0.771743</td>\n",
+       "      <td>0.769455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForest</th>\n",
+       "      <td>0.789572</td>\n",
+       "      <td>0.763747</td>\n",
+       "      <td>0.860902</td>\n",
+       "      <td>0.997984</td>\n",
+       "      <td>0.852140</td>\n",
+       "      <td>0.853051</td>\n",
+       "      <td>0.852140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVM</th>\n",
+       "      <td>0.677840</td>\n",
+       "      <td>0.739308</td>\n",
+       "      <td>0.736842</td>\n",
+       "      <td>0.919355</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>0.768336</td>\n",
+       "      <td>0.766051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">support</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">test</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <td>537.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>532.000000</td>\n",
+       "      <td>496.000000</td>\n",
+       "      <td>0.653696</td>\n",
+       "      <td>2056.000000</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNN</th>\n",
+       "      <td>537.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>532.000000</td>\n",
+       "      <td>496.000000</td>\n",
+       "      <td>0.791342</td>\n",
+       "      <td>2056.000000</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>537.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>532.000000</td>\n",
+       "      <td>496.000000</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>2056.000000</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForest</th>\n",
+       "      <td>537.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>532.000000</td>\n",
+       "      <td>496.000000</td>\n",
+       "      <td>0.852140</td>\n",
+       "      <td>2056.000000</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVM</th>\n",
+       "      <td>537.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>532.000000</td>\n",
+       "      <td>496.000000</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>2056.000000</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">f1-score</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">train</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNN</th>\n",
+       "      <td>0.855417</td>\n",
+       "      <td>0.801724</td>\n",
+       "      <td>0.852437</td>\n",
+       "      <td>0.988062</td>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.874410</td>\n",
+       "      <td>0.874410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>0.766843</td>\n",
+       "      <td>0.739037</td>\n",
+       "      <td>0.772645</td>\n",
+       "      <td>0.948789</td>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.806828</td>\n",
+       "      <td>0.806828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForest</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVM</th>\n",
+       "      <td>0.773551</td>\n",
+       "      <td>0.747402</td>\n",
+       "      <td>0.777617</td>\n",
+       "      <td>0.953505</td>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.813019</td>\n",
+       "      <td>0.813019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">precision</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">train</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNN</th>\n",
+       "      <td>0.912238</td>\n",
+       "      <td>0.810458</td>\n",
+       "      <td>0.808673</td>\n",
+       "      <td>0.976405</td>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.876944</td>\n",
+       "      <td>0.876944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>0.761204</td>\n",
+       "      <td>0.747636</td>\n",
+       "      <td>0.791356</td>\n",
+       "      <td>0.924477</td>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.806169</td>\n",
+       "      <td>0.806169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForest</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVM</th>\n",
+       "      <td>0.773826</td>\n",
+       "      <td>0.753613</td>\n",
+       "      <td>0.790169</td>\n",
+       "      <td>0.931525</td>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.812283</td>\n",
+       "      <td>0.812283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">recall</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">train</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNN</th>\n",
+       "      <td>0.805259</td>\n",
+       "      <td>0.793177</td>\n",
+       "      <td>0.901208</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.874911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>0.772566</td>\n",
+       "      <td>0.730633</td>\n",
+       "      <td>0.754797</td>\n",
+       "      <td>0.974414</td>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.808102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForest</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVM</th>\n",
+       "      <td>0.773276</td>\n",
+       "      <td>0.741294</td>\n",
+       "      <td>0.765458</td>\n",
+       "      <td>0.976546</td>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.814144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">support</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">train</th>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>5628.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNN</th>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>5628.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>5628.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForest</th>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>5628.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVM</th>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>1407.000000</td>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>5628.000000</td>\n",
+       "      <td>5628.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    LAYING      SITTING     STANDING  \\\n",
+       "          source model_type                                            \n",
+       "f1-score  test   DecisionTree     0.595545     0.571751     0.673133   \n",
+       "                 KNN              0.725720     0.670487     0.794275   \n",
+       "                 LogReg           0.684211     0.715543     0.786495   \n",
+       "                 RandomForest     0.805318     0.773994     0.845018   \n",
+       "                 SVM              0.683568     0.706226     0.784000   \n",
+       "precision test   DecisionTree     0.803797     0.642132     0.695391   \n",
+       "                 KNN              0.850000     0.631295     0.757679   \n",
+       "                 LogReg           0.690702     0.687970     0.833684   \n",
+       "                 RandomForest     0.821705     0.784519     0.829710   \n",
+       "                 SVM              0.689394     0.675978     0.837607   \n",
+       "recall    test   DecisionTree     0.472998     0.515275     0.652256   \n",
+       "                 KNN              0.633147     0.714868     0.834586   \n",
+       "                 LogReg           0.677840     0.745418     0.744361   \n",
+       "                 RandomForest     0.789572     0.763747     0.860902   \n",
+       "                 SVM              0.677840     0.739308     0.736842   \n",
+       "support   test   DecisionTree   537.000000   491.000000   532.000000   \n",
+       "                 KNN            537.000000   491.000000   532.000000   \n",
+       "                 LogReg         537.000000   491.000000   532.000000   \n",
+       "                 RandomForest   537.000000   491.000000   532.000000   \n",
+       "                 SVM            537.000000   491.000000   532.000000   \n",
+       "f1-score  train  DecisionTree     1.000000     1.000000     1.000000   \n",
+       "                 KNN              0.855417     0.801724     0.852437   \n",
+       "                 LogReg           0.766843     0.739037     0.772645   \n",
+       "                 RandomForest     1.000000     1.000000     1.000000   \n",
+       "                 SVM              0.773551     0.747402     0.777617   \n",
+       "precision train  DecisionTree     1.000000     1.000000     1.000000   \n",
+       "                 KNN              0.912238     0.810458     0.808673   \n",
+       "                 LogReg           0.761204     0.747636     0.791356   \n",
+       "                 RandomForest     1.000000     1.000000     1.000000   \n",
+       "                 SVM              0.773826     0.753613     0.790169   \n",
+       "recall    train  DecisionTree     1.000000     1.000000     1.000000   \n",
+       "                 KNN              0.805259     0.793177     0.901208   \n",
+       "                 LogReg           0.772566     0.730633     0.754797   \n",
+       "                 RandomForest     1.000000     1.000000     1.000000   \n",
+       "                 SVM              0.773276     0.741294     0.765458   \n",
+       "support   train  DecisionTree  1407.000000  1407.000000  1407.000000   \n",
+       "                 KNN           1407.000000  1407.000000  1407.000000   \n",
+       "                 LogReg        1407.000000  1407.000000  1407.000000   \n",
+       "                 RandomForest  1407.000000  1407.000000  1407.000000   \n",
+       "                 SVM           1407.000000  1407.000000  1407.000000   \n",
+       "\n",
+       "                                   WALKING  accuracy    macro avg  \\\n",
+       "          source model_type                                         \n",
+       "f1-score  test   DecisionTree     0.729710  0.653696     0.642535   \n",
+       "                 KNN              0.974257  0.791342     0.791185   \n",
+       "                 LogReg           0.895874  0.769455     0.770530   \n",
+       "                 RandomForest     0.984095  0.852140     0.852106   \n",
+       "                 SVM              0.894995  0.766051     0.767197   \n",
+       "precision test   DecisionTree     0.578512  0.653696     0.679958   \n",
+       "                 KNN              0.957198  0.791342     0.799043   \n",
+       "                 LogReg           0.873563  0.769455     0.771480   \n",
+       "                 RandomForest     0.970588  0.852140     0.851631   \n",
+       "                 SVM              0.871893  0.766051     0.768718   \n",
+       "recall    test   DecisionTree     0.987903  0.653696     0.657108   \n",
+       "                 KNN              0.991935  0.791342     0.793634   \n",
+       "                 LogReg           0.919355  0.769455     0.771743   \n",
+       "                 RandomForest     0.997984  0.852140     0.853051   \n",
+       "                 SVM              0.919355  0.766051     0.768336   \n",
+       "support   test   DecisionTree   496.000000  0.653696  2056.000000   \n",
+       "                 KNN            496.000000  0.791342  2056.000000   \n",
+       "                 LogReg         496.000000  0.769455  2056.000000   \n",
+       "                 RandomForest   496.000000  0.852140  2056.000000   \n",
+       "                 SVM            496.000000  0.766051  2056.000000   \n",
+       "f1-score  train  DecisionTree     1.000000  1.000000     1.000000   \n",
+       "                 KNN              0.988062  0.874911     0.874410   \n",
+       "                 LogReg           0.948789  0.808102     0.806828   \n",
+       "                 RandomForest     1.000000  1.000000     1.000000   \n",
+       "                 SVM              0.953505  0.814144     0.813019   \n",
+       "precision train  DecisionTree     1.000000  1.000000     1.000000   \n",
+       "                 KNN              0.976405  0.874911     0.876944   \n",
+       "                 LogReg           0.924477  0.808102     0.806169   \n",
+       "                 RandomForest     1.000000  1.000000     1.000000   \n",
+       "                 SVM              0.931525  0.814144     0.812283   \n",
+       "recall    train  DecisionTree     1.000000  1.000000     1.000000   \n",
+       "                 KNN              1.000000  0.874911     0.874911   \n",
+       "                 LogReg           0.974414  0.808102     0.808102   \n",
+       "                 RandomForest     1.000000  1.000000     1.000000   \n",
+       "                 SVM              0.976546  0.814144     0.814144   \n",
+       "support   train  DecisionTree  1407.000000  1.000000  5628.000000   \n",
+       "                 KNN           1407.000000  0.874911  5628.000000   \n",
+       "                 LogReg        1407.000000  0.808102  5628.000000   \n",
+       "                 RandomForest  1407.000000  1.000000  5628.000000   \n",
+       "                 SVM           1407.000000  0.814144  5628.000000   \n",
+       "\n",
+       "                               weighted avg  \n",
+       "          source model_type                  \n",
+       "f1-score  test   DecisionTree      0.642306  \n",
+       "                 KNN               0.790227  \n",
+       "                 LogReg            0.769222  \n",
+       "                 RandomForest      0.851239  \n",
+       "                 SVM               0.765972  \n",
+       "precision test   DecisionTree      0.682790  \n",
+       "                 KNN               0.799743  \n",
+       "                 LogReg            0.771161  \n",
+       "                 RandomForest      0.850813  \n",
+       "                 SVM               0.768568  \n",
+       "recall    test   DecisionTree      0.653696  \n",
+       "                 KNN               0.791342  \n",
+       "                 LogReg            0.769455  \n",
+       "                 RandomForest      0.852140  \n",
+       "                 SVM               0.766051  \n",
+       "support   test   DecisionTree   2056.000000  \n",
+       "                 KNN            2056.000000  \n",
+       "                 LogReg         2056.000000  \n",
+       "                 RandomForest   2056.000000  \n",
+       "                 SVM            2056.000000  \n",
+       "f1-score  train  DecisionTree      1.000000  \n",
+       "                 KNN               0.874410  \n",
+       "                 LogReg            0.806828  \n",
+       "                 RandomForest      1.000000  \n",
+       "                 SVM               0.813019  \n",
+       "precision train  DecisionTree      1.000000  \n",
+       "                 KNN               0.876944  \n",
+       "                 LogReg            0.806169  \n",
+       "                 RandomForest      1.000000  \n",
+       "                 SVM               0.812283  \n",
+       "recall    train  DecisionTree      1.000000  \n",
+       "                 KNN               0.874911  \n",
+       "                 LogReg            0.808102  \n",
+       "                 RandomForest      1.000000  \n",
+       "                 SVM               0.814144  \n",
+       "support   train  DecisionTree   5628.000000  \n",
+       "                 KNN            5628.000000  \n",
+       "                 LogReg         5628.000000  \n",
+       "                 RandomForest   5628.000000  \n",
+       "                 SVM            5628.000000  "
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_results.sort_index(level=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_results.sort_index(level=1).to_csv('Results_VIF_lower10.csv')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 5.  Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#helper function\n",
+    "def make_confusion_matrix(y_test, X_test, the_clf):\n",
+    "    y_pred = the_clf.best_estimator_.predict(X_test)\n",
+    "    cf_matrix = confusion_matrix(y_test,y_pred)\n",
+    "    df_cf_matrix = pd.DataFrame(cf_matrix,columns=the_clf.best_estimator_.steps[1][1].classes_)\n",
+    "    df_cf_matrix.index = the_clf.best_estimator_.steps[1][1].classes_\n",
+    "    \n",
+    "    plt.figure(figsize=(28,7))\n",
+    "\n",
+    "    plt.subplot(1,3,1) # first heatmap\n",
+    "    heatmap1 = sns.heatmap(df_cf_matrix, annot=True,fmt='d');\n",
+    "    heatmap1.yaxis.set_ticklabels(heatmap1.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=10)\n",
+    "    heatmap1.xaxis.set_ticklabels(heatmap1.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=10)\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')\n",
+    "\n",
+    "    plt.subplot(1,3,2) # second heatmap\n",
+    "    heatmap2 = sns.heatmap(df_cf_matrix/np.sum(df_cf_matrix), annot=True, fmt='.2%')\n",
+    "    heatmap2.yaxis.set_ticklabels(heatmap2.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=10)\n",
+    "    heatmap2.xaxis.set_ticklabels(heatmap2.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=10)\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')\n",
+    "\n",
+    "    fig_name = str(the_clf.best_estimator_.steps[1][1].estimators_[0]) + '_confusionMatrix.png'\n",
+    "    plt.savefig(fig_name, dpi=150, format='png')\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Erzielte Genauigkeit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>accuracy_train</th>\n",
+       "      <th>accuracy_test</th>\n",
+       "      <th>model</th>\n",
+       "      <th>diff_train_test</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>model_type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>KNN</th>\n",
+       "      <td>0.874911</td>\n",
+       "      <td>0.791342</td>\n",
+       "      <td>KNN</td>\n",
+       "      <td>0.083569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForest</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.852140</td>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>0.147860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>0.808102</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>LogReg</td>\n",
+       "      <td>0.038647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DecisionTree</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.653696</td>\n",
+       "      <td>DecisionTree</td>\n",
+       "      <td>0.346304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SVM</th>\n",
+       "      <td>0.814144</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>SVM</td>\n",
+       "      <td>0.048093</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              accuracy_train  accuracy_test         model  diff_train_test\n",
+       "model_type                                                                \n",
+       "KNN                 0.874911       0.791342           KNN         0.083569\n",
+       "RandomForest        1.000000       0.852140  RandomForest         0.147860\n",
+       "LogReg              0.808102       0.769455        LogReg         0.038647\n",
+       "DecisionTree        1.000000       0.653696  DecisionTree         0.346304\n",
+       "SVM                 0.814144       0.766051           SVM         0.048093"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_train_test = pd.DataFrame()\n",
+    "accuracy_train_test['accuracy_train'] = final_results.xs(\"train\", level=1).xs(\"precision\", level=0)['accuracy']\n",
+    "accuracy_train_test['accuracy_test'] = final_results.xs(\"test\", level=1).xs(\"precision\", level=0)['accuracy']\n",
+    "accuracy_train_test['model'] = accuracy_train_test.index\n",
+    "\n",
+    "accuracy_train_test['diff_train_test'] = accuracy_train_test['accuracy_train'] - accuracy_train_test['accuracy_test']\n",
+    "accuracy_train_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,7))\n",
+    "df_temp = accuracy_train_test.melt('model', var_name='cols',  value_name='vals')\n",
+    "ax = sns.barplot(x=\"model\", y=\"vals\", hue=\"cols\", data=df_temp)\n",
+    "#for index, row in df_temp:\n",
+    "#    ax.text(row.name,row.vals, round(row.vals,2), color='black', ha=\"center\")\n",
+    "for p in ax.patches:\n",
+    "    ax.annotate(format(p.get_height(), '.3f'), \n",
+    "                   (p.get_x() + p.get_width() / 2., p.get_height()), \n",
+    "                   ha = 'center', va = 'center', \n",
+    "                   xytext = (0, 9), \n",
+    "                   textcoords = 'offset points')\n",
+    "plt.ylabel('Accuracy')\n",
+    "plt.xlabel('Model')\n",
+    "\n",
+    "fig_name = 'Accuracy_performance.png'\n",
+    "plt.savefig(fig_name, dpi=150, format='png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>model</th>\n",
+       "      <th>cols</th>\n",
+       "      <th>vals</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>accuracy_train</td>\n",
+       "      <td>0.874911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>accuracy_train</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LogReg</td>\n",
+       "      <td>accuracy_train</td>\n",
+       "      <td>0.808102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>DecisionTree</td>\n",
+       "      <td>accuracy_train</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SVM</td>\n",
+       "      <td>accuracy_train</td>\n",
+       "      <td>0.814144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>accuracy_test</td>\n",
+       "      <td>0.791342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>accuracy_test</td>\n",
+       "      <td>0.852140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>LogReg</td>\n",
+       "      <td>accuracy_test</td>\n",
+       "      <td>0.769455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>DecisionTree</td>\n",
+       "      <td>accuracy_test</td>\n",
+       "      <td>0.653696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>SVM</td>\n",
+       "      <td>accuracy_test</td>\n",
+       "      <td>0.766051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>KNN</td>\n",
+       "      <td>diff_train_test</td>\n",
+       "      <td>0.083569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>RandomForest</td>\n",
+       "      <td>diff_train_test</td>\n",
+       "      <td>0.147860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>LogReg</td>\n",
+       "      <td>diff_train_test</td>\n",
+       "      <td>0.038647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>DecisionTree</td>\n",
+       "      <td>diff_train_test</td>\n",
+       "      <td>0.346304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>SVM</td>\n",
+       "      <td>diff_train_test</td>\n",
+       "      <td>0.048093</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           model             cols      vals\n",
+       "0            KNN   accuracy_train  0.874911\n",
+       "1   RandomForest   accuracy_train  1.000000\n",
+       "2         LogReg   accuracy_train  0.808102\n",
+       "3   DecisionTree   accuracy_train  1.000000\n",
+       "4            SVM   accuracy_train  0.814144\n",
+       "5            KNN    accuracy_test  0.791342\n",
+       "6   RandomForest    accuracy_test  0.852140\n",
+       "7         LogReg    accuracy_test  0.769455\n",
+       "8   DecisionTree    accuracy_test  0.653696\n",
+       "9            SVM    accuracy_test  0.766051\n",
+       "10           KNN  diff_train_test  0.083569\n",
+       "11  RandomForest  diff_train_test  0.147860\n",
+       "12        LogReg  diff_train_test  0.038647\n",
+       "13  DecisionTree  diff_train_test  0.346304\n",
+       "14           SVM  diff_train_test  0.048093"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_temp"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* **KNN** --> Die Leistung auf Testdaten ist ca. 8% niedriger als auf Trainingsdaten. Wahrscheinlich passt sich das Modell zu sehr an die Trainingsdaten an und lÃ¤sst sich nicht gut auf ungesehene Testdaten verallgemeinern.\n",
+    "* **Random Forest** --> Die Leistung bei den Testdaten ist 15% geringer als bei den Trainingsdaten. Das Modell **Ã¼bererfÃ¼llt** die Trainingsdaten und verallgemeinert sich nicht gut auf die ungesehenen Testdaten.\n",
+    "* **Logistische Regression** --> Trainings- und Testleistung sind sehr Ã¤hnlich (diff=3.9%). Dies bedeutet wahrscheinlich, dass das erstellte Modell **gut** auf neue Daten verallgemeinert.\n",
+    "* **Entscheidungsbaum** --> Die Leistung bei den Testdaten ist 34% niedriger als bei den Trainingsdaten. Das Modell passt sich zu sehr an die Trainingsdaten an und verallgemeinert **nicht gut** auf die unbekannten Testdaten.\n",
+    "* **Support Vector Machine** --> Trainings- und Testleistung sind sehr Ã¤hnlich (diff=4.8%). Das bedeutet wahrscheinlich, dass das erstellte Modell **gut** auf neue Daten verallgemeinert.\n",
+    "-->ÃœberprÃ¼fen wir die Konfusionsmatrix der beiden besten Modelle"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Konfusionsmatrix-Logistische Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2016x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "make_confusion_matrix(y_test,X_test, clf3)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Konfusionsmatrix-SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2016x504 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "make_confusion_matrix(y_test,X_test, clf5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>LAYING</th>\n",
+       "      <th>SITTING</th>\n",
+       "      <th>STANDING</th>\n",
+       "      <th>WALKING</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>macro avg</th>\n",
+       "      <th>weighted avg</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>model_type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>precision</th>\n",
+       "      <th>test</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>0.690702</td>\n",
+       "      <td>0.687970</td>\n",
+       "      <td>0.833684</td>\n",
+       "      <td>0.873563</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>0.771480</td>\n",
+       "      <td>0.771161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recall</th>\n",
+       "      <th>test</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>0.677840</td>\n",
+       "      <td>0.745418</td>\n",
+       "      <td>0.744361</td>\n",
+       "      <td>0.919355</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>0.771743</td>\n",
+       "      <td>0.769455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>test</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>0.684211</td>\n",
+       "      <td>0.715543</td>\n",
+       "      <td>0.786495</td>\n",
+       "      <td>0.895874</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>0.770530</td>\n",
+       "      <td>0.769222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>support</th>\n",
+       "      <th>test</th>\n",
+       "      <th>LogReg</th>\n",
+       "      <td>537.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>532.000000</td>\n",
+       "      <td>496.000000</td>\n",
+       "      <td>0.769455</td>\n",
+       "      <td>2056.000000</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>precision</th>\n",
+       "      <th>test</th>\n",
+       "      <th>SVM</th>\n",
+       "      <td>0.689394</td>\n",
+       "      <td>0.675978</td>\n",
+       "      <td>0.837607</td>\n",
+       "      <td>0.871893</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>0.768718</td>\n",
+       "      <td>0.768568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>recall</th>\n",
+       "      <th>test</th>\n",
+       "      <th>SVM</th>\n",
+       "      <td>0.677840</td>\n",
+       "      <td>0.739308</td>\n",
+       "      <td>0.736842</td>\n",
+       "      <td>0.919355</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>0.768336</td>\n",
+       "      <td>0.766051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f1-score</th>\n",
+       "      <th>test</th>\n",
+       "      <th>SVM</th>\n",
+       "      <td>0.683568</td>\n",
+       "      <td>0.706226</td>\n",
+       "      <td>0.784000</td>\n",
+       "      <td>0.894995</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>0.767197</td>\n",
+       "      <td>0.765972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>support</th>\n",
+       "      <th>test</th>\n",
+       "      <th>SVM</th>\n",
+       "      <td>537.000000</td>\n",
+       "      <td>491.000000</td>\n",
+       "      <td>532.000000</td>\n",
+       "      <td>496.000000</td>\n",
+       "      <td>0.766051</td>\n",
+       "      <td>2056.000000</td>\n",
+       "      <td>2056.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 LAYING     SITTING    STANDING     WALKING  \\\n",
+       "          source model_type                                                   \n",
+       "precision test   LogReg        0.690702    0.687970    0.833684    0.873563   \n",
+       "recall    test   LogReg        0.677840    0.745418    0.744361    0.919355   \n",
+       "f1-score  test   LogReg        0.684211    0.715543    0.786495    0.895874   \n",
+       "support   test   LogReg      537.000000  491.000000  532.000000  496.000000   \n",
+       "precision test   SVM           0.689394    0.675978    0.837607    0.871893   \n",
+       "recall    test   SVM           0.677840    0.739308    0.736842    0.919355   \n",
+       "f1-score  test   SVM           0.683568    0.706226    0.784000    0.894995   \n",
+       "support   test   SVM         537.000000  491.000000  532.000000  496.000000   \n",
+       "\n",
+       "                             accuracy    macro avg  weighted avg  \n",
+       "          source model_type                                       \n",
+       "precision test   LogReg      0.769455     0.771480      0.771161  \n",
+       "recall    test   LogReg      0.769455     0.771743      0.769455  \n",
+       "f1-score  test   LogReg      0.769455     0.770530      0.769222  \n",
+       "support   test   LogReg      0.769455  2056.000000   2056.000000  \n",
+       "precision test   SVM         0.766051     0.768718      0.768568  \n",
+       "recall    test   SVM         0.766051     0.768336      0.766051  \n",
+       "f1-score  test   SVM         0.766051     0.767197      0.765972  \n",
+       "support   test   SVM         0.766051  2056.000000   2056.000000  "
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_results.loc[(slice(None), 'test', ('LogReg','SVM')), :].to_csv('LogReg_SVM_results.csv')\n",
+    "final_results.loc[(slice(None), 'test', ('LogReg','SVM')), :]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wichtigkeit der Merkmale\n",
+    "\n",
+    "* \n",
+    "Ein One vs Rest Classifier besteht aus n einzelnen Klassifikatoren (mit n=Anzahl der Klassen). Das macht es schwierig, die Merkmalsbedeutung fÃ¼r den gesamten OvsR-Klassifikator zu interpretieren. Eine MÃ¶glichkeit besteht darin, die Koeffizienten fÃ¼r jedes einzelne Modell zu bestimmen und den Mittelwert Ã¼ber jeden Koeffizienten zu bilden.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_the_feature_importances(clfobject,est_name, functype):\n",
+    "    feat_impts = []\n",
+    "    #estimator have feature_importances_ attribute\n",
+    "    if functype ==1:\n",
+    "        for esti in clfobject.best_estimator_.named_steps[est_name].estimators_:\n",
+    "            feat_impts.append(np.abs(esti.feature_importances_))\n",
+    "    #estimator have coef_ attribute\n",
+    "    if functype ==2:\n",
+    "        for esti in clfobject.best_estimator_.named_steps[est_name].estimators_:\n",
+    "            feat_impts.append(np.abs(esti.coef_[0]))\n",
+    "        \n",
+    "    return np.mean(feat_impts, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_importance = pd.DataFrame({},[])\n",
+    "feature_importance['Features'] = X_test.columns\n",
+    "feature_importance['Importance'] = get_the_feature_importances(clf3,'logreg',2)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ergebnisse"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Das beste Modell ist der OvsR-Klassifikator mit logistischer Regression. \n",
+    "* Es hat eine gute Genauigkeit (0.77:test, 0.81:train) und eine gute PrÃ¤zision (0.87:test) fÃ¼r die Klasse 'WALKING'.\n",
+    "* Die 10 wichtigsten Eigenschaften dieses OvsR-Klassifikators sind die folgenden:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Features</th>\n",
+       "      <th>Importance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>tBodyGyro-entropy()-X</td>\n",
+       "      <td>1.170518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>tBodyGyro-entropy()-Y</td>\n",
+       "      <td>1.070693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>tBodyAcc-correlation()-Y,Z</td>\n",
+       "      <td>0.791616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>tBodyGyro-entropy()-Z</td>\n",
+       "      <td>0.790347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>tBodyGyro-mean()-Y</td>\n",
+       "      <td>0.643740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>fBodyGyro-meanFreq()-X</td>\n",
+       "      <td>0.537990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>fBodyAccJerk-maxInds-X</td>\n",
+       "      <td>0.510996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>tBodyGyro-correlation()-X,Y</td>\n",
+       "      <td>0.501925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>tBodyGyro-mean()-X</td>\n",
+       "      <td>0.490046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>tBodyGyro-correlation()-X,Z</td>\n",
+       "      <td>0.468363</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Features  Importance\n",
+       "15        tBodyGyro-entropy()-X    1.170518\n",
+       "16        tBodyGyro-entropy()-Y    1.070693\n",
+       "3    tBodyAcc-correlation()-Y,Z    0.791616\n",
+       "17        tBodyGyro-entropy()-Z    0.790347\n",
+       "14           tBodyGyro-mean()-Y    0.643740\n",
+       "32       fBodyGyro-meanFreq()-X    0.537990\n",
+       "29       fBodyAccJerk-maxInds-X    0.510996\n",
+       "18  tBodyGyro-correlation()-X,Y    0.501925\n",
+       "13           tBodyGyro-mean()-X    0.490046\n",
+       "19  tBodyGyro-correlation()-X,Z    0.468363"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_importance.sort_values('Importance',ascending=0).head(10).to_csv('FeatureImportanceTop10.csv')\n",
+    "feature_importance.sort_values('Importance',ascending=0).to_csv('FeatureImportanceAll.csv')\n",
+    "feature_importance.sort_values('Importance',ascending=0).head(10)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-->Die meisten dieser wichtigen Funktionen stammen vom Gyroskopsensor im Smartphone."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## EndgÃ¼ltiges Modell fÃ¼r das Deployment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_model = clf3.best_estimator_"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 6. Deployment\n",
+    "Teste die Prognose lokal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>tBodyAcc-mean()-Y</th>\n",
+       "      <th>tBodyAcc-correlation()-X,Y</th>\n",
+       "      <th>tBodyAcc-correlation()-X,Z</th>\n",
+       "      <th>tBodyAcc-correlation()-Y,Z</th>\n",
+       "      <th>tGravityAcc-correlation()-X,Y</th>\n",
+       "      <th>tGravityAcc-correlation()-X,Z</th>\n",
+       "      <th>tGravityAcc-correlation()-Y,Z</th>\n",
+       "      <th>tBodyAccJerk-mean()-X</th>\n",
+       "      <th>tBodyAccJerk-mean()-Y</th>\n",
+       "      <th>tBodyAccJerk-mean()-Z</th>\n",
+       "      <th>...</th>\n",
+       "      <th>fBodyAccJerk-maxInds-X</th>\n",
+       "      <th>fBodyAccJerk-maxInds-Y</th>\n",
+       "      <th>fBodyAccJerk-maxInds-Z</th>\n",
+       "      <th>fBodyGyro-meanFreq()-X</th>\n",
+       "      <th>fBodyGyro-meanFreq()-Y</th>\n",
+       "      <th>fBodyGyro-meanFreq()-Z</th>\n",
+       "      <th>angle(tBodyAccMean,gravity)</th>\n",
+       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
+       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
+       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5293</th>\n",
+       "      <td>-0.023285</td>\n",
+       "      <td>0.076989</td>\n",
+       "      <td>-0.490546</td>\n",
+       "      <td>-0.709003</td>\n",
+       "      <td>0.980580</td>\n",
+       "      <td>-0.996352</td>\n",
+       "      <td>-0.960117</td>\n",
+       "      <td>0.072046</td>\n",
+       "      <td>0.045754</td>\n",
+       "      <td>-0.106043</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.52</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>0.184035</td>\n",
+       "      <td>-0.059323</td>\n",
+       "      <td>0.438107</td>\n",
+       "      <td>0.006462</td>\n",
+       "      <td>0.162920</td>\n",
+       "      <td>-0.825886</td>\n",
+       "      <td>0.271151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5294</th>\n",
+       "      <td>-0.013163</td>\n",
+       "      <td>-0.104983</td>\n",
+       "      <td>-0.429134</td>\n",
+       "      <td>0.399177</td>\n",
+       "      <td>0.945233</td>\n",
+       "      <td>-0.911415</td>\n",
+       "      <td>-0.738535</td>\n",
+       "      <td>0.070181</td>\n",
+       "      <td>-0.017876</td>\n",
+       "      <td>-0.001721</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.16</td>\n",
+       "      <td>-0.32</td>\n",
+       "      <td>-0.40</td>\n",
+       "      <td>0.018109</td>\n",
+       "      <td>-0.227266</td>\n",
+       "      <td>-0.151698</td>\n",
+       "      <td>-0.083495</td>\n",
+       "      <td>0.017500</td>\n",
+       "      <td>-0.434375</td>\n",
+       "      <td>0.920593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5295</th>\n",
+       "      <td>-0.026050</td>\n",
+       "      <td>0.305653</td>\n",
+       "      <td>-0.323848</td>\n",
+       "      <td>0.279786</td>\n",
+       "      <td>0.548432</td>\n",
+       "      <td>-0.334864</td>\n",
+       "      <td>0.590302</td>\n",
+       "      <td>0.069368</td>\n",
+       "      <td>-0.004908</td>\n",
+       "      <td>-0.013673</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.64</td>\n",
+       "      <td>-0.40</td>\n",
+       "      <td>-0.44</td>\n",
+       "      <td>-0.479145</td>\n",
+       "      <td>-0.210084</td>\n",
+       "      <td>0.049310</td>\n",
+       "      <td>-0.034956</td>\n",
+       "      <td>0.202302</td>\n",
+       "      <td>0.064103</td>\n",
+       "      <td>0.145068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5296</th>\n",
+       "      <td>-0.032614</td>\n",
+       "      <td>-0.063792</td>\n",
+       "      <td>-0.167111</td>\n",
+       "      <td>0.544916</td>\n",
+       "      <td>0.985534</td>\n",
+       "      <td>0.653169</td>\n",
+       "      <td>0.746518</td>\n",
+       "      <td>0.074853</td>\n",
+       "      <td>0.032274</td>\n",
+       "      <td>0.012141</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.44</td>\n",
+       "      <td>-0.56</td>\n",
+       "      <td>-0.48</td>\n",
+       "      <td>-0.496954</td>\n",
+       "      <td>-0.499906</td>\n",
+       "      <td>-0.258896</td>\n",
+       "      <td>-0.017067</td>\n",
+       "      <td>0.154438</td>\n",
+       "      <td>0.340134</td>\n",
+       "      <td>0.296407</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4 rows Ã— 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      tBodyAcc-mean()-Y  tBodyAcc-correlation()-X,Y  \\\n",
+       "5293          -0.023285                    0.076989   \n",
+       "5294          -0.013163                   -0.104983   \n",
+       "5295          -0.026050                    0.305653   \n",
+       "5296          -0.032614                   -0.063792   \n",
+       "\n",
+       "      tBodyAcc-correlation()-X,Z  tBodyAcc-correlation()-Y,Z  \\\n",
+       "5293                   -0.490546                   -0.709003   \n",
+       "5294                   -0.429134                    0.399177   \n",
+       "5295                   -0.323848                    0.279786   \n",
+       "5296                   -0.167111                    0.544916   \n",
+       "\n",
+       "      tGravityAcc-correlation()-X,Y  tGravityAcc-correlation()-X,Z  \\\n",
+       "5293                       0.980580                      -0.996352   \n",
+       "5294                       0.945233                      -0.911415   \n",
+       "5295                       0.548432                      -0.334864   \n",
+       "5296                       0.985534                       0.653169   \n",
+       "\n",
+       "      tGravityAcc-correlation()-Y,Z  tBodyAccJerk-mean()-X  \\\n",
+       "5293                      -0.960117               0.072046   \n",
+       "5294                      -0.738535               0.070181   \n",
+       "5295                       0.590302               0.069368   \n",
+       "5296                       0.746518               0.074853   \n",
+       "\n",
+       "      tBodyAccJerk-mean()-Y  tBodyAccJerk-mean()-Z  ...  \\\n",
+       "5293               0.045754              -0.106043  ...   \n",
+       "5294              -0.017876              -0.001721  ...   \n",
+       "5295              -0.004908              -0.013673  ...   \n",
+       "5296               0.032274               0.012141  ...   \n",
+       "\n",
+       "      fBodyAccJerk-maxInds-X  fBodyAccJerk-maxInds-Y  fBodyAccJerk-maxInds-Z  \\\n",
+       "5293                   -0.52                    0.08                    0.32   \n",
+       "5294                   -0.16                   -0.32                   -0.40   \n",
+       "5295                   -0.64                   -0.40                   -0.44   \n",
+       "5296                   -0.44                   -0.56                   -0.48   \n",
+       "\n",
+       "      fBodyGyro-meanFreq()-X  fBodyGyro-meanFreq()-Y  fBodyGyro-meanFreq()-Z  \\\n",
+       "5293                0.184035               -0.059323                0.438107   \n",
+       "5294                0.018109               -0.227266               -0.151698   \n",
+       "5295               -0.479145               -0.210084                0.049310   \n",
+       "5296               -0.496954               -0.499906               -0.258896   \n",
+       "\n",
+       "      angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
+       "5293                     0.006462                              0.162920   \n",
+       "5294                    -0.083495                              0.017500   \n",
+       "5295                    -0.034956                              0.202302   \n",
+       "5296                    -0.017067                              0.154438   \n",
+       "\n",
+       "      angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \n",
+       "5293                         -0.825886                              0.271151  \n",
+       "5294                         -0.434375                              0.920593  \n",
+       "5295                          0.064103                              0.145068  \n",
+       "5296                          0.340134                              0.296407  \n",
+       "\n",
+       "[4 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# recap: print first rows of predictors (here: training data without predicted column \"Survived\")\n",
+    "X_test.head(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5293    STANDING\n",
+       "5294    STANDING\n",
+       "5295    STANDING\n",
+       "5296    STANDING\n",
+       "Name: Activity, dtype: object"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# recap: print first rows of training data\n",
+    "data_test['Activity'].head(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['LAYING', 'STANDING', 'STANDING', 'STANDING'], dtype='<U8')"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_model.predict(X_test.head(4))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-->final_model works and can be deplyoed"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "aab7ff84f4433dd8b68de441cd3c658d57659112bcb62d3bd6aa325045009f13"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "title": "Analysis of the movement and activity of free-ranging cattle",
+  "category": "Agriculture"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CRM/Customer Churn Prediction/notebook.ipynb b/CRM/Customer Churn Prediction/notebook.ipynb
index 40e62a164b31ec807c321e97bfff7336d83f0989..12ee14d6c715ded47131ccfe846d8b60085d1f42 100644
--- a/CRM/Customer Churn Prediction/notebook.ipynb	
+++ b/CRM/Customer Churn Prediction/notebook.ipynb	
@@ -1,3784 +1,3838 @@
 {
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# 1. Business Understanding\n",
-                "\n",
-                "Kundenabwanderung ist die Entscheidung eines Kunden, eine bestimmte Unternehmensdienstleistung nicht mehr zu kaufen. Sie stellt somit das Gegenst\u00fcck zur langfristigen Kundenbindung dar. Um die Kundenbindung zu f\u00f6rdern, m\u00fcssen Unternehmen Analysen einsetzen, die fr\u00fchzeitig erkennen, ob ein Kunde das Unternehmen verlassen will. So k\u00f6nnen Marketing- und Vertriebsma\u00dfnahmen eingeleitet werden, bevor es zum eigentlichen Kundenverlust kommt. In diesem Zusammenhang beantwortet der Service konkret diese beiden Fragen: Wie hoch ist die Wahrscheinlichkeit, dass anhand historischer Daten vorhergesagt werden kann, ob ein Kunde zu einem anderen Anbieter abwandert? Welche Faktoren f\u00fchren zur Kundenabwanderung?"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# 2. Daten und Datenverst\u00e4ndnis\n",
-                "\n",
-                "Zur Visualisierung und Implementierung des Dienstes wird der Datensatz eines fiktiven Telekommunikationsunternehmens verwendet. Dieser besteht aus 7.043 Zeilen. Jede Zeile beschreibt einen Kunden mit 21 Spalten. Jede Spalte definiert verschiedene Merkmale (Attribute) der Kunden. Anhand der Daten soll klassifiziert werden, ob ein Kunde das Unternehmen verl\u00e4sst oder nicht. Zu diesem Zweck enthalten die historischen Daten die Zielvariable \"Churn\", die Auskunft dar\u00fcber gibt, ob ein Kunde abgewandert ist oder nicht.ot."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 2.1. Import von relevanten Modulen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 141,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import pandas as pd\n",
-                "import numpy as np\n",
-                "import matplotlib.pyplot as plt\n",
-                "import seaborn as sns\n",
-                "import statsmodels.api as sm\n",
-                "import warnings\n",
-                "import imblearn\n",
-                "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
-                "from sklearn.preprocessing import StandardScaler\n",
-                "from sklearn.linear_model import LogisticRegression\n",
-                "from imblearn.under_sampling import InstanceHardnessThreshold\n",
-                "from sklearn import metrics\n",
-                "from sklearn.metrics import classification_report, confusion_matrix\n",
-                "from sklearn.model_selection import train_test_split\n",
-                "sns.set()\n",
-                "# remove warnings\n",
-                "warnings.filterwarnings('ignore')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 2.2. Daten einlesen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 142,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>customerID</th>\n",
-                            "      <th>gender</th>\n",
-                            "      <th>SeniorCitizen</th>\n",
-                            "      <th>Partner</th>\n",
-                            "      <th>Dependents</th>\n",
-                            "      <th>tenure</th>\n",
-                            "      <th>PhoneService</th>\n",
-                            "      <th>MultipleLines</th>\n",
-                            "      <th>InternetService</th>\n",
-                            "      <th>OnlineSecurity</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>DeviceProtection</th>\n",
-                            "      <th>TechSupport</th>\n",
-                            "      <th>StreamingTV</th>\n",
-                            "      <th>StreamingMovies</th>\n",
-                            "      <th>Contract</th>\n",
-                            "      <th>PaperlessBilling</th>\n",
-                            "      <th>PaymentMethod</th>\n",
-                            "      <th>MonthlyCharges</th>\n",
-                            "      <th>TotalCharges</th>\n",
-                            "      <th>Churn</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>7590-VHVEG</td>\n",
-                            "      <td>Female</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No phone service</td>\n",
-                            "      <td>DSL</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Month-to-month</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>Electronic check</td>\n",
-                            "      <td>29.85</td>\n",
-                            "      <td>29.85</td>\n",
-                            "      <td>No</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>5575-GNVDE</td>\n",
-                            "      <td>Male</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>34</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>DSL</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>One year</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Mailed check</td>\n",
-                            "      <td>56.95</td>\n",
-                            "      <td>1889.5</td>\n",
-                            "      <td>No</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>3668-QPYBK</td>\n",
-                            "      <td>Male</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>DSL</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Month-to-month</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>Mailed check</td>\n",
-                            "      <td>53.85</td>\n",
-                            "      <td>108.15</td>\n",
-                            "      <td>Yes</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>7795-CFOCW</td>\n",
-                            "      <td>Male</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>45</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No phone service</td>\n",
-                            "      <td>DSL</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>One year</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Bank transfer (automatic)</td>\n",
-                            "      <td>42.30</td>\n",
-                            "      <td>1840.75</td>\n",
-                            "      <td>No</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>9237-HQITU</td>\n",
-                            "      <td>Female</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Fiber optic</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Month-to-month</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>Electronic check</td>\n",
-                            "      <td>70.70</td>\n",
-                            "      <td>151.65</td>\n",
-                            "      <td>Yes</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>5 rows \u00d7 21 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
-                            "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
-                            "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
-                            "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
-                            "3  7795-CFOCW    Male              0      No         No      45           No   \n",
-                            "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
-                            "\n",
-                            "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
-                            "0  No phone service             DSL             No  ...               No   \n",
-                            "1                No             DSL            Yes  ...              Yes   \n",
-                            "2                No             DSL            Yes  ...               No   \n",
-                            "3  No phone service             DSL            Yes  ...              Yes   \n",
-                            "4                No     Fiber optic             No  ...               No   \n",
-                            "\n",
-                            "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
-                            "0          No          No              No  Month-to-month              Yes   \n",
-                            "1          No          No              No        One year               No   \n",
-                            "2          No          No              No  Month-to-month              Yes   \n",
-                            "3         Yes          No              No        One year               No   \n",
-                            "4          No          No              No  Month-to-month              Yes   \n",
-                            "\n",
-                            "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
-                            "0           Electronic check          29.85         29.85    No  \n",
-                            "1               Mailed check          56.95        1889.5    No  \n",
-                            "2               Mailed check          53.85        108.15   Yes  \n",
-                            "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
-                            "4           Electronic check          70.70        151.65   Yes  \n",
-                            "\n",
-                            "[5 rows x 21 columns]"
-                        ]
-                    },
-                    "execution_count": 142,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "data_raw = pd.read_csv(\"https://storage.googleapis.com/ml-service-repository-datastorage/Customer_Churn_Prediction_data.csv\")\n",
-                "data_raw.head()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 143,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "<class 'pandas.core.frame.DataFrame'>\n",
-                        "RangeIndex: 7043 entries, 0 to 7042\n",
-                        "Data columns (total 21 columns):\n",
-                        " #   Column            Non-Null Count  Dtype  \n",
-                        "---  ------            --------------  -----  \n",
-                        " 0   customerID        7043 non-null   object \n",
-                        " 1   gender            7043 non-null   object \n",
-                        " 2   SeniorCitizen     7043 non-null   int64  \n",
-                        " 3   Partner           7043 non-null   object \n",
-                        " 4   Dependents        7043 non-null   object \n",
-                        " 5   tenure            7043 non-null   int64  \n",
-                        " 6   PhoneService      7043 non-null   object \n",
-                        " 7   MultipleLines     7043 non-null   object \n",
-                        " 8   InternetService   7043 non-null   object \n",
-                        " 9   OnlineSecurity    7043 non-null   object \n",
-                        " 10  OnlineBackup      7043 non-null   object \n",
-                        " 11  DeviceProtection  7043 non-null   object \n",
-                        " 12  TechSupport       7043 non-null   object \n",
-                        " 13  StreamingTV       7043 non-null   object \n",
-                        " 14  StreamingMovies   7043 non-null   object \n",
-                        " 15  Contract          7043 non-null   object \n",
-                        " 16  PaperlessBilling  7043 non-null   object \n",
-                        " 17  PaymentMethod     7043 non-null   object \n",
-                        " 18  MonthlyCharges    7043 non-null   float64\n",
-                        " 19  TotalCharges      7043 non-null   object \n",
-                        " 20  Churn             7043 non-null   object \n",
-                        "dtypes: float64(1), int64(2), object(18)\n",
-                        "memory usage: 1.1+ MB\n"
-                    ]
-                }
-            ],
-            "source": [
-                "data_raw.info()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Der Datensatz besteht aus 7.043 Zeilen und 21 Attributen:\n",
-                "\n",
-                "- Zu prognostizierendes Attribut: Abwanderung\n",
-                "- Numerische Attribute: Vertragsdauer, Monatsgeb\u00fchren und Gesamtgeb\u00fchren.\n",
-                "- Kategorische Attribute: CustomerID, Gender, SeniorCitizen, Partner, Angeh\u00f6rige, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod.\n",
-                "Es wurden nicht alle Datentypen korrekt eingelesen:\n",
-                "\n",
-                "- TotalCharges muss ein numerischer Wert sein -> in Float umwandeln\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 144,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>customerID</th>\n",
-                            "      <th>gender</th>\n",
-                            "      <th>SeniorCitizen</th>\n",
-                            "      <th>Partner</th>\n",
-                            "      <th>Dependents</th>\n",
-                            "      <th>tenure</th>\n",
-                            "      <th>PhoneService</th>\n",
-                            "      <th>MultipleLines</th>\n",
-                            "      <th>InternetService</th>\n",
-                            "      <th>OnlineSecurity</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>DeviceProtection</th>\n",
-                            "      <th>TechSupport</th>\n",
-                            "      <th>StreamingTV</th>\n",
-                            "      <th>StreamingMovies</th>\n",
-                            "      <th>Contract</th>\n",
-                            "      <th>PaperlessBilling</th>\n",
-                            "      <th>PaymentMethod</th>\n",
-                            "      <th>MonthlyCharges</th>\n",
-                            "      <th>TotalCharges</th>\n",
-                            "      <th>Churn</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>0 rows \u00d7 21 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "Empty DataFrame\n",
-                            "Columns: [customerID, gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges, Churn]\n",
-                            "Index: []\n",
-                            "\n",
-                            "[0 rows x 21 columns]"
-                        ]
-                    },
-                    "execution_count": 144,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# test for duplicates\n",
-                "data_raw[data_raw.duplicated(keep=False)]"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Keine Duplikate im Datensatz"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 2.3. Datenbereinigung"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Hier sollten die ersten Lesefehler korrigiert werden, bevor die eigentliche Datenaufbereitung erfolgt."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 145,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "<class 'pandas.core.frame.DataFrame'>\n",
-                        "RangeIndex: 7043 entries, 0 to 7042\n",
-                        "Data columns (total 21 columns):\n",
-                        " #   Column            Non-Null Count  Dtype  \n",
-                        "---  ------            --------------  -----  \n",
-                        " 0   customerID        7043 non-null   object \n",
-                        " 1   gender            7043 non-null   object \n",
-                        " 2   SeniorCitizen     7043 non-null   int64  \n",
-                        " 3   Partner           7043 non-null   object \n",
-                        " 4   Dependents        7043 non-null   object \n",
-                        " 5   tenure            7043 non-null   int64  \n",
-                        " 6   PhoneService      7043 non-null   object \n",
-                        " 7   MultipleLines     7043 non-null   object \n",
-                        " 8   InternetService   7043 non-null   object \n",
-                        " 9   OnlineSecurity    7043 non-null   object \n",
-                        " 10  OnlineBackup      7043 non-null   object \n",
-                        " 11  DeviceProtection  7043 non-null   object \n",
-                        " 12  TechSupport       7043 non-null   object \n",
-                        " 13  StreamingTV       7043 non-null   object \n",
-                        " 14  StreamingMovies   7043 non-null   object \n",
-                        " 15  Contract          7043 non-null   object \n",
-                        " 16  PaperlessBilling  7043 non-null   object \n",
-                        " 17  PaymentMethod     7043 non-null   object \n",
-                        " 18  MonthlyCharges    7043 non-null   float64\n",
-                        " 19  TotalCharges      7032 non-null   float64\n",
-                        " 20  Churn             7043 non-null   object \n",
-                        "dtypes: float64(2), int64(2), object(17)\n",
-                        "memory usage: 1.1+ MB\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# convert total charges\n",
-                "data_raw['TotalCharges'] = pd.to_numeric(data_raw['TotalCharges'], errors='coerce')\n",
-                "data_raw.info()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Die Konvertierung der TotalCharges hat zu Nullwerten gef\u00fchrt. Diese Nullwerte m\u00fcssen korrigiert werden."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 146,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Remove zero values\n",
-                "# axis = 0 rows / axis = 1 columns\n",
-                "data_no_mv = data_raw.dropna(axis=0)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 2.4. Deskriptive Analytik"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "In diesem Teil des Notebooks soll das Datenverst\u00e4ndnis mit Hilfe der deskriptiven Analytik ber\u00fccksichtigt werden. Nach dem Entfernen der Nullwerte besteht der Datensatz aus 7032 Zeilen, von denen jeweils eine einen Kunden beschreibt, und 21 Spalten, die die Attribute des Kunden definieren. Mit Hilfe dieser Daten soll versucht werden, zu klassifizieren, ob ein Kunde abwandert oder nicht. Zu diesem Zweck enthalten die historischen Daten die Zielvariable \"Churn\", die Auskunft dar\u00fcber gibt, ob ein Kunde abgewandert ist.\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 2.4.1. Kontinuierliche Merkmale"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Zun\u00e4chst werden die Verteilungen der kontinuierlichen Merkmale einzeln untersucht und in einem zweiten Schritt die kategorialen Merkmale in Zusammenhang mit der Zielvariablen gesetzt."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 147,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# load continous features\n",
-                "numeric_data = data_no_mv.select_dtypes(include=[np.number])"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Besitz"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 148,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<seaborn.axisgrid.FacetGrid at 0x23e942418e0>"
-                        ]
-                    },
-                    "execution_count": 148,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 360x360 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.displot(numeric_data[\"tenure\"])"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "- Keine Normalverteilung erkennbar.\n",
-                "- Keine Ausrei\u00dfer erkennbar.\n",
-                "- Kunden sind potentiell gleichm\u00e4\u00dfig \u00fcber die einzelnen Monate verteilt, aber eine gro\u00dfe Anzahl von Kunden ist noch nicht lange im Unternehmen.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 149,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:xlabel='tenure', ylabel='Density'>"
-                        ]
-                    },
-                    "execution_count": 149,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"tenure\"],\n",
-                "             bins=10,\n",
-                "             color='orange',\n",
-                "             label='Non-Churn',\n",
-                "             kde=True)\n",
-                "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"tenure\"],\n",
-                "             bins=10,\n",
-                "             color='blue',\n",
-                "             label='Churn',\n",
-                "             kde=True)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Kunden, die noch nicht lange bei dem Unternehmen sind, werden eher abwandern als langj\u00e4hrige Kunden."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Monatliche Kosten"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 150,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:xlabel='MonthlyCharges', ylabel='Density'>"
-                        ]
-                    },
-                    "execution_count": 150,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.distplot(numeric_data[\"MonthlyCharges\"])"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "- Es ist keine Normalverteilung erkennbar.\n",
-                "- Die meisten Kunden befinden sich im vorderen Teil der Verteilung und zahlen relativ niedrige monatliche Geb\u00fchren.\n",
-                "- Dennoch verl\u00e4uft die Kurve gleichm\u00e4\u00dfig mit einem erneuten Anstieg nach hinten und dementsprechend k\u00f6nnen keine Ausrei\u00dfer identifiziert werden.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 151,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:xlabel='MonthlyCharges', ylabel='Density'>"
-                        ]
-                    },
-                    "execution_count": 151,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"MonthlyCharges\"],\n",
-                "             bins=10,\n",
-                "             color='orange',\n",
-                "             label='Non-Churn',\n",
-                "             kde=True)\n",
-                "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"MonthlyCharges\"],\n",
-                "             bins=10,\n",
-                "             color='blue',\n",
-                "             label='Churn',\n",
-                "             kde=True)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "- Kunden mit niedrigen monatlichen Geb\u00fchren sind eher abwanderungsbereit.\n",
-                "- Der Abwanderungstrend zwischen Kunden, die abwandern, und Kunden, die nicht abwandern, gleicht sich an, wenn die monatlichen Geb\u00fchren steigen.\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Gesamtkosten"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 152,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
-                        ]
-                    },
-                    "execution_count": 152,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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yrFarf9lms7U61XbmdqvVisPhoLq6GrPZjF6vb7X+zH30ej1ms5mqqiq8Xi9/+tOf+NOf/kR6enpHShJCCHGJdChovF7vWSEzd+5cXnjhBW688cZz7uPz+dBoNP5lRVFaLbe1/cx2wFnL398HYMGCBTz++OMEBwd3pJyzREWZL2i/7sBkMmIxt1+3yWTEGmm6RD26uKxWS1d3QTWBXBsEdn1SW+e0GzRPPPEEDoeDXbt2UVVV5V/v8XgoKipq98AxMTHs3LnTv+x0OrHZbK22f/9ifkVFBTabjcjISGpra/F6veh0ulb72Ww2KioqiImJwePx4HK5qK6u5vDhwyxYsACA48ePs3DhQp566inGjRvXoW9CZWUdPp/SobbdidVqob6+idq6xnbb1dc34fR6L1GvLh6r1YLTWdvV3VBFINcGgV2f1AZaraZTH9DbDZpp06Zx4MAB9u3b579mAqDT6fzXVtoyfvx4XnzxRaqqqggJCWHLli089dRT/u1xcXEYjUZ27drF9ddfT3Z2NomJiRgMBux2O7m5uaSkpJCVlUViYiIASUlJZGVlMXv2bHJzc7Hb7QwbNoyPP/7Yf9x77rmHBx98kLFjx3b4myCEEEI97QZNQkICCQkJ/OhHPyI6OrpTB46Ojubhhx9m5syZuN1upk2bxsiRI0lPT2fu3LkkJCSQmZnJwoULqaurIz4+npkzZwItI6mMjAxefvllYmNjWbZsGQDz5s0jIyODKVOmYLFYyMzMvMCyhRBCXCoa5fSFjnOYN28ezz//vP9OrzPl5OSo1rFLqSefOjtaXE1BoaPddqOHRxNq7NDluG5FTlH0XIFcn9R2kU+dnb6D6/HHH+/wAYUQQojva/c5mhEjRgAwZswYYmNjGTNmDPX19RQUFDB8+PBL0kEhhBA9W4ce2Fy0aBF//OMfOXToEAsXLqS4uJjHHntM7b4JIYQIAB0Kmj179vCb3/yGDz74gDvvvJOlS5dSUlKidt+EEEIEgA4FjaIoaLVaPvvsM/+zKY2N7T+7IYQQQkAHg6Z///6kp6dTXFzMmDFj+J//+R+ZU0wIIUSHdOie16VLl/LBBx9w/fXX+x+ovOOOO1TumhBCiEDQoRGNyWTCbrdTU1PDN998w8iRIzl8+LDafRNCCBEAOjSief7553njjTeIioryr9NoNHz44YeqdUwIIURg6FDQZGdns2XLlk5PQyOEEEJ06NRZbGyshIwQQogL0qERzQ033MCzzz7LT37yk1bvfImPj1etY+KHcXt87DteTXVtE0XldUwa05/eESFd3S0hxGWoQ0GzYcMGgFavYpZrNN2Xq9HNR7tKqK5tIjRYz3FHHR9/Ucp9k4Zx48jYru6eEOIy06Gg+eijj9Tuh7hIvD4ffysopr7Jw632vvTpHcqQvhG8+7f9vJlbCCBhI4S4pDp0jcblcvHkk09y7733cvLkSRYtWoTL5VK7b+IC7DlcxSlXM4nX9KFP71AAelmMzP23kQwf2Is/5e3luCMwpzgXQnRPHQqap59+GovFQmVlJUajkbq6OhYtWqR230Qn1bia2X2oioGxFuKsoa22BRl0/Ndt8ZhNBl7N+ZZmd897tbMQomfqUNAUFhby8MMPo9frCQkJITMzk8LCQrX7Jjrp26NVoAH7VbZzbreYgnhgynBKK1y8v/3YJe6dEOJy1aGg0WpbN/N6vWetE12r2e3lcGkNg2ItmILbvvQ2YlAUY6+OZvPnx6k42XAJeyiEuFx1KC1Gjx7N73//exobG/nkk0948MEHGTt2rNp9E51wsOQUHq/CsP69ztt2+k1XoAHW5R9Sv2NCiMteh4LmkUcewWQyYbFYWLFiBcOGDeNXv/qV2n0THaQoCvuLTmGNCCYqPPi87SPDgpk4tj8795Zz7ITcGCCEUNd5g+aDDz7gnnvu4bXXXqO4uBiLxcJ1112H0Wi8FP0THXCyrpkaVzNXxIV3eJ8Jo/sRYtTz18+OqNgzIYQ4T9Bs2rSJ3//+9/z85z/nL3/5C++88w533HEHS5YsYcuWLec9eE5ODpMnT2bChAmsXr36rO2FhYWkpqaSnJzMggUL8Hg8AJSWlpKWlsbEiROZM2eO/1bqmpoaZs2axaRJk0hLS8PpdAJQXl7Offfdx2233cb06dMvuxsVjp2oRQP0s5k7vI8p2EDy6H58caBCRjVCCFW1GzR//vOfeeutt5g6dSpDhgzhiiuu4I477mDVqlW8+eab7R7Y4XCwfPly1qxZQ1ZWFmvXruXgwYOt2syfP59FixaxefNmFEVh3bp1ACxevJgZM2aQl5fHiBEjWLlyJQArVqzAbrezadMmpk+fzpIlSwBYvnw5ycnJ/PWvf+WXv/wlixcvvuBvSE90zFGLLTKEEGOHnr/1u9XeD5NRT/anMqoRQqin3aBxuVz06dPnrPWDBg2iqamp3QNv27aNcePGERERgclkIjk5udUUNiUlJTQ2NjJq1CgAUlNTycvLw+12U1BQQHJycqv1APn5+aSkpAAwdepUtm7ditvtZsmSJdx1110AFBcXExYW1sHye74TlfWcqmtmQLSl0/uagvUkj+nHlwcrOHqiRoXeCSHEeYJGp9O1uU1RlHYPXF5ejtVq9S/bbDYcDkeb261WKw6Hg+rqasxmM3q9vtX6M/fR6/WYzWaqqqrQarVotVomTpzI0qVLueeee9rtWyDZc7gSgP7RHT9t9n232vsRGqwn+xMZ1Qgh1NG5cy2d4PP50Gg0/mVFUVott7X9zHbAWcvf3+f7z/Pk5eVRWFjI/fffz6ZNm4iIiOhQX6OiLuyHdHdwoOQUkWHBRPdue0QTZDSg6M79mSLYpGPC2AFs/PgQpScbuWao9ZztuorV2vmRWk8RyLVBYNcntXVOu0Gzb98+rrvuurPWK4pCc3NzuweOiYlh586d/mWn04nNZmu1/fTFfICKigpsNhuRkZHU1tbi9XrR6XSt9rPZbFRUVBATE4PH48HlchEREUF+fj6jR48mNDSU4cOH06dPH4qKijocNJWVdfh87Y/QuqOwCBP7j1cztG8EtXWNbbarq2/iq/3ONrcbDRqC9FrWfrCPPhHnvz36UrFaLTidgXmjQiDXBoFdn9QGWq2mUx/Q2w2aDz74oMMHOtP48eN58cUXqaqqIiQkhC1btvDUU0/5t8fFxWE0Gtm1axfXX3892dnZJCYmYjAYsNvt5ObmkpKSQlZWFomJiQAkJSWRlZXF7Nmzyc3NxW63YzAY2LhxI0eOHOE//uM/OHjwIBUVFQwePPiC+95TfHO4Eo9XoU9v0w86TpBex/CBvfjqYCXHHbX0v4DrPUII0RaNcr6LLT9ATk4Oq1atwu12M23aNNLT00lPT2fu3LkkJCSwd+9eFi5cSF1dHfHx8SxdupSgoCBKSkrIyMigsrKS2NhYli1bRnh4OCdPniQjI4OioiIsFguZmZn07dsXh8PBY489htPpxGg08utf/xq73d7hfvbUEU3OP46T88khpt88BIO+7ctt11xpbXdEAy1T2GR/eoSrB0Tyi9SEi93VCyKfHHuuQK5Pauv8iEbVoOkpemrQPPXnnei1Gm4YEdNuu44EDUB5dQN5O46z+P4xnXomRy3yH7rnCuT6pLbOB43MjNlDuRrdHC2r4cp+ERftmDddF0dwkI6cbUcv2jGFEEKCpoc6WHwKRaFT086cT2iwgZ9c35dde8spcdZdtOMKIS5vEjQ91IHiU+i0GgbEXNwL98lj+hNkkFGNEOLikaDpoQ4Un2RI3wiCDG0/VHshzCEGbrk+joLCcsoq5XXdQogfToKmB3J7vBwpq+HqwVGqHD95TH8MBi0bZbYAIcRFIEHTAx0pq8XjVbh6UKQqxw8zBTFxTMv7ag6WnFLlawghLh8SND3Q6R/+wweqEzQAE8f2Jzw0iHUfHTzvvHZCCNEeCZoe6EhZDdaIYMLN6r18LjhIzx0/HsTBklPs2nf+Z3CEEKItEjQ90NGyWgbFqv8qhBtHxhLXO5T1+YfweH2qfz0hRGCSoOlhauqbqaxpZGCM+kGj02qZfvMQyk828NGuYtW/nhAiMEnQ9DBHy1qmhxgUe2kmvkwYHMmIwZFs/PQIVTVtzxAthBBtkaDpYY6eqEEDl2yGZY1Gwz0TrkLxKaz+YP8l+ZpCiMAiQdPDHC2rJSbKRIhRtXfWncUaEcLtNw7iiwMVcmOAEKLTJGh6mCMnalS7PqPRanA1ec7560cjY4mzhvLOln3UNHhU+fpCiMB06T4Wix+suraJU3XNDFTp+kyT29vu6wSuuSKK3H8c5y8fHeCBKcNV6YMQIvDIiKYHOVpWA3BJbm0+l94RIVw9sBef7S7jq4MVXdIHIUTPI0HTgxw5UYNWo+nSl5Jde2Vv+vQO5c3cQmpczV3WDyFEzyFB04McLaslzhqK8SLP2NwZOq2W+yYNo77Jyxu5hTI9jRDivCRoeghFUTh6opaBF/n9Mxcitnco02++gq8PVfL3L0q6ujtCiG5ObgboISpONVLX4GZgF12f+T6NVsO4ETF8ebCCdz88QJ/eofQ747keo0GPXj7GCCGQoOkxjp64tDMCtOf03WkJgyM5dqKWlRv3MGX8gFan9EYPj0Z/CZ/1EUJ0X6p+5szJyWHy5MlMmDCB1atXn7W9sLCQ1NRUkpOTWbBgAR5Py/MZpaWlpKWlMXHiRObMmYPL1fKmx5qaGmbNmsWkSZNIS0vD6Wy5Fbe8vJwHHniA22+/nTvvvJPt27erWVaXOFJWg16noa+1624EOFNwkJ6kUX2ob3Tz2ddlcr1GCHFOqgWNw+Fg+fLlrFmzhqysLNauXcvBgwdbtZk/fz6LFi1i8+bNKIrCunXrAFi8eDEzZswgLy+PESNGsHLlSgBWrFiB3W5n06ZNTJ8+nSVLlgDw7LPPcsstt5Cdnc1zzz3HI488gtfrVau0LnG0rIZ+NjN6Xfc6H2WNCOH6YTaKnS72HKnq6u4IIboh1X5qbdu2jXHjxhEREYHJZCI5OZm8vDz/9pKSEhobGxk1ahQAqamp5OXl4Xa7KSgoIDk5udV6gPz8fFJSUgCYOnUqW7duxe1289Of/pSpU6cCMGDAAJqamqivr1ertEvOpygcc9RekhmbL8Sw/hEMiLHw5f4KTlQGzvddCHFxqBY05eXlWK1W/7LNZsPhcLS53Wq14nA4qK6uxmw2o9frW60/cx+9Xo/ZbKaqqork5GTCw8MBeP311xk+fDgWS9dfy7hYHFX1NDR5VZsR4IfSaDSMHxFDWGgQW78qpb5RpqgRQvyLaldrfT4fGo3Gv6woSqvltraf2Q44a/n7+2i1/8rKt956i7Vr1/LOO+90qq9RUd3nuse5fHP8JADXXR2L1do6bEwmIxZzcLv7Gwz687a5GO0mjR/E+o8O8OnXZdw6diDWi3A96cx6A0kg1waBXZ/U1jmqBU1MTAw7d+70LzudTmw2W6vtpy/mA1RUVGCz2YiMjKS2thav14tOp2u1n81mo6KigpiYGDweDy6Xi4iICKDlOs3HH3/M6tWriYmJ6VRfKyvr8Pm674Xsr/aXE6TXEqxVcDpr/eutVgv19U3U1rX/nhi323PeNhejXZAOxifEsPXLUt7O/fYHz4dmtVpa1RtIArk2COz6pDbQajWd+oCu2qmz8ePHs337dqqqqmhoaGDLli0kJib6t8fFxWE0Gtm1axcA2dnZJCYmYjAYsNvt5ObmApCVleXfLykpiaysLAByc3Ox2+0YDAbeeustduzYwf/+7/92OmR6gqMnaukfY0Gn7V43ApzLwBgL8YMi+Wx3GR9/KQ9zCiFUHNFER0fz8MMPM3PmTNxuN9OmTWPkyJGkp6czd+5cEhISyMzMZOHChdTV1REfH8/MmTMBeOKJJ8jIyODll18mNjaWZcuWATBv3jwyMjKYMmUKFouFzMxMFEXhpZdewmw2c8899/i//quvvkp0dLRa5V0yXp+P4ydqSRzVp6u70mHXXtkb73cvSutrNXNFXHhXd0kI0YU0ijz80K1PnRWX17Hojc9JT7maG+Jbj9asVgtHi6spKHS0sXeLa660tjv9vxrtrh4USeaaL/B4fTxx32jCzcbzHvdMcoqi5wrk+qS2bnTqTFwcR757NUB3mOOsM0KDDTyYmkB9o4eVWXvweH1d3SUhRBeRoOnmjp6oJcSoIzrS1NVd6bT+0RbumzyMA8WnePfDA13dHSFEF5HJqLq5I2U1DIi2oG3jFu/ubtzVMRw7Ucvmz4sYGBPGjSNju7pLQohLTEY03Zjb46OovK5bzNj8Q0y76QqGD+jFnzfv858KFEJcPiRourGSijq8PqXLXt18sei0WmbfHk94aBAvbdwtb+YU4jIjQdONHSlrufujp90IAC3vrHE1efy/tDotD0wdTq3LzR827qamvhlXkweP3CMgRMCTazTd2JGyGswhBnqHn39amO7m9DtrzjTmahuf7T7By1l7GHt1tLy3RojLgIxourHDpTUM7hPW5lxvPdEVceFcPbAX+46fpPBYdVd3RwhxCUjQdFP1jR7KKlwM7tOzr8+cy3VXWelrM7OzsJxv5B02QgQ8CZpu6siJGhQIyKDRajT8eGQsERYjb+UWUlxe19VdEkKoSIKmmzpc2nIb8OAefsdZWwx6LbdcH4fRoOP59V9xqq6pq7skhFCJBE03dbjkFLFRJkzBhq7uimpCgw3Muj2e2gY3L7y3m6bmwHr9thCihQRNN6QoCofLagLytNmZ+kdbmJUSz9ETNfxh427ccr+zEAFHgqYbcp5qpLbezeA+l8f0+tddaeW+icP45kgVf8z5ptvOpC2EuDASNN3Q4dJTAFxxGYxoTvvxNX2465Yh7Nzn5E95e/HJ2yuECBjypFw3dLikhiCDljhraFd35ZJKHtOf+kYPOduOogCP3DO6q7skhLgIJGi6ocNlNQyMCesRr27+oU5PVXPaT8f0w+31kbfjOM1v7mDGrUMxBQehD/xvhRABS4Kmm3F7fBx31PJTe7+u7solca6pamy9QrjuKiuff+ugtMLFw3ddQ6/Qzr+hUwjRPcjnxG7meHktHq9yWdxx1p4RgyJJvDaO4vI6Vqz9iupaec5GiJ5KgqabOVzy3YOal8kdZ+1JuKI3N18XR3l1A0++VcCh726SEEL0LBI03cyh0lP0shjpZZFTRQD9bGb+5+5RBBm0/G71F3yws0juSBOih5Gg6UYURWHf8ZNc1S+iq7vSrcT2DuXxe0dz9cBe/O/fDvDcu19SVdPY1d0SQnSQqkGTk5PD5MmTmTBhAqtXrz5re2FhIampqSQnJ7NgwQI8npa7j0pLS0lLS2PixInMmTMHl8sFQE1NDbNmzWLSpEmkpaXhdLa+iPzZZ59x7733qlmSqk5U1XPK1cyV/SO6uivdjjnEwLxpI7l34lUcLq3h8dc/58NdxXh9MpOAEN2dakHjcDhYvnw5a9asISsri7Vr13Lw4MFWbebPn8+iRYvYvHkziqKwbt06ABYvXsyMGTPIy8tjxIgRrFy5EoAVK1Zgt9vZtGkT06dPZ8mSJQD4fD7eeOMN/t//+3/4evAPnn1FJwFkRNMGjUZD0qg4fnP/aAZEm1n9wX6eeKOAPYcru7prQoh2qBY027ZtY9y4cURERGAymUhOTiYvL8+/vaSkhMbGRkaNGgVAamoqeXl5uN1uCgoKSE5ObrUeID8/n5SUFACmTp3K1q1bcbvdHDp0iEOHDvHUU0+pVc4lsf/4ScJCg4iJNHV1V7q16F4m5v/7tfwyNQGPx8eydV+x9J1dfHmwQq7fCNENqfYcTXl5OVar1b9ss9n4+uuv29xutVpxOBxUV1djNpvR6/Wt1p+5j16vx2w2U1VVxdChQ1myZAk7duy4oL5GRZkvaL+LSVEUDpScYuSQ3thsHb+12WQyYjG3/6png0F/3jYXu93FOpbFHEyQ0YCiO/sz0ajhMcQPtbH1i2I27zjGC+u/pn+Mhck3DCTxur5YTEHn/fpdyWq1dHUXVBXI9UltnaNa0Ph8vlavIFYUpdVyW9vPbAe0+SpjRVHQXoSn5ysr67p8IkdHdT2VpxoZGG3G6azt0D5Wq4X6+iZq69q/MO52e87b5mK3uxjHspiDqa1rpK6+6ayHOr9Pr4VJY/uj1WrI/2cJr2zczWt/3cOooVbGDLMRPyiSEGP3ejbZarV0+O+5Jwrk+qQ20Go1nfqArtr/vpiYGHbu3Olfdjqd2Gy2Vtu/fzG/oqICm81GZGQktbW1eL1edDpdq/1sNhsVFRXExMTg8XhwuVxERESoVcIldfqVxlcPjOzinvRMWq2G0cOjuWlUHMcdtXz6dRn/+NbBzr3l6HUahg3oRfzASK7sF0H/aPNlMb2PEN2FakEzfvx4XnzxRaqqqggJCWHLli2trqHExcVhNBrZtWsX119/PdnZ2SQmJmIwGLDb7eTm5pKSkkJWVhaJiYkAJCUlkZWVxezZs8nNzcVut2MwBMaLwfYcrqJ3eDDRvUK6uis9Xv9oCzN+auGunwzhYPEpvjhQwVcHK1h7uOVmFKNBx5C4MIb0jWBAtIX+0WZ6WYxtjpyFED+MakETHR3Nww8/zMyZM3G73UybNo2RI0eSnp7O3LlzSUhIIDMzk4ULF1JXV0d8fDwzZ84E4IknniAjI4OXX36Z2NhYli1bBsC8efPIyMhgypQpWCwWMjMz1er+JeXx+ig8Xs0NV0fLD7sf4MwJOgH6RlvoG20h5cZBnKxr4lDJKY6W1XKw+CR//fQIp0+Yhgbr6WczE9fbTEyUiejIEGIiTUSGBaOVvxMhfhBVT1ynpKT47xI77Y9//KP/z8OGDWP9+vVn7RcXF8fbb7991vqIiAheeeWVNr/e2LFjGTt27A/ocdc4VHKKpmYv8YOiurorPdq5Jug8l2m3DEHxKTQ2eyitcFHsdFHqrKPY6eLT3WU0uf/1SmmDXkt0rxCsESFEhQUTGRZMVHgwkWFGosKCCQsNkiAS4jy61xXSy9SeI1VoNRqGD+jV1V25LJwZSCFBOq6IC+eKuHAURaGhyUuczYyjqh5ndQOO6npOVNVTeKyaxmZvq2PptBp6WYyEm4MIMwVhMRmwmIKwmIII++7PphADBr2W0BA9QXodAEpVPfVnjL6MBr28DkEEJAmabuCrg5UMiQvDFCx/HV1No9FgCtbTP8ZCdU0jcdZQ/wvoFEXB7fHhanTjavBQ1+gmzGSktr6ZGlcz5ScbOFRaQ129u83nefQ6DUEGHaZgAwadBqNBhzFIh9Gg48p+EURajJhDDJhNBszBLb8bDTo5pSp6NPnJ1sXKq+spdtZx90+GdnVXxHloNC0hEWTQ0eu7Rw1GD48m9Ixbp32KQn2jxx9AFTWNfHOkiqZmL01uL03NXjw+BVeDm7oGN01uL81uH18fOvcMB3qdhtAQA5YQQ0sIfffr9LrQ0+tMLb+HmYIIDpJwEt2HBE0X++f+CgCuu7J3F/dEXIhz3YBwen2Y2UiY2UgfmwW3p/XUSKefETrN51O4elAkik+hrsFNXX1LCJ3rV7HTRV2Dm/pGN209/hVk0BIeGkS42djy+xl/jjAbCQsNIizUILd6C9VJ0HSxXfvLGRBtoXe43NbcE3XkBoRrrrS2ux1angMKMxtRfAph5vZfEeFTYNdeB4qi0OzxtRopNbm9NDR7CQ8Nor7Rw6m6JkorXOw9Vo2r8RyBCFhMBsJCgwg26jHqtRj0OoIM2pbRm77ld4Neh16vISRIR3CQnuDvfg8x6gk3BxFi1Ms1JtEmCZouVF3bxKGSGu5MHNzVXRHdQEfvmjsdXBrNd9d4DLqz2oyJj0E5Y7jj9vj8p/NOuZqpdTXj+t4pvsZmL41uLzX1bprdXpo9Pv/vZ47IzmTQaYkMb7kTr3d4CEMH9MJi1BMbZSIqXG4Rv9xJ0HShf3x7AoDRw2znaSlE53QktIxBOn58bdxZgXQuHq/Cjm/K/KFz+leT20tDk4f6Rg/GIB3VtU0cdzjZ+lWpf1+DTovtu+eS+vQOZWBsOAOjzUSYg+Q60mVCgqaLKIrCZ7tPMCQuXGZrFl2mM6Oo0zdCtNfm9LH0Bj0l5TXU1LWMnk65mtl7rJpd+/71tcwhBvrZzK1+xUaFYpDzbwFHgqaLHCmrpbTCxb0Tr+rqrghx0YUY9UT3MhHdq/WHqGa3l5jeoRQ76iipcFHidPH3f5bg9racmtNqNUT3CqH/d1MDtQSQhfDQ7j0Tt2ifBE0X+XR3GUF6LaOHRXd1V4S4ZIIMOvpFW6g61cjQvuEM7RuOT1GodTVTVdtE9Xe/9h2vZse3Dv9+YSaDP3T62cz0tZmJjTKhP8frI0T3I0HTBeoa3GzfcwL7MJs8pCkue1qNpuXWa7ORQbEt68bEx1Dnav5u1FNHidNFSYWLfbuK8HhbrinpdRr69DYzINrcagQUHCT/p7ob+RvpAh9/WUKT20vymP5d3RUhuqUmt5dvj7a8OsMcYuCq/hFc1T8Cn0+hxj/6acTjVfjiQAWffF0GtNyubYs0MeC70GmZndtCmJx661ISNJeY2+PjbzuLGTEokn62rn+zpxA9iVarIcJiJMJiBMIYEx+Dz+vjZF0TxeUuip11FJfXcbDkFJ8Xlvv3izAH+Uc9/W0W+sdYsIYHy11vl4gEzSX28ZclnHI1859jZTQjxA915l1zvcOD6R0ezKihvWlq9lJd20RVTSMajYbSChd7Dlf556ELMerpbzPTL/pfIx+57qMOCZpLyNXoJvvTI1w9sBdXy0zNQqjKGKQjJspETJTJ/wBrs8dLWUU9xeV1/tHPx1+W+h9I1eu0xFlD6W8zMyDGwpC4cPpazWi1MvL5ISRoLqG/fnqU+kYPd90yVIbsQlxCZ458ggxaBvcJY3CfMHxKy3WfXmHBFDvqKCqvY9d+p/+6T4hRx6DYMP+rJK41yo/NzpLv2CWy73g1f9tZRNKoPnJtRohuRKvREGE2MnJIbxSfQpw1FEWx4WrwUH6yHkdVAyVOF98erQZaRj1X9QsnflAUCYMj6dM7VD44nocEzSXganTzx/e/xdorhJ/dMqSruyOEOA+NRtPy2gVTOIP7hAPQ2OyhvLoBnV7Ht4crWff3g6z7e8uNBsMHRjJ8QC+u6t8LU7BMMHomCRqVNbm9vLD+a07VNfPoz6+Xe/yF6KGCg/T0j7Zgj48lLsqEq8FNaYWL0goXO/eWs33PCTQasEaEMObqaK4faqV/tFlGO0jQqKqhycPLWXs4WHyK/7o9nsF9wrq6S0KIiyQ0xMDQfhEM7dfyfE/FqQb/g6Xvf3aU9z87SnhoECMGR5IwOIr4QZGEBhu6uttdQoJGJSUVLl7J3kNZRT33ThrGmOEy1YwQgUqr1WDrZcLWy8S1V1q5qn8vDpecYvfhSr48UMFnu0+g1WgYHBdGwuAoRg6Oop/t8rmbTdWgycnJ4eWXX8bj8XDvvfeSlpbWanthYSELFizA5XJht9tZvHgxer2e0tJS5s+fT2VlJYMGDSIzM5PQ0FBqamp45JFHKCoqIjIykhUrVmC1WmlubmbBggXs2bOH4OBgMjMzueKKK9QsrU1VNY1sKSjiw13FBAfpePiua4gfGNklfRFCdI1wi5FRV1oZdaUVr0/h2Ikavj1azbdHqti49TAbtx7GaGi5m21I33CGxIUxMCYsYGcwUC1oHA4Hy5cvZ8OGDQQFBXH33XczduxYhgz518Xw+fPn8/TTTzNq1Cgee+wx1q1bx4wZM1i8eDEzZsxgypQpvPTSS6xcuZL58+ezYsUK7HY7r776KllZWSxZsoQVK1bw9ttvExISwqZNmygoKODRRx9l3bp1apXWSn2jh2JnHccctew+VMm3R6tRUPhRQizTbrqCMFNg/sMRQrTtXK9fiO4VQnSvOBqaPJRV1uM82YCr0U3u9mP+h0jNIQb6RJmI7R1KbFQoUWFGIsxGellaXr3dUx8mVS1otm3bxrhx44iIiAAgOTmZvLw8HnzwQQBKSkpobGxk1KhRAKSmpvLCCy8wffp0CgoKeOmll/zrf/7znzN//nzy8/NZvXo1AFOnTuXJJ5/E7XaTn5/PvHnzABg9ejRVVVWUlpbSp0+fDvX1Qoevq7L3cKi0xr8cFR7MtJuv4Iaro+kVFnxBx+wsvU6L6TznfTvS5mK3uxjHCjHq8XoMPbb/7bU5Xdul/Jpqt/t+m3PVd6HHulTtOn4szQ86linYQNR3r26/ZkhvdBoNReW1lFbWU15dj6O6gWMnaik8Vn32vkY9xu+9TtsYpMOg06LVatBpNWi1GvQ6rf/POq0GDRr89yNo/vWbRgND4v51V91pHfl52NmfmaoFTXl5OVbrv96VbrPZ+Prrr9vcbrVacTgcVFdXYzab0ev1rdafuY9er8dsNlNVVXXOY504caLDQdOrV+gF1fjY/eMuaL+LqW9sOH1jw8/bbnDfjs1EcDHbdddjdcXX7On974qv2Z373y/6/Df2dPRYALEx3edGoaioi/+cn2rjMJ/P1+q2PkVRWi23tf3MdkCbtwcqioJWqz1rn9PrhRBCdD3VfhrHxMTgdP7rHKXT6cRms7W5vaKiApvNRmRkJLW1tXi93rP2s9lsVFRUAODxeHC5XERERBAdHU15eflZxxJCCNH1VAua8ePHs337dqqqqmhoaGDLli0kJib6t8fFxWE0Gtm1axcA2dnZJCYmYjAYsNvt5ObmApCVleXfLykpiaysLAByc3Ox2+0YDAaSkpLIzs4GYOfOnRiNxg6fNhNCCKEujaJ8d7uDCnJycli1ahVut5tp06aRnp5Oeno6c+fOJSEhgb1797Jw4ULq6uqIj49n6dKlBAUFUVJSQkZGBpWVlcTGxrJs2TLCw8M5efIkGRkZFBUVYbFYyMzMpG/fvjQ1NbFo0SL27NlDUFAQTz/9NPHx8WqVJYQQohNUDRohhBBCrpgLIYRQlQSNEEIIVUnQCCGEUJUEjRBCCFVJ0PRQOTk5TJ48mQkTJvin5ekJ/vCHPzBlyhSmTJnCs88+C7RMV5SSksKECRNYvny5v21hYSGpqakkJyezYMECPB4PAKWlpaSlpTFx4kTmzJmDy+Xqklra8rvf/Y6MjAwgsGr76KOPSE1NZdKkSTz99NNA4NSXnZ3t/3f5u9/9Duj5tdXV1TF16lSKi4uBi1dPTU0Ns2bNYtKkSaSlpbV6HrJNiuhxTpw4odx8881KdXW14nK5lJSUFOXAgQNd3a3z+uyzz5S77rpLaWpqUpqbm5WZM2cqOTk5SlJSknL8+HHF7XYr999/v5Kfn68oiqJMmTJF+eKLLxRFUZRHH31UWb16taIoijJr1izl/fffVxRFUf7whz8ozz77bJfUcy7btm1Txo4dq/z6179WGhoaAqa248ePKzfeeKNSVlamNDc3K//+7/+u5OfnB0R99fX1yujRo5XKykrF7XYr06ZNUz788MMeXduXX36pTJ06VYmPj1eKioou6r/FxYsXK6tWrVIURVE2btyozJs377z9kRFND/T9CUtNJpN/wtLuzmq1kpGRQVBQEAaDgSuuuIKjR48yYMAA+vXrh16vJyUlhby8vHNOupqXl4fb7aagoIDk5ORW67uDkydPsnz5cmbPng3A119/HTC1ffDBB0yePJmYmBgMBgPLly8nJCQkIOrzer34fD4aGhrweDx4PB7MZnOPrm3dunU88cQT/hlSLua/xfz8fFJSUoCWyY23bt2K2+1utz/y4rMe6HwTlnZXQ4cO9f/56NGjbNq0iZ///Odn1eJwOC5o0tWutmjRIh5++GHKysqAc/899dTajh07hsFgYPbs2ZSVlXHTTTcxdOjQgKjPbDYzb948Jk2aREhICKNHj+7xf3dLlixptXwx62lrcuPo6LZf7igjmh7ofBOWdncHDhzg/vvv51e/+hX9+vU7Zy0XY9LVS+kvf/kLsbGx3HDDDf51bdXQ02qDlk/927dv55lnnmHt2rV8/fXXFBUVBUR9e/fu5b333uPvf/87n3zyCVqtlqNHjwZEbaep+W9R6cAkxjKi6YFiYmLYuXOnf/nMCUu7s127djF37lwee+wxpkyZwueff37OyVc7MumqTqfrNrXn5ubidDq5/fbbOXXqFPX19ZSUlKDT6fxtemptAL179+aGG24gMrLlbbG33noreXl5AVHfp59+yg033EBUVBTQcpro9ddfD4jaTmtrkuMLqef05MYxMTGtJjduj4xoeqDzTVjaXZWVlfGLX/yCzMxMpkyZAsA111zDkSNHOHbsGF6vl/fff5/ExMQLmnS1K7355pu8//77ZGdnM3fuXG655RZee+21gKgN4Oabb+bTTz+lpqYGr9fLJ598wsSJEwOivmHDhrFt2zbq6+tRFIWPPvooYP5dnnYx62lrcuP2yFxnPdS5Jizt7p5++mnee+89+vfv71939913M3DgQJYuXUpTUxNJSUk8+uijaDSaTk+62l1s2LCBzz//nN/+9rds3749YGpbv349b731Fm63mx/96EcsXLiQHTt2BER9r776Khs2bMBgMJCQkMATTzzBP//5zx5f2y233MKf//xn+vbte9H+LbY1uXF7JGiEEEKoSk6dCSGEUJUEjRBCCFVJ0AghhFCVBI0QQghVSdAIIYRQlTywKcQZnn76aQoKCgA4dOgQcXFxBAcHA7B27Vr/n0/Lz8/nq6++Yt68ee0e95577vHPhguwceNG3n33XRobG3G73Vx//fXMnz+fsLAwXnzxRaqrq1m0aJEKFQpxaUnQCHGGhQsX+v98yy23kJmZSUJCQpvtd+/ezalTpzr1NV555RW2bt3KSy+9RO/evXG73TzzzDPMnj2bNWvWXHDfheiOJGiE6KCXXnqJ//u//0On0zFo0CAef/xxSktLeffdd/F6vVgsFv7rv/6L3/zmNxw7doyTJ08SGhpKZmYmgwcP9h+nvr6eVatWsXHjRnr37g2AwWDgV7/6FR988AHNzc0AHD58mHvuuQen00nv3r1ZtmwZNpuNv//976xatYrm5maqqqq44447eOihh9ixYwdLlizBZDLhcrl47733eOutt1i/fj2hoaHY7XY+/PBDPvroI5qbm8nMzKSgoACv18vVV1/NwoULMZvNrFmzhnfffReDwYDRaOTJJ59kyJAhXfI9F4FBrtEI0QHvvfcen3zyCevXrycnJ4ehQ4eSkZHBNddcw913383kyZN5+OGH2bp1K2FhYaxdu5bNmzczYsSIs15Md/jwYYKDgxk4cGCr9SEhIdx2220EBQUBUFRUxPPPP09eXh5hYWH85S9/QVEU3njjDX7729+yYcMG1q5dy6uvvkpVVRXQMmHpc889R05ODjt27GDDhg2sX7+eDRs2tHoR16uvvopOp2PDhg389a9/xWazkZmZidfr5ZlnnuG1117jvffe42c/+5l/ehIhLpSMaITogK1bt5KamorJZAJg5syZvPLKK/7Rx2kTJ06kX79+vP322xw7dozPP/+ca6+9tlUbrVaLz+c779f80Y9+5J/EctiwYVRVVaHRaHjllVfIz8/n/fff59ChQyiKQkNDAwCxsbHExcUB8PHHHzNx4kTCwsIASEtL4x//+AfQcl2ptraWbdu2AeB2u4mKikKn0zFx4kTuvvtubrrpJm688UaSkpIu9NsmBCBBI0SHnDmdus/n87/y9vvWrFnDunXrSEtLIyUlhYiICP+rdE8bMmQIHo+Ho0ePthrVNDU18eCDD/pfk3z6XSCAf+r2+vp67rzzTm699Vbsdjv/9m//xt/+9jdOzyR1OghP7//9Gaa+Pxuxz+fjscce84eIy+WiqakJgMzMTPbv38+2bdt49dVXyc7O5vnnn+/090yI0+TUmRAd8OMf/5j33nuP+vp6AN5++21Gjx5NUFAQOp3OHzqffvopd955J9OnT2fQoEF89NFHeL3eVscKCgoiPT2dBQsWUFFRAUBzczPPPPMMDQ0N7b5A6tixY9TV1fHQQw9xyy23sGPHDpqbm885QkpKSmLLli3U1tYCLZNinnbjjTeyevVq/76PP/44y5Yto6qqiqSkJCIiIrjvvvt46KGH2L179w/75onLnoxohOiAadOmUVZWxvTp0/H5fAwYMIDMzEwAxo0bxyOPPMJTTz3F/fffz6JFi/w/1EeNGsX+/fvPOt7s2bMJCQnhgQceAFpGM2PGjGHlypXt9uOqq67ipptuYtKkSQQFBXHllVcyZMgQjh075r+2c9oNN9zAz372M+666y6Cg4MZOnQoISEhAPz3f/83v/vd77jzzjvxer0MHz6cjIwMzGYzc+bM4b777iM4OBidTucfYQlxoWT2ZiEC1O7du/niiy+YOXMm0PLOnK+++ooVK1Z0bcfEZUeCRogAVVdXx2OPPcbhw4fRaDTExsby1FNPtXtqTgg1SNAIIYRQldwMIIQQQlUSNEIIIVQlQSOEEEJVEjRCCCFUJUEjhBBCVRI0QgghVPX/AWQ8H7K4Xk+GAAAAAElFTkSuQmCC",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.distplot(numeric_data[\"TotalCharges\"])"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "- Die Kurve flacht nach hinten hin extrem stark ab.\n",
-                "- Es sind \u00c4hnlichkeiten mit der Exponentialverteilung zu erkennen. -> Test der logarithmischen Transformation zur Erreichung einer Normalverteilung.\n",
-                "- Es ist fraglich, ob es Ausrei\u00dfer im hinteren Teil gibt. -> Boxplot\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 153,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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MBtXR0aFTp04pMzNT+/bt0yOPPKIHHnggWVsAAPy/pN359/f369FHH9X27du1efNmeTweBQKBZC0PALhO0u78Q6GQXC5X7LFlWbp48WKylgdWxe1+RCMjwwm/LpH3E+zc+S998835hNcAbqekxT8ajcrhcMQe27a94nE8ubnZa7EtYIXh4R9TvQUgKZIW/7y8PF24cCH2OBwOy7KsVb9+enpO0ai9FlsD/haXa4vC4dlUbwNYIS3Nccub5qSd+T/22GM6d+6cfv31V127dk1ffvml3G53spYHAFwnaXf+99xzjw4ePKj9+/draWlJzz//vB566KFkLQ8AuI7Dtu0NcZbCsQ/WK459sB6tm2MfAMD6QfwBwEDEHwAMtGE+zz8tbfXvCQCSjd9PrDfxfic3zB98AQC3D8c+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g8ABiL+AGAg4g/8DXNzc3rmmWf0yy+/pHorQEKIP/AXff/993rhhRc0NjaW6q0ACSP+wF/U2dmp1tbWhL6LGlgvNsynegLrzZEjR1K9BeAv484fAAxE/AHAQMQfAAxE/AHAQHyTFwAYiDt/ADAQ8QcAAxF/ADAQ8QcAAxF/ADAQ8QcAAxF/ADAQ8QcAA/0v1rvu0vlBQPUAAAAASUVORK5CYII=",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# Boxplot f\u00fcr TotalCharges erstellen, um sicherzustellen, dass keine Ausrei\u00dfer vorhanden sind.\n",
-                "plt.boxplot(numeric_data[\"TotalCharges\"])\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "- Boxplot zeigt keine Ausrei\u00dfer.\n",
-                "- Dies bedeutet, dass auch bei den Gesamtkosten keine Ausrei\u00dfer festgestellt werden k\u00f6nnen.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 154,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
-                        ]
-                    },
-                    "execution_count": 154,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# logarithmic transformation\n",
-                "log_charges = np.log(data_no_mv[\"TotalCharges\"])\n",
-                "sns.distplot(log_charges)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "- Auch die Transformation mit Hilfe des Logarithmus f\u00fchrt nicht zu einer Normalverteilung.\n",
-                "- Vor weiteren Transformationen sollte zun\u00e4chst die Korrelation mit anderen Variablen untersucht werden.\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 155,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
-                        ]
-                    },
-                    "execution_count": 155,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"TotalCharges\"],\n",
-                "             bins=10,\n",
-                "             color='orange',\n",
-                "             label='Non-Churn',\n",
-                "             kde=True)\n",
-                "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"TotalCharges\"],\n",
-                "             bins=10,\n",
-                "             color='blue',\n",
-                "             label='Churn',\n",
-                "             kde=True)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "\n",
-                "Die Verteilung ist \u00fcber die gesamte Bandbreite der Kosten sowohl bei den abwandernden als auch bei den nicht abwandernden Kunden nahezu identisch.\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Korrelationsanalyse"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 156,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:>"
-                        ]
-                    },
-                    "execution_count": 156,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# correlation between continous features\n",
-                "feature_corr = numeric_data.drop(\"SeniorCitizen\", axis=1).corr()\n",
-                "sns.heatmap(feature_corr, annot=True, cmap='coolwarm')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Die Korrelationsmatrix zeigt, dass die Attribute \"Tenure\" und \"TotalCharges\" eine kritische positive Korrelation von \u00fcber 0,8 aufweisen. Diese Beziehung wird sp\u00e4ter im Zusammenhang mit der Multikollinearit\u00e4t erneut untersucht und muss entfernt werden."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Streudiagramme mit kontinuierlichen Merkmalen und Ziel"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 157,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:xlabel='tenure', ylabel='MonthlyCharges'>"
-                        ]
-                    },
-                    "execution_count": 157,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.scatterplot(data=data_no_mv, x=\"tenure\", y=\"MonthlyCharges\", hue=\"Churn\")"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Das Streudiagramm deutet darauf hin, dass Kunden im oberen linken Bereich, d. h. Kunden mit hohen monatlichen Kosten und kurzer Betriebszugeh\u00f6rigkeit, am ehesten abwandern."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 158,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot:xlabel='tenure', ylabel='TotalCharges'>"
-                        ]
-                    },
-                    "execution_count": 158,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.scatterplot(data=data_no_mv, x=\"tenure\", y=\"TotalCharges\", hue=\"Churn\")"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Es besteht eine rein logische, lineare Beziehung zwischen der Dauer der Betriebszugeh\u00f6rigkeit und den in Rechnung gestellten Gesamtkosten. Je l\u00e4nger eine Person Kunde ist, desto mehr monatliche Betr\u00e4ge musste sie bereits zahlen."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 2.4.2. Kategorische Merkmale"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Abwanderung (Ziel)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Zun\u00e4chst wird die Verteilung der Zielvariablen Churn untersucht."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 159,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Text(0.5, 1.0, 'Abwanderung im Vergleich zur Nicht-Abwanderung')"
-                        ]
-                    },
-                    "execution_count": 159,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# produce pie chart for churn\n",
-                "# generate procentage relationship\n",
-                "churn_rate = data_no_mv.Churn.value_counts() / len(data_no_mv.Churn)\n",
-                "\n",
-                "# Plot\n",
-                "labels = 'Keine Abwanderung', 'Abwanderung'\n",
-                "fig, ax = plt.subplots()\n",
-                "ax.pie(churn_rate, labels=labels, autopct='%.f%%')  \n",
-                "ax.set_title('Abwanderung im Vergleich zur Nicht-Abwanderung')"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "- Die Abwanderungen machen etwa 27 % des gesamten Datensatzes aus, w\u00e4hrend die Nicht-Abwanderungen etwa 73 % ausmachen.\n",
-                "- Dies ist ein unausgewogener Datensatz und eine andere Metrik muss in der Bewertungsphase verwendet werden.\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Geschlecht"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 160,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": "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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"gender\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Die Abwanderungsrate zwischen M\u00e4nnern und Frauen ist ungef\u00e4hr gleich hoch."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Senioren"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 161,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEJCAYAAABohnsfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdV0lEQVR4nO3de3xU1d3v8e8kE5JAQAjMkMjhyVFapUKRFgSpkii2JBICmMoLDDSgDRWqaL0QIiAULXKREkEbDo8iRQQUFAPFOHh7yRGDJc2rSmOpFwp5NGgyhASSQG4z+/zR40AEwgIzmYF83v8ke83ae347hPlm1p69ls2yLEsAABgICXQBAICLB6EBADBGaAAAjBEaAABjhAYAwBihAQAwRmgAAIzZA12Av1VU1Mjr5VYUADAREmJTly4dzvr4JR8aXq9FaABAC2F4CgBgjNAAABi75IenAOBCWJaligq36utrJV16Q9yhoXZFRXVWZOTZr1+cCaEBAGdQXX1UNptN3bv/L9lsl9agjGVZamioV2WlW5LOKzgurZ8EALSQEyeq1bFj50suMCTJZrOpXbtwde7sUHV15Xnte+n9NACgBXi9HoWGXtqDMWFh7eTxNJ7XPoQGAJyFzWYLdAl+dSHnd2nHaAvo2ClCEeFhgS4jKNTWNajqWG2gywCCmsfj0ebNG/XWWzvk8XjU2Nign/1sqDIypurJJ5/QFVf0UlrarwJd5gUjNM4hIjxMaZnrA11GUNiwZIKqRGgAzVm6dJGqqo5p+fKVioqK0okTJ/TYY3O0ePHjCgkJDXR53xuhAQAt5OuvD+mtt97Q1q0udegQJUmKjIzUww8/on/842N98MH7Kiraq6lT79KRI+W68spemjdvgSIjI3XjjQO1ffvb6ty5syT5tv/97y+0fPkfFRkZqRMnjuu3v71ff/7zs7r88h7697/3q7GxUTNmzFK/fv1b5Ry5pgEALeTTT/fpiiuu9AXGt7p27aabbrpFkuR2l+mpp3K0ceMWlZWVaefOd8953AMH9uv3v1+gtWtfUlhYmP75z080fvxErVmzQSNGjNJ//3eOX87nTAgNAGghNlvIOee6i4+/SREREQoNDdWVV/ZSRUXFOY/rdHZXTEysb7t79xj98IdXS5Kuvrq3jh07+v0KPw+EBgC0kD59+qq4+ICOH69p0u52l2nGjPtVV1fX5GO8NptNlnUyZL79vqGhocn+kZGRTbbDw8ObbJ96DH8jNACghXTr5tAvfnGrnnjiMdXUVEuSamqq9cc/LlKnTped9mJ/qs6du+hf//qnJOmtt1ytUu+F4EI4ALSghx6aqbVrV2vq1LsUGmpXQ0O9hg69Sb/+9d1asmTBWff73e8e1rJlS9SxY5QGDhysrl27tWLV5mxWa76vCYDy8urvtZ6Gw9GRj9z+fxuWTJDbXRXoMoBW8c03xYqJiQt0GX733fMMCbGpa9eos/ZneAoAYIzQAAAYIzQAAMYIDQCAMUIDAGCM0AAAGCM0AADGuLkPAAz4a20d03Vqvv76kMaOHaXs7Gd03XXX+9pvvz1FTz+9SrGxl7d4bWdCaACAAX+trXM+69TY7XYtXrxAL7zwktq379DitZhgeAoALhLdujl03XWD9fTTT5322AsvPK+JE8cqPX2cnn46Wx6Pxy81+D00Fi9erKysLElSfn6+UlJSNHz4cGVnZ/v67Nu3T6mpqUpMTNTs2bPV2Pifhc4PHTqkCRMmKCkpSdOmTVNNTc0ZnwMA2op77/2d9uzZrYKCD31tH36Yr127/q+ee26dnn9+vUpKvlRu7qt+eX6/hsbu3bv12muvSZJqa2s1a9Ys5eTkKC8vT0VFRdq5c6ckacaMGZo7d6527Nghy7K0adMmSdL8+fOVlpYml8ulvn37Kien9RYaAYBg1KFDlGbOnKPFixf4pmAvLNyjn/88UREREbLb7UpOHqXCwgK/PL/fQqOyslLZ2dmaOnWqJGnv3r2Ki4tTz549ZbfblZKSIpfLpZKSEtXW1qp///6SpNTUVLlcLjU0NKigoECJiYlN2gGgrRs06Pomw1TfnZTVsiSPp9Evz+230Jg7d64eeOABderUSZJUVlYmh8Phe9zpdKq0tPS0dofDodLSUlVUVCgqKkp2u71JOwDg5DBVeflhDRgwUG+/vUN1dbVqbGxUXt42/fSnA/3yvH759NTmzZsVGxurIUOGaMuWLZIkr9crm83m62NZlmw221nbv/16qu9um2huil+cP4ejY6BLAFpFWVmI7PaTf1fX1Tdqw5IJLf48dfWNTZ7nbEJD/9Pn276XXdZJs2Y9qvvvv0dDhyaopqZGGRnp8ng8GjToeo0bd4fRcUNCQs7r/7VfQiMvL09ut1ujR4/W0aNHdfz4cZWUlCg0NNTXx+12y+l0KiYmRm6329d++PBhOZ1ORUdHq6qqSh6PR6Ghob7+56sl1tPASayngbbC6/WqsdHr2z529EQAq5Gczhi98spfmtQ0YMBg7dr1N0lSevpdSk+/q8k+p/Y9G6/X2+T/dUDW01izZo22b9+urVu36r777tOwYcP03HPP6cCBAyouLpbH49H27dsVHx+vHj16KDw8XIWFhZKkrVu3Kj4+XmFhYRo4cKDy8vIkSbm5uYqPj/dHuQAAQ612c194eLgWLVqk6dOnq66uTgkJCUpKSpIkLV26VHPmzFF1dbX69Omj9PR0SdK8efOUlZWllStXKjY2VsuWLWutcgEAZ8Byr+fAcq8nsdwr2hKWez0z7ggHABgjNAAAxggNAIAxZrkFAANdLmsne7vwFj9uY32dKo7WN9tn2bLFOnKkXH/4wxJf2549H+rJJxdq7doNrTrjLaEBAAbs7cJVuCSjxY87IPM5Sc2HxtSp0zVp0njt2rVTN96YoBMnTmjp0oV65JFHW32KdEIDAIJc+/btlZk5WwsXPqYBAwbpuef+j268MV6RkZGaNu3Xqqur1WWXddaMGbN0+eU99NJLL+qNN15XSIhNP/pRH2Vmzm6xWggNALgIXHfdYA0ePERPPDFfxcUHlJOzWvfcM0WLF2crJiZGf/3rbi1evEDLlj2tF1/8s3JzXQoJCdGiRY/L7S6Tw3H+M2qcCaEBABeJe+/9nX75y5F64omlKiv7RocOfaWsrAd9j9fU1Cg0NFR9+/ZTRka6hg5N0PjxE1osMCRCAwAuGh06RCkqqqNiYy9XdXW1Lr+8h/785w2SJI/Ho4qKI5KkhQv/qE8++Yc+/DBfDz10n+bOfVw/+cmAFqmBj9wCwEUoLu5/69ixY/r4479Lkl5/fZt+//vZqqio0MSJY3XllT9QRsZUXXfdYO3f/3mLPS/vNADgItSuXTs9/vgiLV++VPX19WrfvoPmzJmvLl26aNSo2zRlSrrCwyP0X/8Vp+Tk0S32vMw9dQ7MPXUSc0+hLfnunEyBvE/Dn8537ineaQCAgf+8sAfuxT1YcE0DAGCM0AAAGCM0AOAsLvFLvrIsryTbee1DaADAGdjt7VRTc+ySDA7LstTY2KDKysNq1y7ivPblQjgAnEGXLg5VVLhVXV0Z6FL8IiQkVJGRUYqKuuy89iM0AOAMQkPt6tYtNtBlBB2GpwAAxggNAIAxQgMAYIzQAAAYIzQAAMYIDQCAMUIDAGCM0AAAGCM0AADGCA0AgDFCAwBgjNAAABgjNAAAxggNAIAxQgMAYIzQAAAYIzQAAMYIDQCAMb+GxvLlyzVixAglJydrzZo1kqT8/HylpKRo+PDhys7O9vXdt2+fUlNTlZiYqNmzZ6uxsVGSdOjQIU2YMEFJSUmaNm2aampq/FkyAKAZfguNPXv26MMPP9S2bdv06quvat26dfrXv/6lWbNmKScnR3l5eSoqKtLOnTslSTNmzNDcuXO1Y8cOWZalTZs2SZLmz5+vtLQ0uVwu9e3bVzk5Of4qGQBwDn4LjUGDBumFF16Q3W5XeXm5PB6Pjh07pri4OPXs2VN2u10pKSlyuVwqKSlRbW2t+vfvL0lKTU2Vy+VSQ0ODCgoKlJiY2KQdABAYfh2eCgsL04oVK5ScnKwhQ4aorKxMDofD97jT6VRpaelp7Q6HQ6WlpaqoqFBUVJTsdnuTdgBAYNj9/QT33XefpkyZoqlTp+rgwYOy2Wy+xyzLks1mk9frPWP7t19P9d3tc+naNer7nQCacDg6BroEAAHkt9DYv3+/6uvr9aMf/UiRkZEaPny4XC6XQkNDfX3cbrecTqdiYmLkdrt97YcPH5bT6VR0dLSqqqrk8XgUGhrq638+ysur5fVaF3wevEg25XZXBboEAH4UEmJr9o9tvw1PffXVV5ozZ47q6+tVX1+vd955R+PHj9eBAwdUXFwsj8ej7du3Kz4+Xj169FB4eLgKCwslSVu3blV8fLzCwsI0cOBA5eXlSZJyc3MVHx/vr5IBAOfgt3caCQkJ2rt3r8aMGaPQ0FANHz5cycnJio6O1vTp01VXV6eEhAQlJSVJkpYuXao5c+aourpaffr0UXp6uiRp3rx5ysrK0sqVKxUbG6tly5b5q2QAwDnYLMu68LGbi0BLDE+lZa5vwYouXhuWTGB4CrjEBWx4CgBw6SE0AADGCA0AgDFCAwBgjNAAABgjNAAAxggNAIAxQgMAYIzQAAAYIzQAAMYIDQCAMUIDAGDMKDTOtFreF1980eLFAACCW7OhUVlZqcrKSk2ZMkVHjx71bR8+fFj33ntva9UIAAgSza6n8dBDD+mDDz6QJA0ePPjkTna7EhMT/VsZACDoNBsaq1evliQ98sgjWrhwYasUBAAIXkYr9y1cuFAlJSU6evSoTl2zqU+fPn4rDAAQfIxCY8WKFVq9erW6du3qa7PZbHrnnXf8VhgAIPgYhUZubq7efPNNde/e3d/1AACCmNFHbmNjYwkMAIDZO40hQ4ZoyZIluuWWWxQREeFr55oGALQtRqGxZcsWSZLL5fK1cU0DANoeo9B49913/V0HAOAiYBQaa9asOWP7nXfe2aLFAACCm1FofPbZZ77v6+vrVVBQoCFDhvitKABAcDK+ue9UpaWlmj17tl8KAgAErwuaGr179+4qKSlp6VoAAEHuvK9pWJaloqKiJneHAwDahvO+piH952a/zMxMvxQEAAhe53VNo6SkRI2NjYqLi/NrUQCA4GQUGsXFxfrtb3+rsrIyeb1edenSRatWrVKvXr38XR8AIIgYXQh/7LHHlJGRoYKCAhUWFmratGmaP3++v2sDAAQZo9AoLy/Xbbfd5tv+5S9/qYqKCr8VBQAITkah4fF4VFlZ6ds+cuSIv+oBAAQxo2saEydO1Lhx43TrrbfKZrMpLy9PkyZN8ndtAIAgY/ROIyEhQZLU0NCg/fv3q7S0VL/4xS/8WhgAIPgYvdPIysrShAkTlJ6errq6Om3cuFGzZs3Ss88+6+/6AABBxOidRkVFhdLT0yVJ4eHhmjx5stxut18LAwAEH+ML4aWlpb7tw4cPy7Ksc+73zDPPKDk5WcnJyVqyZIkkKT8/XykpKRo+fLiys7N9ffft26fU1FQlJiZq9uzZamxslCQdOnRIEyZMUFJSkqZNm6aamprzOkEAQMsxCo3JkydrzJgxyszM1MyZM3XbbbcpIyOj2X3y8/O1a9cuvfbaa8rNzdUnn3yi7du3a9asWcrJyVFeXp6Kioq0c+dOSdKMGTM0d+5c7dixQ5ZladOmTZKk+fPnKy0tTS6XS3379lVOTs73PGUAwIUyCo3bb79da9as0TXXXKO+fftq9erVSklJaXYfh8OhrKwstWvXTmFhYerVq5cOHjyouLg49ezZU3a7XSkpKXK5XCopKVFtba369+8vSUpNTZXL5VJDQ4MKCgqUmJjYpB0AEBhGF8IlqXfv3urdu7fxgX/4wx/6vj948KDeeOMNTZw4UQ6Hw9fudDpVWlqqsrKyJu0Oh0OlpaWqqKhQVFSU7HZ7k/bz0bVr1Hn1R/Mcjo6BLgFAABmHxoX6/PPPdffddyszM1OhoaE6ePCg7zHLsmSz2eT1emWz2U5r//brqb67fS7l5dXyes99/eVseJFsyu2uCnQJAPwoJMTW7B/bF7QIk6nCwkJNnjxZDz30kG677TbFxMQ0+dSV2+2W0+k8rf3w4cNyOp2Kjo5WVVWVPB5Pk/4AgMDwW2h8/fXXuueee7R06VIlJydLkq699lodOHBAxcXF8ng82r59u+Lj49WjRw+Fh4ersLBQkrR161bFx8crLCxMAwcOVF5eniQpNzdX8fHx/ioZAHAOfhueWr16terq6rRo0SJf2/jx47Vo0SJNnz5ddXV1SkhIUFJSkiRp6dKlmjNnjqqrq9WnTx/ffSHz5s1TVlaWVq5cqdjYWC1btsxfJQMAzsFmmdxwcRFriWsaaZnrW7Cii9eGJRO4pgFc4gJ6TQMAcGkhNAAAxggNAIAxQgMAYIzQAAAYIzQAAMYIDQCAMUIDAGCM0AAAGCM0AADGCA0AgDFCAwBgjNAAABgjNAAAxggNAIAxQgMAYIzQAAAYIzQAAMYIDQCAMUIDAGCM0AAAGCM0AADG7IEuABcPb2ODHI6OgS4jKDTW16niaH2gywBaHaEBYyH2MBUuyQh0GUFhQOZzkggNtD0MTwEAjBEaAABjhAYAwBihAQAwRmgAAIwRGgAAY4QGAMAYoQEAMEZoAACMERoAAGOEBgDAGKEBADBGaAAAjPk1NKqrqzVy5Eh99dVXkqT8/HylpKRo+PDhys7O9vXbt2+fUlNTlZiYqNmzZ6uxsVGSdOjQIU2YMEFJSUmaNm2aampq/FkuAOAc/BYaH3/8se644w4dPHhQklRbW6tZs2YpJydHeXl5Kioq0s6dOyVJM2bM0Ny5c7Vjxw5ZlqVNmzZJkubPn6+0tDS5XC717dtXOTk5/ioXAGDAb6GxadMmzZs3T06nU5K0d+9excXFqWfPnrLb7UpJSZHL5VJJSYlqa2vVv39/SVJqaqpcLpcaGhpUUFCgxMTEJu0AgMDx2yJMCxYsaLJdVlYmh8Ph23Y6nSotLT2t3eFwqLS0VBUVFYqKipLdbm/Sfr66do26wDMAmscqhmiLWm3lPq/XK5vN5tu2LEs2m+2s7d9+PdV3t02Ul1fL67UuuG5eGHA2bndVoEsAWlxIiK3ZP7Zb7dNTMTExcrvdvm232y2n03la++HDh+V0OhUdHa2qqip5PJ4m/QEAgdNqoXHttdfqwIEDKi4ulsfj0fbt2xUfH68ePXooPDxchYWFkqStW7cqPj5eYWFhGjhwoPLy8iRJubm5io+Pb61yAQBn0GrDU+Hh4Vq0aJGmT5+uuro6JSQkKCkpSZK0dOlSzZkzR9XV1erTp4/S09MlSfPmzVNWVpZWrlyp2NhYLVu2rLXKBQCcgd9D49133/V9P2TIEG3btu20Pr1799Yrr7xyWnuPHj20bt06v9YHADDHHeEAAGOEBgDAGKEBADBGaAAAjBEaAABjhAYAwBihAQAwRmgAAIwRGgAAY4QGAMBYq809BaBldewUoYjwsECXERRq6xpUdaw20GW0CYQGcJGKCA9TWub6QJcRFDYsmaAqERqtgeEpAIAxQgMAYIzQAAAYIzQAAMYIDQCAMUIDAGCMj9wCuOh5GxvkcHQMdBlBobG+ThVH6/12fEIDwEUvxB6mwiUZgS4jKAzIfE6S/0KD4SkAgDFCAwBgjNAAABgjNAAAxggNAIAxQgMAYIzQAAAYIzQAAMYIDQCAMUIDAGCM0AAAGCM0AADGCA0AgDFCAwBgjNAAABgjNAAAxggNAICxiyI0/vKXv2jEiBEaPny41q9fH+hyAKDNCvrlXktLS5Wdna0tW7aoXbt2Gj9+vAYPHqwf/OAHgS4NANqcoA+N/Px8XX/99ercubMkKTExUS6XS/fee6/R/iEhtu9dQ7cuHb73MS4V7Tp1DXQJQaMlfre+L343T+J386Tv87t5rn1tlmVZF3z0VrBq1SodP35cDzzwgCRp8+bN2rt3rx5//PEAVwYAbU/QX9Pwer2y2U4mn2VZTbYBAK0n6EMjJiZGbrfbt+12u+V0OgNYEQC0XUEfGj/72c+0e/duHTlyRCdOnNCbb76p+Pj4QJcFAG1S0F8I7969ux544AGlp6eroaFBt99+u/r16xfosgCgTQr6C+EAgOAR9MNTAIDgQWgAAIwRGgAAY4QGAMAYoYFzYsJIBLPq6mqNHDlSX331VaBLaRMIDTTr2wkjN2zYoNzcXL388sv64osvAl0WIEn6+OOPdccdd+jgwYOBLqXNIDTQrFMnjGzfvr1vwkggGGzatEnz5s1jlohWFPQ39yGwysrK5HA4fNtOp1N79+4NYEXASQsWLAh0CW0O7zTQLCaMBHAqQgPNYsJIAKciNNAsJowEcCquaaBZTBgJ4FRMWAgAMMbwFADAGKEBADBGaAAAjBEaAABjhAYAwBihgTbno48+0q9+9SulpKRo5MiRysjI0Oeff37Bx5syZcoFTeJYWlqqrKwspaSkaNSoURo7dqzefvtt3+OjR4/WsWPHVFVVpfT09NPagYCwgDakrq7OGjRokFVUVORry83NtRISEqzGxsZWq6O8vNy66aabrNdee83yer2WZVnWvn37rOuvv97atWtXk75ffvml1b9//1arDWgON/ehTTlx4oSqqqp0/PhxX9uoUaMUFRUlj8ejnTt3auXKlWpoaFBERIRmzpypn/zkJ3r66adVUlIit9utkpISde/eXU8++aScTqeGDRum5cuX68c//rFefvllrVu3TiEhIerWrZseffRRXXHFFcrKylJlZaW+/PJL3XTTTYqMjNRPf/pTjRkzxldH7969tWLFCnXq1EmSdPXVV2v37t165JFHVFtbq9GjR2vLli265pprtHv3bq1du1bvvfeepP/MCfbpp5/qD3/4g8aOHavNmzdr48aN8nq96ty5sx599FH16tVLWVlZioqK0qeffqpvvvlGV199tRYvXqwOHTq05j8DLmaBTi2gtT3//PNWv379rGHDhlkPP/ywtXnzZuv48ePWgQMHrJEjR1pHjhyxLMuyPvvsM+uGG26wampqrBUrVli33HKLVVVVZVmWZd19993W8uXLLcuyrJtvvtnau3evlZ+fb/385z+3ysvLLcuyrFdffdW69dZbLa/Xa82cOdOaNGmSr4a7777bevHFF5ut86qrrrLKy8tPe6fxbfuplixZYv3mN7+xGhsbrb/+9a9WWlqadfz4ccuyLOv999+3kpKSLMuyrJkzZ1rjxo2z6urqrPr6emvMmDHWK6+88j1+mmhreKeBNufOO+/U2LFjVVBQoIKCAj377LN69tlnlZaWprKyMk2ePNnX12az6X/+538kSYMGDVJUVJQk6ZprrtHRo0ebHPf999/XiBEjFB0dLUlKTU3VggULfCvKDRgwoMlxrRaajOGFF17Q7t279eKLLyo0NFTvvfeeiouLNX78eF+fY8eOqbKyUpI0dOhQtWvXTpJ01VVXnXYeQHMIDbQphYWF+vvf/66MjAzdfPPNuvnmm/Xggw9q5MiRqq6u1pAhQ/TUU0/5+n/99ddyOp166623FBER4Ws/04u+1+s97fksy1JjY6MkqX379r72/v3766OPPtLEiROb9H/ppZd04sQJ3XnnnUbn88Ybb2jt2rV66aWXfMf3er0aPXq0ZsyY4dsuKyvTZZddJknnPA+gOXx6Cm1KdHS0Vq5cqb/97W++Nrfbrerqat1yyy364IMPtH//fknSzp07NWrUKNXW1hode+jQocrLy9ORI0ckSa+++qo6d+6suLi40/qOGzdOe/bs0bZt23wv2kVFRVqxYoWuuuqqJn3tdrs8Hs9pL+579uzRggULtGrVqiYLZd144416/fXXVVZWJknauHGjJk2aZHQOwLnwTgNtyhVXXKE//elPys7O1jfffKPw8HB17NhRTzzxhHr37q3HHntMDz74oCzLkt1u18qVK40vEt9www2aPHmyJk2aJK/Xq+joaK1atUohIaf/bda5c2etW7dOTz75pK9PZGSkFixYoBtuuKFJX4fDoX79+ik5OVnr16/3tT/66KOy2WzKzMyUx+ORJA0bNkz333+/pkyZorvuuks2m01RUVF65plnWDwLLYJZbgEAxhieAgAYIzQAAMYIDQCAMUIDAGCM0AAAGCM0AADGCA0AgDFCAwBg7P8BcSY1gN498MEAAAAASUVORK5CYII=",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"SeniorCitizen\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Bei Kunden, die als Senioren eingestuft werden, ist die Wahrscheinlichkeit h\u00f6her, dass sie abwandern."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Partner"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 162,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": "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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"Partner\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Kunden, die keinen Partner haben, sind eher bereit, abzuwandern."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Angeh\u00f6rige"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 163,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"Dependents\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Kunden, die Verwandte haben, sind eher bereit, abzuwandern."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Mehrere Anschl\u00fcsse"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 164,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"MultipleLines\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Bei Kunden, die mehrere Anschl\u00fcsse haben, ist die Wahrscheinlichkeit einer Abwanderung  geringer."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Internet Service"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 165,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"InternetService\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Wenn ein Kunde einen Glasfaseranschluss hat, ist es wahrscheinlicher, dass er ausf\u00e4llt als ein Kunde mit DSL."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Online-Sicherheit"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 166,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"OnlineSecurity\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Kunden, die den Internet-Sicherheitsdienst nicht nutzen, werden eher abwandern."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Online Backup"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 167,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"OnlineBackup\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Personen, die keine Online-Datensicherung nutzen, sind eher bereit, umzuziehen."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Ger\u00e4teschutz\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 168,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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a8fm4ceOIjo4mPDwci8VSZdpfD5vRtKnvbyu4jvPza+jqEuQ30Parv67VbVerofH9998zfvx4xowZw7hx44CzF7t37drFiBEjADAMA6vVSpMmTSgqKqKyshJ3d3dycnIcp7qq4+TJYux247Lj1dcdICenyNUl1AnafvVbfdx+V+u2c3OzXPKP7Vq75ba4uJiHH36YadOmOQIDwNvbmyVLlnD8+HEMw2DDhg2Ehobi4eFBUFAQ27ZtAyA1NZWQkJDaKldERC6g1kIjJSWF3NxcXn75ZcettcuWLaNJkybEx8czceJEBgwYgGEYPPTQQwDExsaSnJzMwIED2b9/P9OnT6+tckVE5AKcfnpq+/btAIwdO5axY8decJz+/fs77pI6V2BgIOvWrXNmefWavcJW7w7rK8rLyD9V7uoyROQK1fqFcKk5blYP0hePd3UZ1dJ15hpAoSFSX6kbERERMU2hISIipik0RETENIWGiIiYptAQERHTFBoiImKaQkNERExTaIiIiGkKDRERMU2hISIippkKjQu9/Oi7776r8WJERKRuu2RoFBQUUFBQwCOPPMKpU6ccw7m5uUyePLm2ahQRkTrikh0WPvHEE+zcuRPA8ZY9AKvVesFeaUVE5Op2ydBYu3YtALNnz+bZZ5+tlYJERKTuMtU1+rPPPktGRganTp3CMP776tQOHTo4rTAREal7TIXG8uXLWbt2LU2bNnW0WSwWPvroI6cVJiIidY+p0EhNTeX999+nWbNmzq5HRETqMFO33DZv3lyBISIi5kIjODiYxYsXk56ezldffeX473KKi4sZPHgwJ06cAGDXrl2EhYXRr18/li5d6hjv0KFDDBs2jP79+zNnzhwqKioAyMzMJCIiggEDBjBx4kRKSkquZB1FRKSGmAqNTZs2kZaWRlRUFFOmTGHKlClMnTr1ktMcOHCA0aNHc/ToUQBKS0uJjo4mKSmJbdu2cfDgQT755BMAoqKiePrpp3nvvfcwDIPk5GQA4uLiCA8PJy0tjY4dO5KUlPQbVlVERH4rU6Gxffv28/673EXw5ORkYmNj8ff3B+CLL77gpptuomXLllitVsLCwkhLSyMjI4PS0lI6d+4MwLBhw0hLS8Nms7Fv3z7H8yC/tIuIiOuYuhD+8ssvX7D9oYceuug08+fPrzKcnZ2Nn5+fY9jf35+srKzz2v38/MjKyiI/Px9fX1+sVmuV9upq2tS32tOIc/n5NXR1CXWGvov661rddqZC4z//+Y/j5/Lycvbt20dwcHC1FmS327FYLI5hwzCwWCwXbf/l/+f69bAZJ08WY7cblx3vWt0BXCEnp6jG51lft58zvov6qD5uv6t127m5WS75x7bph/vOlZWVxZw5c6pVSEBAADk5OY7hnJwc/P39z2vPzc3F39+fJk2aUFRURGVlJe7u7o7xRUTEda6oa/RmzZqRkZFRrWn++Mc/cuTIEY4dO0ZlZSVvv/02ISEhBAYG4uXlRXp6OgBbtmwhJCQEDw8PgoKC2LZtG3D2WZGQkJArKVdERGpIta9pGIbBwYMHqzwdboaXlxcLFy5kypQplJWV0atXLwYMGABAQkICMTExFBcX06FDByIjIwGIjY1l1qxZrFy5kubNm5OYmFitZYqISM2q9jUNOPuw38yZM00tYPv27Y6fg4OD2bp163njtG/fnpSUlPPaAwMDWbdunanliIiI81XrmkZGRgYVFRXcdNNNTi1KRETqJlOhcezYMf7yl7+QnZ2N3W6ncePGrFq1ijZt2ji7PhERqUNMXQiPj49n/Pjx7Nu3j/T0dCZOnEhcXJyzaxMRkTrGVGicPHmSoUOHOoaHDx9Ofn6+04oSEZG6yVRoVFZWUlBQ4BjOy8tzVj0iIlKHmbqm8cADD3D//fdzzz33YLFY2LZtGw8++KCzaxMRkTrG1JFGr169ALDZbHz//fdkZWURGhrq1MJERKTuMXWkMWvWLCIiIoiMjKSsrIzXXnuN6OhoVq9e7ez6RESkDjF1pJGfn+94StvLy4uxY8dW6S9KRESuDaYvhJ/bLXlubi6GcfmeY0VE5Opi6vTU2LFjue+++7jrrruwWCzs2rXLdDciIiJy9TAVGiNGjKBjx47s2bMHd3d3Hn74Ydq1a+fs2kREpI4xFRpwtlPB9u3bO7MWERGp467ofRoiInJtUmiIiIhpCg0RETFNoSEiIqYpNERExDTTd0/VlDfeeIP169c7hk+cOMGQIUM4c+YM6enp+Pj4ADB58mRCQ0M5dOgQc+bMoaSkhKCgIOLi4rBaa71sERHBBaExcuRIRo4cCcC3337LpEmTmDx5Mg8++CDr16/H39+/yvhRUVHMmzePzp07Ex0dTXJyMuHh4bVdtoiI4OLTU3PnzuXxxx/Hx8eHzMxMoqOjCQsLY/ny5djtdjIyMigtLaVz584ADBs2jLS0NFeWLCJyTXNZaOzatYvS0lLuuececnNz6d69OwsWLCA5OZn9+/eTkpJCdnY2fn5+jmn8/Pyq9IElIiK1y2UXB15//XUeeughAFq2bMmKFSscn40ZM4bU1FTatGmDxWJxtBuGUWXYjKZNfWumYKkxfn4NXV1CnaHvov66VredS0KjvLycffv2sXDhQgAOHz7M0aNH6d+/P3A2HKxWKwEBAVW6YM/NzT3vmsflnDxZjN1++R55r9UdwBVycopqfJ71dfs547uoj+rj9rtat52bm+WSf2y75PTU4cOHufnmm7nuuuuAsyGxYMECTp06hc1mY+PGjYSGhhIYGIiXlxfp6ekAbNmyhZCQEFeULCIiuOhI4/jx4wQEBDiG27dvz6OPPsro0aOpqKigX79+DB48GICEhARiYmIoLi6mQ4cOjpdBiYhI7XNJaAwcOJCBAwdWaYuIiCAiIuK8cdu3b09KSkptlSYiIpegJ8JFRMQ0hYaIiJim0BAREdMUGiIiYppCQ0RETFNoiIiIaQoNERExTaEhIiKmKTRERMQ0hYaIiJim0BAREdMUGiIiYppCQ0RETFNoiIiIaQoNERExTaEhIiKmKTRERMQ0l7y5T0TAXmHDz6+hq8swraK8jPxT5a4uQ1xMoSHiIm5WD9IXj3d1GaZ1nbkGUGhc61wSGmPGjCEvLw+r9ezi4+PjKSkp4dlnn6WsrIx77rmHxx9/HIBDhw4xZ84cSkpKCAoKIi4uzjGdiIjUrlr/7WsYBkePHuVf//qX45d/aWkpAwYMYN26dTRv3pzHHnuMTz75hF69ehEVFcW8efPo3Lkz0dHRJCcnEx4eXttli4gILrgQ/sMPPwAwbtw47r33XtavX88XX3zBTTfdRMuWLbFarYSFhZGWlkZGRgalpaV07twZgGHDhpGWllbbJYuIyM9q/UijsLCQ4OBgnnrqKWw2G5GRkYwfPx4/Pz/HOP7+/mRlZZGdnV2l3c/Pj6ysrGotr2lT3xqrXWpGfbr4K1Vp2/3Xtfpd1HpodOnShS5dujiGR4wYwfLly+nataujzTAMLBYLdrsdi8VyXnt1nDxZjN1uXHa8a3UHcIWcnKIan6e2X+1wxraD+rn9nPVduJqbm+WSf2zX+ump/fv3s3v3bsewYRgEBgaSk5PjaMvJycHf35+AgIAq7bm5ufj7+9dqvSIi8l+1HhpFRUUsXryYsrIyiouL2bx5MzNmzODIkSMcO3aMyspK3n77bUJCQggMDMTLy4v09HQAtmzZQkhISG2XLCIiP6v101N9+vThwIED3HfffdjtdsLDw+nSpQsLFy5kypQplJWV0atXLwYMGABAQkICMTExFBcX06FDByIjI2u7ZBER+ZlLHniYPn0606dPr9IWHBzM1q1bzxu3ffv2pKSk1FJlIiJyKep7SkRETFNoiIiIaQoNERExTaEhIiKmKTRERMQ0hYaIiJim0BAREdMUGiIiYppCQ0RETFNoiIiIaQoNERExTaEhIiKmKTRERMQ0hYaIiJim0BAREdMUGiIiYppCQ0RETFNoiIiIaS553esLL7zAu+++C0CvXr2YOXMms2fPJj09HR8fHwAmT55MaGgohw4dYs6cOZSUlBAUFERcXBxWq0vKFhG55tX6b99du3axY8cONm/ejMViYfz48XzwwQccPHiQ9evX4+/vX2X8qKgo5s2bR+fOnYmOjiY5OZnw8PDaLltERHDB6Sk/Pz9mzZqFp6cnHh4etGnThszMTDIzM4mOjiYsLIzly5djt9vJyMigtLSUzp07AzBs2DDS0tJqu2QREflZrR9ptG3b1vHz0aNHeffdd9mwYQN79+4lNjaWhg0b8thjj5GSkkLbtm3x8/NzjO/n50dWVlZtlywiIj9z2cWBb7/9lscee4yZM2fSunVrVqxY4fhszJgxpKam0qZNGywWi6PdMIwqw2Y0bepbYzVLzfDza+jqEuQKadv917X6XbgkNNLT05k6dSrR0dEMGjSIw4cPc/ToUfr37w+cDQer1UpAQAA5OTmO6XJzc8+75nE5J08WY7cblx3vWt0BXCEnp6jG56ntVzucse2gfm4/Z30XrubmZrnkH9u1fk3jxx9/ZNKkSSQkJDBo0CDgbEgsWLCAU6dOYbPZ2LhxI6GhoQQGBuLl5UV6ejoAW7ZsISQkpLZLFhGRn9X6kcbatWspKytj4cKFjrZRo0bx6KOPMnr0aCoqKujXrx+DBw8GICEhgZiYGIqLi+nQoQORkZG1XbKIiPys1kMjJiaGmJiYC34WERFxXlv79u1JSUlxdlkiImKCnggXERHTFBoiImKaQkNERExTaIiIiGkKDRERMU2hISIipik0RETENIWGiIiYptAQERHTFBoiImKa3psqIlJN9gpbveuZt6K8jPxT5b95PgoNEZFqcrN6kL54vKvLqJauM9cAvz00dHpKRERMU2iIiIhpCg0RETFNoSEiIqYpNERExDSFhoiImKbQEBER0+pFaLz11lsMHDiQfv36sWHDBleXIyJyzarzD/dlZWWxdOlSNm3ahKenJ6NGjaJbt27ccsstri5NROSaU+dDY9euXXTv3p1GjRoB0L9/f9LS0pg8ebKp6d3cLKaXdUPjBldSokt5Xt/U1SVUW3W2SXVo+zmfs7Yd1L/tV9+2HZjbfpcbx2IYhlFTBTnDqlWrOH36NI8//jgAb7zxBl988QXPPPOMiysTEbn21PlrGna7HYvlv8lnGEaVYRERqT11PjQCAgLIyclxDOfk5ODv7+/CikRErl11PjTuvPNOdu/eTV5eHmfOnOH9998nJCTE1WWJiFyT6vyF8GbNmvH4448TGRmJzWZjxIgRdOrUydVliYhck+r8hXAREak76vzpKRERqTsUGiIiYppCQ0RETFNoiIiIaQqNeuDEiRPceuut7Ny5s0r73XffzYkTJ1xUlVxKfHw8U6dOrdK2Y8cO+vbtS3FxsYuqqntqYt/OysrikUceueQ4X3zxBUuWLLniOs0oKipi0qRJTl3GLz766COWLVtWK8v6NYVGPeHh4cFTTz2lXzj1xBNPPMHBgwf56KOPADh9+jRz585lwYIF+Pr6uri6uuW37tvNmjVj9erVlxznu+++4+TJk1c0f7NOnTrFoUOHnLqMX/Tt25dp06bVyrJ+rc4/pyFn+fv7c+edd7Jo0aLz+t168cUX2bp1K+7u7vTo0YOoqCjc3d1dVKkANGjQgHnz5hEdHU1wcDDLly/n7rvvxsfHh9GjR1NaWkrjxo2Ji4ujZcuWvPzyy2zevBk3Nzc6depEfHy8q1eh1lxq34bL798nTpwgMjKS7du3M2vWLHx9ffnqq6/Iyspi0qRJhIaGsnz5ck6fPs3KlSt59NFHWbx4MXv37qWyspJhw4YxduxYPvvsM5YsWYLdbqdt27bceOONZGVlcezYMTIyMhg5ciQTJ06ksrLygtPPmzeP7OxsJk2axIoVKxz1FRcXM2PGDHJzcwGYNGkSffv25dixY8ydO5eCggK8vb156qmnuP3225k1axYFBQUcO3aMJ554gpSUFF588UUA1q1bx7Fjx7j99tvZu3cvCxcuZNeuXSxcuBDDMGjRogXPPfccPj4+F6yxRhhS5x0/ftzo06ePUVRUZPTu3dvYsWOHYRiG0adPH2PDhg3GyJEjjdOnTxs2m82YMGGCsX79ehdXLL+YM2eOMXXqVGPw4MFGYWGhERYWZmRkZBiGYRiffvqp8eCDDxoVFRVGt27djPLycqOystKYNWuW8dNPP7m48tpxqX37+PHjxscff3zZ/fuXeRiGYTz55JPGpEmTDLvdbnzzzTfGHXfcYRiGYbz55pvGk08+aRiGYbz66qvGggULDMMwjLKyMuOBBx4w9u3bZ+zZs8fo2rWrUVhYaBiGYSxfvtwYMWKEUVZWZuTm5hqdO3c2Tp06ddHpz63jXJs2bTLmzp1rGIZhfP3118bChQsNwzCM+++/3/jqq68MwzCMb7/91ujXr59jHX6ptby83OjRo4dRUFDgmObAgQOO9SkrKzOCg4ONr7/+2jAMw0hISDD++c9/XrTGmqAjjXrE19eXZ555hqeeeoqtW7cCsGfPHgYNGoSPjw8Aw4cPJzU1lYiICFeWKj+bNWsWvXv3ZsWKFfz4448cP36ciRMnOj4vLi7G3d2dLl26MGLECPr27ctDDz1Es2bNXFh17bvQvg1Xtn/36NEDi8VCu3btKCgoOO/z3bt3c+jQIfbs2QOcPXV4+PBhbrnlFlq1akXDhg0d43br1g1PT0+aNm1Ko0aNKCoquuj0AQEBF6ynS5cuJCYmkpWVRe/evZk0aRIlJSUcPHiQ2bNnO8Y7ffo0+fn5AI5eLzw8PAgNDeX999+nR48eFBQU0KlTJ7777jsADh8+TLNmzbjtttuAs6dFAaZOnXrBGoOCgi76vZml0Khnevbs6TiUh7O9AP9aRUVFbZclF+Hr68v1119PYGAgxcXF3HjjjWzZsgWAyspKxymLpKQkPv/8cz799FPGjx9PQkICd9xxhytLr3W/3rfhyvZvLy8vgIv2hl1ZWUlUVBT9+vUDIC8vjwYNGvD555/j7e19wXn9Mj/DMC46/bkdq57r5ptv5t133+Xf//43//rXv/j73//OG2+8gaenp2NfAPjpp58c7w06t44hQ4awbNkyTp06RVhYWJV5e3h4VFnPoqIiSkpKLlpjTdCF8Hpo1qxZ7Nixg+zsbLp3784777xDaWkpFRUVvPnmm3Tv3t3VJcoFtG7dmlOnTrF//34A3nzzTf7617+Sl5fHwIEDadeuHdOmTaNHjx4cPnzYxdW6xrn7NlBj+7e7u7sjbLp3705ycjI2m42SkhLCw8P5/PPPTc/rYtNbrdYLBtr69et5/vnnueeee4iNjSUvLw/DMLj55psdobFz586LHj117tyZ7OxstmzZwr333lvls1atWnHy5EnHkceaNWt47bXXfvM6XoqONOqhXw7lH374YXr37k1hYSHDhw+noqKCnj178sADD7i6RLkAT09Pli1bxvz58ykrK8PX15dFixbRpEkT7r//fkaMGIGPjw+tWrVi+PDhri7XJc7dtwH69OnDoUOHfvP+3alTJ1544QUSEhKYNm0ax44dY+jQoVRUVDBs2DC6devGZ599Zmpeo0aNuuD0NpuNFi1aMGbMGNatW+cY/7777mPGjBmEhYXh7u5OVFQU119/PUuWLGHu3LmsWbMGDw8Pli5detGjo3vuuYcdO3bQsmXLKu1eXl4sWbKEmTNnYrPZ+P3vf8/ixYvx9PS8YI01QR0WioiIaTo9JSIipik0RETENIWGiIiYptAQERHTFBoiImKabrmVq8KJEycIDQ2lXbt2wNmHwho0aEBkZCQDBw68onk+8sgjPPnkk9xyyy3VnnbTpk3Mnz+fG2+80fFQmI+PD08++SRdunSp1rxeeOEF2rdvz5///Odq1wHwxhtvUF5eTkREBK+99hpFRUU8+uijVzQvEYWGXDW8vb2rPGGbkZHB2LFjcXd3p3///tWe3+V6Tr2coKAgVq1a5Rjevn07U6ZM4eOPP8ZqNf9P77PPPrui4PpFeno6bdu2BWD06NFXPB8RUGjIVSwwMJCpU6eydu1a+vTpQ0JCAvv27aOyspLbb7+dmJgYPv/8cxYtWsRbb70FQGFhIX379uXDDz9k6NChLFu2jD/84Q+kpKTw8ssv4+bmRuPGjVm0aBHNmzdn+/btrFy5EpvNhre39yWPJIKDg8nJyaGwsJDFixdTUFDA8ePH6d27NxMmTCAuLo5vvvkGi8XCXXfdxYwZM9i4cSMHDx5k8eLFuLu706tXrwuuh6+vL0eOHOHpp58mLy8PNzc3Jk6ciIeHB9u3b2fnzp14e3uTl5dHfn4+Tz/9NN9++y3x8fEUFBRgsVgYN24c9913H5999hlLly6lZcuWfPvtt1RUVBAXF0fXrl1rc/NJHaVrGnJVa9++Pf/5z3946aWXcHd3Z9OmTWzduhV/f38SEhLo0aMHJSUlfPnllwC8/fbb9OrVi9/97neOeXzzzTckJCSwZs0a3nrrLe6++25WrlzJ0aNHWbp0KS+99BKpqak888wzTJkyhdOnT59Xh2EYbNy4kXbt2tGkSRMASktLeeedd4iKimLevHk0atSIt956izfffJPDhw/z97//nYiICDp27MjMmTMJDQ296HoAzJgxgwEDBvDOO+/w0ksvkZiYSHBwMHfffTdjx46t0k1FRUUFEydOZMyYMbz11lusXr2axMRE/vd//xc4+9KicePGkZqayrBhw1i6dKnTtpHULzrSkKuaxWLB29ubjz/+mKKiInbt2gWAzWajadOmWCwWhg8fzubNm/nDH/7Apk2bmDlzZpV57N69m549e9K8eXMAx3sJNmzYQHZ2dpX3FFgsFv7v//4PgP379zNkyBAsFgvl5eW0bt2a5cuXO8Y99y/3Tz/9lNdeew2LxYKnpyejRo3ilVdeOe/aw8XWo6CggG+++YaRI0cC0Lx5cz788MOLfi9Hjx6lrKzM0aFds2bN6NevH//+97/p1q0bLVq0cPScevvtt7N582ZzX7hc9RQaclX78ssvadeuHcXFxURHR9OrVy8ASkpKKCsrA2DEiBEMHTqUkSNHUlRUdF7vsu7u7lX6BCotLSUjIwO73U5wcDB/+9vfHJ/9+OOP+Pv78/XXX593TePXrrvuOsfPdru9yjLsdvsFO7+z2+0XXI9frpGcO48ffviBFi1aXHDZlZWV5/VzZBiGY5nn9rL6y4V8EdDpKbmKHTlyhKSkJMaNG0fPnj3ZsGED5eXl2O12nnrqKRITE4Gzf2V36tSJp59+mhEjRpw3n27durF7925Hz6uvv/46S5YsITg4mJ07d/L9998D8Mknn3DvvfdSWlpa7Vp79uzJ+vXrMQyD8vJykpOTufPOO4GqPbRebD18fX3p0KEDqampwNnwGj16NEVFRVWm/0Xr1q2xWq28//77wNn3bL/33nuOZYpcjI405KpRWlrKkCFDAHBzc8PLy4sZM2bQu3dvunfvzqJFixg6dCiVlZXcdtttzJo1yzHtyJEjmTZtGitXrjxvvrfeeitRUVGMHz8eAD8/PxYsWECzZs2Ij49nxowZGIaB1Wpl5cqVV/TegpiYGObNm0dYWBg2m4277rqLCRMmAHD33XeTmJiIzWbjL3/5y0XX47nnniMuLo5169ZhsViYP38+fn5+hISEsHDhwirL8/DwICkpiXnz5vH8889TWVnJpEmT6N69u+neXuXapF5uRUTENJ2eEhER0xQaIiJimkJDRERMU2iIiIhpCg0RETFNoSEiIqYpNERExDSFhoiImPb/AVaAPrx8zLhVAAAAAElFTkSuQmCC",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"DeviceProtection\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Kunden, die keinen zus\u00e4tzlichen Ger\u00e4teschutz erworben haben, werden mit gr\u00f6\u00dferer Wahrscheinlichkeit migrieren."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Technischer Support"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 169,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"TechSupport\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Kunden, die keinen technischen Support in Anspruch nehmen, werden eher abwandern."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Streaming-TV/ Streaming-Filme"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 170,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "for col in [\"StreamingTV\", \"StreamingMovies\"]:\n",
-                "    sns.countplot(x=col, hue='Churn', data=data_no_mv)\n",
-                "    plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Die Hinzunahme von Film- und TV-Streaming-Angeboten hat kaum Auswirkungen auf die Abwanderungsrate."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Papierlose Abrechnung"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 171,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"PaperlessBilling\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Kunden, die ohne Rechnung bezahlen, werden eher abwandern."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Zahlungsmethode"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 172,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"PaymentMethod\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Kunden, die mit elektronischen Schecks bezahlen, wandern deutlich h\u00e4ufiger ab als Kunden, die eine andere Zahlungsmethode verwenden."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Vertrag"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 173,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "sns.countplot(x=\"Contract\", hue=\"Churn\", data=data_no_mv)\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Bei Kunden mit kurzfristigen Vertr\u00e4gen ist die Wahrscheinlichkeit gr\u00f6\u00dfer, dass sie das Unternehmen verlassen, als bei Kunden mit l\u00e4ngerfristigen Vertr\u00e4gen."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# 3. Aufbereitung der Daten"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 3.1. Reduzieren der Kunden-ID"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 174,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Removing the Customer ID, it does not add value to the model\n",
-                "data_prep = data_no_mv.drop(\"customerID\", axis = 1)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 3.2. Umkodierung der kategorialen Variablen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 175,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Convert binary variables to 1 and 0 with Yes and No\n",
-                "bin_var = [\"Partner\",\"Dependents\",\"PhoneService\",\"PaperlessBilling\",\"Churn\"]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 176,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "def binaer_umwandeln(x):\n",
-                "    return x.map({'Yes':1,'No':0})"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 177,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>gender</th>\n",
-                            "      <th>SeniorCitizen</th>\n",
-                            "      <th>Partner</th>\n",
-                            "      <th>Dependents</th>\n",
-                            "      <th>tenure</th>\n",
-                            "      <th>PhoneService</th>\n",
-                            "      <th>MultipleLines</th>\n",
-                            "      <th>InternetService</th>\n",
-                            "      <th>OnlineSecurity</th>\n",
-                            "      <th>OnlineBackup</th>\n",
-                            "      <th>DeviceProtection</th>\n",
-                            "      <th>TechSupport</th>\n",
-                            "      <th>StreamingTV</th>\n",
-                            "      <th>StreamingMovies</th>\n",
-                            "      <th>Contract</th>\n",
-                            "      <th>PaperlessBilling</th>\n",
-                            "      <th>PaymentMethod</th>\n",
-                            "      <th>MonthlyCharges</th>\n",
-                            "      <th>TotalCharges</th>\n",
-                            "      <th>Churn</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>Female</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>No phone service</td>\n",
-                            "      <td>DSL</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Month-to-month</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>Electronic check</td>\n",
-                            "      <td>29.85</td>\n",
-                            "      <td>29.85</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>Male</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>34</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>DSL</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>One year</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>Mailed check</td>\n",
-                            "      <td>56.95</td>\n",
-                            "      <td>1889.50</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>Male</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>DSL</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Month-to-month</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>Mailed check</td>\n",
-                            "      <td>53.85</td>\n",
-                            "      <td>108.15</td>\n",
-                            "      <td>1</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>Male</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>45</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>No phone service</td>\n",
-                            "      <td>DSL</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>Yes</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>One year</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>Bank transfer (automatic)</td>\n",
-                            "      <td>42.30</td>\n",
-                            "      <td>1840.75</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>Female</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Fiber optic</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>No</td>\n",
-                            "      <td>Month-to-month</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>Electronic check</td>\n",
-                            "      <td>70.70</td>\n",
-                            "      <td>151.65</td>\n",
-                            "      <td>1</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
-                            "0  Female              0        1           0       1             0   \n",
-                            "1    Male              0        0           0      34             1   \n",
-                            "2    Male              0        0           0       2             1   \n",
-                            "3    Male              0        0           0      45             0   \n",
-                            "4  Female              0        0           0       2             1   \n",
-                            "\n",
-                            "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
-                            "0  No phone service             DSL             No          Yes   \n",
-                            "1                No             DSL            Yes           No   \n",
-                            "2                No             DSL            Yes          Yes   \n",
-                            "3  No phone service             DSL            Yes           No   \n",
-                            "4                No     Fiber optic             No           No   \n",
-                            "\n",
-                            "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
-                            "0               No          No          No              No  Month-to-month   \n",
-                            "1              Yes          No          No              No        One year   \n",
-                            "2               No          No          No              No  Month-to-month   \n",
-                            "3              Yes         Yes          No              No        One year   \n",
-                            "4               No          No          No              No  Month-to-month   \n",
-                            "\n",
-                            "   PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  \\\n",
-                            "0                 1           Electronic check           29.85         29.85   \n",
-                            "1                 0               Mailed check           56.95       1889.50   \n",
-                            "2                 1               Mailed check           53.85        108.15   \n",
-                            "3                 0  Bank transfer (automatic)           42.30       1840.75   \n",
-                            "4                 1           Electronic check           70.70        151.65   \n",
-                            "\n",
-                            "   Churn  \n",
-                            "0      0  \n",
-                            "1      0  \n",
-                            "2      1  \n",
-                            "3      0  \n",
-                            "4      1  "
-                        ]
-                    },
-                    "execution_count": 177,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "data_prep[bin_var]=data_prep[bin_var].apply(binaer_umwandeln)\n",
-                "data_prep.head()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 178,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
-                            "<style scoped>\n",
-                            "    .dataframe tbody tr th:only-of-type {\n",
-                            "        vertical-align: middle;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe tbody tr th {\n",
-                            "        vertical-align: top;\n",
-                            "    }\n",
-                            "\n",
-                            "    .dataframe thead th {\n",
-                            "        text-align: right;\n",
-                            "    }\n",
-                            "</style>\n",
-                            "<table border=\"1\" class=\"dataframe\">\n",
-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>SeniorCitizen</th>\n",
-                            "      <th>Partner</th>\n",
-                            "      <th>Dependents</th>\n",
-                            "      <th>tenure</th>\n",
-                            "      <th>PhoneService</th>\n",
-                            "      <th>PaperlessBilling</th>\n",
-                            "      <th>MonthlyCharges</th>\n",
-                            "      <th>TotalCharges</th>\n",
-                            "      <th>Churn</th>\n",
-                            "      <th>gender_Male</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>TechSupport_Yes</th>\n",
-                            "      <th>StreamingTV_No internet service</th>\n",
-                            "      <th>StreamingTV_Yes</th>\n",
-                            "      <th>StreamingMovies_No internet service</th>\n",
-                            "      <th>StreamingMovies_Yes</th>\n",
-                            "      <th>Contract_One year</th>\n",
-                            "      <th>Contract_Two year</th>\n",
-                            "      <th>PaymentMethod_Credit card (automatic)</th>\n",
-                            "      <th>PaymentMethod_Electronic check</th>\n",
-                            "      <th>PaymentMethod_Mailed check</th>\n",
-                            "    </tr>\n",
-                            "  </thead>\n",
-                            "  <tbody>\n",
-                            "    <tr>\n",
-                            "      <th>0</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>29.85</td>\n",
-                            "      <td>29.85</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>1</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>34</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>56.95</td>\n",
-                            "      <td>1889.50</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>2</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>53.85</td>\n",
-                            "      <td>108.15</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>45</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>42.30</td>\n",
-                            "      <td>1840.75</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>2</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>70.70</td>\n",
-                            "      <td>151.65</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>...</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>0</td>\n",
-                            "    </tr>\n",
-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>5 rows \u00d7 31 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   SeniorCitizen  Partner  Dependents  tenure  PhoneService  PaperlessBilling  \\\n",
-                            "0              0        1           0       1             0                 1   \n",
-                            "1              0        0           0      34             1                 0   \n",
-                            "2              0        0           0       2             1                 1   \n",
-                            "3              0        0           0      45             0                 0   \n",
-                            "4              0        0           0       2             1                 1   \n",
-                            "\n",
-                            "   MonthlyCharges  TotalCharges  Churn  gender_Male  ...  TechSupport_Yes  \\\n",
-                            "0           29.85         29.85      0            0  ...                0   \n",
-                            "1           56.95       1889.50      0            1  ...                0   \n",
-                            "2           53.85        108.15      1            1  ...                0   \n",
-                            "3           42.30       1840.75      0            1  ...                1   \n",
-                            "4           70.70        151.65      1            0  ...                0   \n",
-                            "\n",
-                            "   StreamingTV_No internet service  StreamingTV_Yes  \\\n",
-                            "0                                0                0   \n",
-                            "1                                0                0   \n",
-                            "2                                0                0   \n",
-                            "3                                0                0   \n",
-                            "4                                0                0   \n",
-                            "\n",
-                            "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
-                            "0                                    0                    0   \n",
-                            "1                                    0                    0   \n",
-                            "2                                    0                    0   \n",
-                            "3                                    0                    0   \n",
-                            "4                                    0                    0   \n",
-                            "\n",
-                            "   Contract_One year  Contract_Two year  \\\n",
-                            "0                  0                  0   \n",
-                            "1                  1                  0   \n",
-                            "2                  0                  0   \n",
-                            "3                  1                  0   \n",
-                            "4                  0                  0   \n",
-                            "\n",
-                            "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
-                            "0                                      0                               1   \n",
-                            "1                                      0                               0   \n",
-                            "2                                      0                               0   \n",
-                            "3                                      0                               0   \n",
-                            "4                                      0                               1   \n",
-                            "\n",
-                            "   PaymentMethod_Mailed check  \n",
-                            "0                           0  \n",
-                            "1                           1  \n",
-                            "2                           1  \n",
-                            "3                           0  \n",
-                            "4                           0  \n",
-                            "\n",
-                            "[5 rows x 31 columns]"
-                        ]
-                    },
-                    "execution_count": 178,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# create dummies\n",
-                "data_enc = pd.get_dummies(data_prep, drop_first=True)\n",
-                "data_enc.head()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 179,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Dropping of dummies that also contain No phone service and No Internet service\n",
-                "dup_variables = [\"OnlineSecurity_No internet service\",\"OnlineBackup_No internet service\", \"TechSupport_No internet service\",\"StreamingTV_No internet service\",\"StreamingMovies_No internet service\", \"DeviceProtection_No internet service\",\"MultipleLines_No phone service\"]\n",
-                "data_enc.drop(dup_variables, axis=1, inplace=True)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 3.3. Test auf Multikollinearit\u00e4t"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Um ein korrektes Funktionieren der sp\u00e4teren Regression zu gew\u00e4hrleisten, darf keine Multikollinearit\u00e4t zwischen den Variablen bestehen. Das Vorhandensein einer solchen wird mit Hilfe der Bibliothek Statsmodel \u00fcberpr\u00fcft."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 180,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "                                  feature         VIF\n",
-                        "0                           SeniorCitizen    1.376564\n",
-                        "1                                 Partner    2.824725\n",
-                        "2                              Dependents    1.969391\n",
-                        "3                                  tenure   20.482153\n",
-                        "4                            PhoneService   47.244378\n",
-                        "5                        PaperlessBilling    2.956951\n",
-                        "6                          MonthlyCharges  212.353073\n",
-                        "7                            TotalCharges   21.374002\n",
-                        "8                             gender_Male    2.021331\n",
-                        "9                       MultipleLines_Yes    2.861614\n",
-                        "10            InternetService_Fiber optic   17.695260\n",
-                        "11                     InternetService_No    8.234451\n",
-                        "12                     OnlineSecurity_Yes    2.682712\n",
-                        "13                       OnlineBackup_Yes    2.909898\n",
-                        "14                   DeviceProtection_Yes    2.992570\n",
-                        "15                        TechSupport_Yes    2.758343\n",
-                        "16                        StreamingTV_Yes    4.928957\n",
-                        "17                    StreamingMovies_Yes    5.090603\n",
-                        "18                      Contract_One year    2.056188\n",
-                        "19                      Contract_Two year    3.487502\n",
-                        "20  PaymentMethod_Credit card (automatic)    1.984196\n",
-                        "21         PaymentMethod_Electronic check    2.955994\n",
-                        "22             PaymentMethod_Mailed check    2.383290\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# independent variables\n",
-                "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
-                "  \n",
-                "# VIF dataframe \n",
-                "vif_data = pd.DataFrame() \n",
-                "vif_data[\"feature\"] = vif_test.columns \n",
-                "  \n",
-                "# VIF for each Feature \n",
-                "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
-                "                          for i in range(len(vif_test.columns))] \n",
-                "  \n",
-                "print(vif_data)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "\"MonthlyCharges\" hat den h\u00f6chsten VIF und wird aus dem Datensatz entfernt."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 181,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "data_enc.drop(\"MonthlyCharges\", axis=1, inplace=True)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 182,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "                                  feature        VIF\n",
-                        "0                           SeniorCitizen   1.366018\n",
-                        "1                                 Partner   2.817414\n",
-                        "2                              Dependents   1.961947\n",
-                        "3                                  tenure  17.073930\n",
-                        "4                            PhoneService   9.277446\n",
-                        "5                        PaperlessBilling   2.796488\n",
-                        "6                            TotalCharges  18.028499\n",
-                        "7                             gender_Male   1.942509\n",
-                        "8                       MultipleLines_Yes   2.514269\n",
-                        "9             InternetService_Fiber optic   4.186492\n",
-                        "10                     InternetService_No   3.473225\n",
-                        "11                     OnlineSecurity_Yes   1.986701\n",
-                        "12                       OnlineBackup_Yes   2.182678\n",
-                        "13                   DeviceProtection_Yes   2.299462\n",
-                        "14                        TechSupport_Yes   2.099655\n",
-                        "15                        StreamingTV_Yes   2.749724\n",
-                        "16                    StreamingMovies_Yes   2.771330\n",
-                        "17                      Contract_One year   2.056169\n",
-                        "18                      Contract_Two year   3.468149\n",
-                        "19  PaymentMethod_Credit card (automatic)   1.820729\n",
-                        "20         PaymentMethod_Electronic check   2.535918\n",
-                        "21             PaymentMethod_Mailed check   1.982063\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# the independent variables set \n",
-                "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
-                "  \n",
-                "# VIF dataframe \n",
-                "vif_data = pd.DataFrame() \n",
-                "vif_data[\"feature\"] = vif_test.columns \n",
-                "  \n",
-                "# VIF for each Feature \n",
-                "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
-                "                          for i in range(len(vif_test.columns))] \n",
-                "  \n",
-                "print(vif_data)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "\"TotalCharges\" hat den h\u00f6chsten VIF und wird aus dem Datensatz entfernt."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 183,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "data_enc.drop(\"TotalCharges\", axis=1, inplace=True)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 184,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "                                  feature       VIF\n",
-                        "0                           SeniorCitizen  1.363244\n",
-                        "1                                 Partner  2.816895\n",
-                        "2                              Dependents  1.956413\n",
-                        "3                                  tenure  7.530356\n",
-                        "4                            PhoneService  9.260839\n",
-                        "5                        PaperlessBilling  2.757816\n",
-                        "6                             gender_Male  1.931277\n",
-                        "7                       MultipleLines_Yes  2.426699\n",
-                        "8             InternetService_Fiber optic  3.581328\n",
-                        "9                      InternetService_No  3.321342\n",
-                        "10                     OnlineSecurity_Yes  1.947904\n",
-                        "11                       OnlineBackup_Yes  2.093763\n",
-                        "12                   DeviceProtection_Yes  2.241375\n",
-                        "13                        TechSupport_Yes  2.060410\n",
-                        "14                        StreamingTV_Yes  2.636855\n",
-                        "15                    StreamingMovies_Yes  2.661529\n",
-                        "16                      Contract_One year  2.055971\n",
-                        "17                      Contract_Two year  3.456061\n",
-                        "18  PaymentMethod_Credit card (automatic)  1.794059\n",
-                        "19         PaymentMethod_Electronic check  2.401970\n",
-                        "20             PaymentMethod_Mailed check  1.967082\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# the independent variables set \n",
-                "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
-                "  \n",
-                "# VIF dataframe \n",
-                "vif_data = pd.DataFrame() \n",
-                "vif_data[\"feature\"] = vif_test.columns \n",
-                "  \n",
-                "# calculating VIF for each feature \n",
-                "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
-                "                          for i in range(len(vif_test.columns))] \n",
-                "  \n",
-                "print(vif_data)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Keine der Variablen hat jetzt einen VIF von mehr als 10."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 3.4. Merkmalsskalierung"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 185,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Separate target variable and predictors\n",
-                "y = data_enc[\"Churn\"]\n",
-                "X = data_enc.drop(labels = [\"Churn\"], axis = 1)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 186,
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<div>\n",
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-                            "\n",
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-                            "  <thead>\n",
-                            "    <tr style=\"text-align: right;\">\n",
-                            "      <th></th>\n",
-                            "      <th>SeniorCitizen</th>\n",
-                            "      <th>Partner</th>\n",
-                            "      <th>Dependents</th>\n",
-                            "      <th>tenure</th>\n",
-                            "      <th>PhoneService</th>\n",
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-                            "      <th>gender_Male</th>\n",
-                            "      <th>MultipleLines_Yes</th>\n",
-                            "      <th>InternetService_Fiber optic</th>\n",
-                            "      <th>InternetService_No</th>\n",
-                            "      <th>...</th>\n",
-                            "      <th>OnlineBackup_Yes</th>\n",
-                            "      <th>DeviceProtection_Yes</th>\n",
-                            "      <th>TechSupport_Yes</th>\n",
-                            "      <th>StreamingTV_Yes</th>\n",
-                            "      <th>StreamingMovies_Yes</th>\n",
-                            "      <th>Contract_One year</th>\n",
-                            "      <th>Contract_Two year</th>\n",
-                            "      <th>PaymentMethod_Credit card (automatic)</th>\n",
-                            "      <th>PaymentMethod_Electronic check</th>\n",
-                            "      <th>PaymentMethod_Mailed check</th>\n",
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-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>-1.239504</td>\n",
-                            "      <td>1</td>\n",
-                            "      <td>1</td>\n",
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-                            "      <td>1</td>\n",
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-                            "    <tr>\n",
-                            "      <th>3</th>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0</td>\n",
-                            "      <td>0.512486</td>\n",
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-                            "    <tr>\n",
-                            "      <th>4</th>\n",
-                            "      <td>0</td>\n",
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-                            "      <td>0</td>\n",
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-                            "  </tbody>\n",
-                            "</table>\n",
-                            "<p>5 rows \u00d7 21 columns</p>\n",
-                            "</div>"
-                        ],
-                        "text/plain": [
-                            "   SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
-                            "0              0        1           0 -1.280248             0   \n",
-                            "1              0        0           0  0.064303             1   \n",
-                            "2              0        0           0 -1.239504             1   \n",
-                            "3              0        0           0  0.512486             0   \n",
-                            "4              0        0           0 -1.239504             1   \n",
-                            "\n",
-                            "   PaperlessBilling  gender_Male  MultipleLines_Yes  \\\n",
-                            "0                 1            0                  0   \n",
-                            "1                 0            1                  0   \n",
-                            "2                 1            1                  0   \n",
-                            "3                 0            1                  0   \n",
-                            "4                 1            0                  0   \n",
-                            "\n",
-                            "   InternetService_Fiber optic  InternetService_No  ...  OnlineBackup_Yes  \\\n",
-                            "0                            0                   0  ...                 1   \n",
-                            "1                            0                   0  ...                 0   \n",
-                            "2                            0                   0  ...                 1   \n",
-                            "3                            0                   0  ...                 0   \n",
-                            "4                            1                   0  ...                 0   \n",
-                            "\n",
-                            "   DeviceProtection_Yes  TechSupport_Yes  StreamingTV_Yes  \\\n",
-                            "0                     0                0                0   \n",
-                            "1                     1                0                0   \n",
-                            "2                     0                0                0   \n",
-                            "3                     1                1                0   \n",
-                            "4                     0                0                0   \n",
-                            "\n",
-                            "   StreamingMovies_Yes  Contract_One year  Contract_Two year  \\\n",
-                            "0                    0                  0                  0   \n",
-                            "1                    0                  1                  0   \n",
-                            "2                    0                  0                  0   \n",
-                            "3                    0                  1                  0   \n",
-                            "4                    0                  0                  0   \n",
-                            "\n",
-                            "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
-                            "0                                      0                               1   \n",
-                            "1                                      0                               0   \n",
-                            "2                                      0                               0   \n",
-                            "3                                      0                               0   \n",
-                            "4                                      0                               1   \n",
-                            "\n",
-                            "   PaymentMethod_Mailed check  \n",
-                            "0                           0  \n",
-                            "1                           1  \n",
-                            "2                           1  \n",
-                            "3                           0  \n",
-                            "4                           0  \n",
-                            "\n",
-                            "[5 rows x 21 columns]"
-                        ]
-                    },
-                    "execution_count": 186,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# Scaling the variables\n",
-                "num_features = ['tenure']\n",
-                "\n",
-                "scaler = StandardScaler()\n",
-                "\n",
-                "X[num_features] = scaler.fit_transform(X[num_features])\n",
-                "X.head()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 3.5. Undersampling "
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 187,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "iht = InstanceHardnessThreshold(random_state=0,estimator=LogisticRegression (solver='lbfgs', multi_class='auto'))\n",
-                "                               \n",
-                "X_resampled, y_resampled = iht.fit_resample(X, y)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 3.6. Erstellen von Test- & Trainingsdaten"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 188,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Split dataset in train and test datasets\n",
-                "# The default value of 80% to 20% is used.\n",
-                "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, random_state=110)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# 4. Modellierung und Auswertung"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 4.1. Logistische Regression"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Zur L\u00f6sung des Problems wird die logistische Regression verwendet. Hierf\u00fcr werden die beiden Bibliotheken Statsmodels und Scikit-Learn verwendet. Die komplette Auswertung des Modells findet erst im Unterkapitel zu Scikit-Learn statt."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Statistische Modelle"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Training und Vorhersage"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 189,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Optimization terminated successfully.\n",
-                        "         Current function value: 0.082006\n",
-                        "         Iterations 11\n",
-                        "                           Logit Regression Results                           \n",
-                        "==============================================================================\n",
-                        "Dep. Variable:                  Churn   No. Observations:                 2803\n",
-                        "Model:                          Logit   Df Residuals:                     2781\n",
-                        "Method:                           MLE   Df Model:                           21\n",
-                        "Date:                Thu, 21 Oct 2021   Pseudo R-squ.:                  0.8817\n",
-                        "Time:                        15:00:28   Log-Likelihood:                -229.86\n",
-                        "converged:                       True   LL-Null:                       -1942.4\n",
-                        "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
-                        "=========================================================================================================\n",
-                        "                                            coef    std err          z      P>|z|      [0.025      0.975]\n",
-                        "---------------------------------------------------------------------------------------------------------\n",
-                        "const                                     5.1912      0.828      6.266      0.000       3.567       6.815\n",
-                        "SeniorCitizen                             0.4609      0.457      1.008      0.313      -0.435       1.357\n",
-                        "Partner                                  -0.4112      0.302     -1.362      0.173      -1.003       0.181\n",
-                        "Dependents                               -0.5746      0.294     -1.952      0.051      -1.151       0.002\n",
-                        "tenure                                   -2.9281      0.309     -9.468      0.000      -3.534      -2.322\n",
-                        "PhoneService                             -1.2307      0.544     -2.261      0.024      -2.298      -0.164\n",
-                        "PaperlessBilling                          1.2621      0.288      4.385      0.000       0.698       1.826\n",
-                        "gender_Male                              -0.1334      0.255     -0.524      0.600      -0.633       0.366\n",
-                        "MultipleLines_Yes                         1.0865      0.336      3.231      0.001       0.427       1.746\n",
-                        "InternetService_Fiber optic               3.1681      0.400      7.916      0.000       2.384       3.952\n",
-                        "InternetService_No                       -2.8314      0.567     -4.992      0.000      -3.943      -1.720\n",
-                        "OnlineSecurity_Yes                       -1.7901      0.321     -5.581      0.000      -2.419      -1.161\n",
-                        "OnlineBackup_Yes                         -0.3203      0.309     -1.036      0.300      -0.926       0.286\n",
-                        "DeviceProtection_Yes                      0.4336      0.331      1.312      0.190      -0.214       1.082\n",
-                        "TechSupport_Yes                          -0.8710      0.329     -2.648      0.008      -1.516      -0.226\n",
-                        "StreamingTV_Yes                           1.1971      0.351      3.414      0.001       0.510       1.884\n",
-                        "StreamingMovies_Yes                       1.4263      0.374      3.815      0.000       0.693       2.159\n",
-                        "Contract_One year                        -3.5720      0.488     -7.317      0.000      -4.529      -2.615\n",
-                        "Contract_Two year                        -6.5206      0.584    -11.164      0.000      -7.665      -5.376\n",
-                        "PaymentMethod_Credit card (automatic)    -0.0720      0.313     -0.230      0.818      -0.686       0.542\n",
-                        "PaymentMethod_Electronic check            1.2794      0.406      3.154      0.002       0.484       2.075\n",
-                        "PaymentMethod_Mailed check               -0.3240      0.398     -0.813      0.416      -1.105       0.457\n",
-                        "=========================================================================================================\n",
-                        "\n",
-                        "Possibly complete quasi-separation: A fraction 0.37 of observations can be\n",
-                        "perfectly predicted. This might indicate that there is complete\n",
-                        "quasi-separation. In this case some parameters will not be identified.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# add constant\n",
-                "X_const = sm.add_constant(X_train)\n",
-                "# create model\n",
-                "log_reg = sm.Logit(y_train, X_const).fit() \n",
-                "print(log_reg.summary())"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Das trainierte Modell zeigt statistisch nicht-signifikante Variablen an. Dies ist gegeben, wenn der Wert P>|z| gr\u00f6\u00dfer als 0,05 ist und es sich nicht um die Konstante handelt."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 190,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Removing the statistically non-significant features (P>|z|> 0.05)\n",
-                "insignificant_features = [\"Partner\", \"gender_Male\", \"OnlineBackup_Yes\", \"DeviceProtection_Yes\", \"PaymentMethod_Credit card (automatic)\",\"PaymentMethod_Mailed check\"]\n",
-                "X_train.drop(insignificant_features, axis=1, inplace=True)\n",
-                "X_test.drop(insignificant_features, axis=1, inplace=True)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Nun kann ein zweites Modell erstellt werden:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 191,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Optimization terminated successfully.\n",
-                        "         Current function value: 0.083077\n",
-                        "         Iterations 11\n",
-                        "                           Logit Regression Results                           \n",
-                        "==============================================================================\n",
-                        "Dep. Variable:                  Churn   No. Observations:                 2803\n",
-                        "Model:                          Logit   Df Residuals:                     2787\n",
-                        "Method:                           MLE   Df Model:                           15\n",
-                        "Date:                Thu, 21 Oct 2021   Pseudo R-squ.:                  0.8801\n",
-                        "Time:                        15:00:28   Log-Likelihood:                -232.87\n",
-                        "converged:                       True   LL-Null:                       -1942.4\n",
-                        "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
-                        "==================================================================================================\n",
-                        "                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
-                        "--------------------------------------------------------------------------------------------------\n",
-                        "const                              4.7119      0.718      6.566      0.000       3.305       6.118\n",
-                        "SeniorCitizen                      0.3954      0.458      0.864      0.387      -0.501       1.292\n",
-                        "Dependents                        -0.7328      0.262     -2.797      0.005      -1.246      -0.219\n",
-                        "tenure                            -2.9242      0.297     -9.845      0.000      -3.506      -2.342\n",
-                        "PhoneService                      -1.2073      0.540     -2.235      0.025      -2.266      -0.149\n",
-                        "PaperlessBilling                   1.2161      0.285      4.273      0.000       0.658       1.774\n",
-                        "MultipleLines_Yes                  1.0989      0.331      3.320      0.001       0.450       1.748\n",
-                        "InternetService_Fiber optic        3.1159      0.391      7.966      0.000       2.349       3.883\n",
-                        "InternetService_No                -2.8462      0.529     -5.381      0.000      -3.883      -1.809\n",
-                        "OnlineSecurity_Yes                -1.7441      0.313     -5.576      0.000      -2.357      -1.131\n",
-                        "TechSupport_Yes                   -0.8357      0.325     -2.569      0.010      -1.473      -0.198\n",
-                        "StreamingTV_Yes                    1.2193      0.348      3.508      0.000       0.538       1.901\n",
-                        "StreamingMovies_Yes                1.4394      0.368      3.908      0.000       0.717       2.161\n",
-                        "Contract_One year                 -3.4572      0.471     -7.337      0.000      -4.381      -2.534\n",
-                        "Contract_Two year                 -6.3299      0.557    -11.372      0.000      -7.421      -5.239\n",
-                        "PaymentMethod_Electronic check     1.3103      0.362      3.623      0.000       0.601       2.019\n",
-                        "==================================================================================================\n",
-                        "\n",
-                        "Possibly complete quasi-separation: A fraction 0.36 of observations can be\n",
-                        "perfectly predicted. This might indicate that there is complete\n",
-                        "quasi-separation. In this case some parameters will not be identified.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# new model\n",
-                "X_const = sm.add_constant(X_train)\n",
-                "log_reg2 = sm.Logit(y_train, X_const).fit() \n",
-                "print(log_reg2.summary())"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Keine statistisch nicht signifikanten Variablen mehr. Das endg\u00fcltige Modell wurde modelliert:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 192,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Optimization terminated successfully.\n",
-                        "         Current function value: 0.083077\n",
-                        "         Iterations 11\n",
-                        "                           Logit Regression Results                           \n",
-                        "==============================================================================\n",
-                        "Dep. Variable:                  Churn   No. Observations:                 2803\n",
-                        "Model:                          Logit   Df Residuals:                     2787\n",
-                        "Method:                           MLE   Df Model:                           15\n",
-                        "Date:                Thu, 21 Oct 2021   Pseudo R-squ.:                  0.8801\n",
-                        "Time:                        15:00:28   Log-Likelihood:                -232.87\n",
-                        "converged:                       True   LL-Null:                       -1942.4\n",
-                        "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
-                        "==================================================================================================\n",
-                        "                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
-                        "--------------------------------------------------------------------------------------------------\n",
-                        "const                              4.7119      0.718      6.566      0.000       3.305       6.118\n",
-                        "SeniorCitizen                      0.3954      0.458      0.864      0.387      -0.501       1.292\n",
-                        "Dependents                        -0.7328      0.262     -2.797      0.005      -1.246      -0.219\n",
-                        "tenure                            -2.9242      0.297     -9.845      0.000      -3.506      -2.342\n",
-                        "PhoneService                      -1.2073      0.540     -2.235      0.025      -2.266      -0.149\n",
-                        "PaperlessBilling                   1.2161      0.285      4.273      0.000       0.658       1.774\n",
-                        "MultipleLines_Yes                  1.0989      0.331      3.320      0.001       0.450       1.748\n",
-                        "InternetService_Fiber optic        3.1159      0.391      7.966      0.000       2.349       3.883\n",
-                        "InternetService_No                -2.8462      0.529     -5.381      0.000      -3.883      -1.809\n",
-                        "OnlineSecurity_Yes                -1.7441      0.313     -5.576      0.000      -2.357      -1.131\n",
-                        "TechSupport_Yes                   -0.8357      0.325     -2.569      0.010      -1.473      -0.198\n",
-                        "StreamingTV_Yes                    1.2193      0.348      3.508      0.000       0.538       1.901\n",
-                        "StreamingMovies_Yes                1.4394      0.368      3.908      0.000       0.717       2.161\n",
-                        "Contract_One year                 -3.4572      0.471     -7.337      0.000      -4.381      -2.534\n",
-                        "Contract_Two year                 -6.3299      0.557    -11.372      0.000      -7.421      -5.239\n",
-                        "PaymentMethod_Electronic check     1.3103      0.362      3.623      0.000       0.601       2.019\n",
-                        "==================================================================================================\n",
-                        "\n",
-                        "Possibly complete quasi-separation: A fraction 0.36 of observations can be\n",
-                        "perfectly predicted. This might indicate that there is complete\n",
-                        "quasi-separation. In this case some parameters will not be identified.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# final model\n",
-                "X_const = sm.add_constant(X_train)\n",
-                "log_reg_final = sm.Logit(y_train, X_const).fit() \n",
-                "print(log_reg_final.summary())"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 193,
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [],
-            "source": [
-                "# prediction\n",
-                "y_hat = log_reg_final.predict(sm.add_constant(X_test)) \n",
-                "# Statsmodel only gives the probabilities, therefore rounding is required.   \n",
-                "prediction = list(map(round, y_hat))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 4.1. Auswertung"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Zur Evaluation sollen mehrere Metriken verwendet werden, die komfortabler mittels Scikit-Learn erzeugt werden k\u00f6nnen. Deshalb wird das identische Modell wie mit Statsmodels nochmals in Scikit-Learn erzeugt."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Scikit-Learn"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Training und Vorhersage"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 194,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# C is needed to build the exact same model as with Statsmodels; source: https://www.kdnuggets.com/2016/06/regularization-logistic-regression.html\n",
-                "logistic_model = LogisticRegression(random_state=0, C=1e8)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 195,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# prediction with testdata\n",
-                "result = logistic_model.fit(X_train,y_train)\n",
-                "prediction_test = logistic_model.predict(X_test)\n",
-                "prediction_train = logistic_model.predict(X_train)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Evaluation"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 196,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Accuracy with testdata: 0.9882352941176471\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# Accuracy Score \n",
-                "acc = metrics.accuracy_score(y_test, prediction_test)\n",
-                "print('Accuracy with testdata: {}'.format(acc))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Die Genauigkeit deutet auf ein \u00fcberdurchschnittliches Modell hin. Allerdings handelt es sich um einen unausgewogenen Datensatz. Daher m\u00fcssen weitere Metriken analysiert werden.\n",
-                "\t\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 197,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "traindata:\n",
-                        "              precision    recall  f1-score   support\n",
-                        "\n",
-                        "           0       0.96      1.00      0.98      1374\n",
-                        "           1       1.00      0.96      0.98      1429\n",
-                        "\n",
-                        "    accuracy                           0.98      2803\n",
-                        "   macro avg       0.98      0.98      0.98      2803\n",
-                        "weighted avg       0.98      0.98      0.98      2803\n",
-                        "\n",
-                        "testdata:\n",
-                        "              precision    recall  f1-score   support\n",
-                        "\n",
-                        "           0       0.98      1.00      0.99       495\n",
-                        "           1       1.00      0.98      0.99       440\n",
-                        "\n",
-                        "    accuracy                           0.99       935\n",
-                        "   macro avg       0.99      0.99      0.99       935\n",
-                        "weighted avg       0.99      0.99      0.99       935\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# classification report\n",
-                "\n",
-                "print(\"traindata:\")\n",
-                "print(classification_report(y_train,prediction_train))\n",
-                "print(\"testdata:\")\n",
-                "print(classification_report(y_test,prediction_test))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "H\u00f6here Genauigkeit f\u00fcr das Training als f\u00fcr den Testdatensatz. Insgesamt sind die Werte f\u00fcr den Test- und den Trainingsdatensatz sehr \u00e4hnlich. Daher sollte nicht von einem Overfitting oder Underfitting ausgegangen werden."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 198,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "Text(0.5, 39.5, 'Predicted label')"
-                        ]
-                    },
-                    "execution_count": 198,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "image/png": 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y5cu1fft2rVmzRpLUr18/DR06VCtWrJDD4VBMTMx1r0+iBgAAcIW0tDQdOXIk15GWlnbZef/617/0ySefyNvbWydOnFBOTo7S0tJUpUoVVapUSd7e3oqIiFBsbKyOHj2qjIwM1a1bV5IUGRmp2NjY68bBYgIAAGAEVy4mmDNnjiZPnpxrPCoqSn369LlszMfHRxMnTtTMmTMVHh6u5ORkBQYGOl8PCgpSUlJSrvHAwEAlJSVdNw4SNQAAYARXJmqdOnVS69atc42XLFnyque/+OKLev7559WjRw8lJCTIZrP9EZfDIZvNJrvdftXx6yFRAwAAuELJkiWvmZT92f79+3XhwgXVqlVL/v7+atKkiWJjY+Xl5eU8JyUlRUFBQSpXrpxSUlKc48ePH1dQUNB1r88cNQAAYAaHzXXHDTpy5IgGDx6sCxcu6MKFC1q1apWefvppHTx4UImJicrJydGyZcsUFhamChUqyM/PT5s3b5YkLV26VGFhYde9PhU1AABgBE9seNuwYUPFxcWpVatW8vLyUpMmTdS8eXOVLVtWffr0UWZmpho2bKjw8HBJUnR0tAYPHqyzZ8+qdu3a6tix43Wvb3M4HA533Ehhl3X8gKdDAAol//INPB0CUGhlXzjq1s87FtbIZdcq98Nql13rVlBRAwAARnDYb7xlWVCQqAEAACPwrE8AAAC4DRU1AABgBMdNrNYsKEjUAACAEWh9AgAAwG2oqAEAACOw6hMAAMCiTNwZltYnAACARVFRAwAARihUrc9Tp05d942lS5d2cSgAAAB/XaFK1O6//37ZbDZd7VGgNptNu3btytfAAAAACrtrJmq7d+92ZxwAAAC3pFAuJrDb7ZoxY4YGDBigs2fPatq0acrJyXFHbAAAADfMYbe57LCKPBO1cePG6ddff9W2bdvkcDj0448/avTo0e6IDQAAoFDLM1HbsGGDxowZIz8/P5UoUUIzZ87UunXr3BEbAADADXM4bC47rCLP7Tm8vb1VpMgf+Zyvr6+8vdnVAwAAWIuJz/rMM+OqWbOm5s+fr5ycHB04cECzZ89WSEiIO2IDAAAo1PJsfQ4aNEg7duzQiRMn1K5dO507d04DBw50R2wAAAA3zO6wueywijwragEBARo1apQ7YgEAAPjLrDS3zFXyrKidOHFCr7zyiu677z49/PDDGjhwoNLS0twRGwAAQKGWZ6I2ePBgVapUSQsXLtS8efNUqlQpDR061B2xAQAA3DAT91HLs/V59OhRffjhh87v+/fvr4iIiHwNCgAA4GYVyicTBAUF6fDhw87vjx07psDAwHwNCgAA4GYVqopajx49JEmpqalq1aqVHnzwQRUpUkQbN25UcHCw2wIEAAAorK6ZqDVt2vSq440aNcqvWAAAAP4yK22r4SrXTNRat2591XGHw6HExMR8CwgAAOCvMHF7jjwXE3z++ecaN26czp8/7xwrW7Ysz/sEAADIZ3kmah999JFmzZqlDz/8UC+99JK+//57HTt2zB2xAQAA3DATV33mmaiVLl1aoaGhqlWrlk6cOKGePXuqWbNm7ogNAADghpk4Ry3P7Tm8vb11+vRpValSRXFxcZKknJycfA8MAACgsMszUXvqqafUvXt3NWrUSAsWLFBkZKSqVq3qjtgAAABumMNhc9lhFTaHI++Obnp6uooVK6akpCTFx8erQYMG8vPzc0d8xsg6fsDTIQCFkn/5Bp4OASi0si8cdevnbanU0mXXuvvwUpdd61Zcc47arFmzrvmmTz/9VF26dMmXgAAAAHDRNRO1PXv2uDMO4wVUbOjpEIBC6czyIZ4OAYCbmLiY4JqJ2ujRo90ZBwAAwC2x0twyV8lzMQEAAAA8I8991AAAAAqCQtX6BAAAKEgMfDBB3q1Pu92ujz/+WP3799fZs2c1bdo0NrwFAABwgzwrauPGjVNqaqri4+MlST/++KNSUlI0ePDgfA8OAADgRpnY+syzorZhwwaNGTNGfn5+CggI0MyZM7Vu3Tp3xAYAAHDDTHwywQ0967NIkT9O8/X1lbc3U9sAAADyW54ZV82aNTV//nzl5OTowIEDmj17tkJCQtwRGwAAwA2zezqAfJBnRW3QoEHasWOHTpw4oXbt2uncuXMaOHCgO2IDAAC4YQ7ZXHZYRZ4VtYCAAI0aNcodsQAAAOBP8kzURo4cedVxVn0CAAArsRu4kVqerc/SpUs7j+LFi2vTpk3uiAsAAOCm2GVz2WEVeVbUoqKiLvv++eefV8+ePfMtIAAAAFx00/tsBAQEKDk5OT9iAQAA+MustAjAVfJM1EaMGCGb7eKNOxwO7dixQ1WrVs33wAAAAG6Gidtz5JmolSlT5rLvH3/8cT3++OP5FhAAAAAuyjNRO3TokMaNG+eOWAAAAP6yQtn63L17txwOh7P9CQAAYEWFsvUZGBio5s2bKzQ0VMWLF3eOs48aAABA/rpmonbhwgX5+vqqXr16qlevnjtjAgAAuGmFqqLWtm1bLV68ONc+agAAAFZk4hy1az6ZwOEw8DkMAAAABcg1K2qZmZnauXPnNRO22rVr51tQAAAAN8tuXkHt2ona4cOH1adPn6smajabTatWrcrXwAAAAG6GlZ7R6SrXTNSqV6+uJUuWuDEUAAAA/NlNP+sTAADAikycXX/NRO2ee+5xZxwAAAC3xMTtOa656pMNbQEAADyL1icAADCC3cDHXZKoAQAAI5g4R+2arU8AAAB4FhU1AABghEK1mAAAAKAgsdtcd9yMyZMnq3nz5mrevLnGjRsnSVq/fr0iIiLUpEkTTZgwwXnurl27FBkZqaZNm2rQoEHKzs6+7rVJ1AAAAP6i9evXa+3atVq8eLGWLFmiHTt2aNmyZRo4cKCmTJmi5cuXa/v27VqzZo0kqV+/fho6dKhWrFghh8OhmJiY616fRA0AABjBLpvLjhsVGBioAQMGyNfXVz4+PqpWrZoSEhJUpUoVVapUSd7e3oqIiFBsbKyOHj2qjIwM1a1bV5IUGRmp2NjY616fOWoAAMAIrlz1mZaWprS0tFzjJUuWVMmSJZ3f16hRw/l1QkKCvvnmG7Vv316BgYHO8aCgICUlJSk5Ofmy8cDAQCUlJV03DhI1AACAK8yZM0eTJ0/ONR4VFaU+ffrkGt+7d6+6d++u119/XV5eXkpISHC+5nA4ZLPZZLfbZfvTXm+Xxq+HRA0AABjhZhcBXE+nTp3UunXrXON/rqZdsnnzZr344osaOHCgmjdvrk2bNiklJcX5ekpKioKCglSuXLnLxo8fP66goKDrxkGiBgAAjODK7TmubHFey++//67evXtrwoQJeuCBByRJoaGhOnjwoBITE1WxYkUtW7ZMbdq0UYUKFeTn56fNmzerfv36Wrp0qcLCwq57fRI1AACAv2jGjBnKzMzUmDFjnGNPP/20xowZoz59+igzM1MNGzZUeHi4JCk6OlqDBw/W2bNnVbt2bXXs2PG617c5HA4Tn7hgOX5FK3k6BKBQOrVskKdDAAot/8Y93Pp5syq0d9m1uhyd57Jr3QoqagAAwAiunKNmFSRqAADACDxCCgAAAG5DRQ0AABjBxIoaiRoAADCCw8A5arQ+AQAALIqKGgAAMAKtTwAAAIsyMVGj9QkAAGBRVNQAAIARTHzUEokaAAAwgolPJqD1CQAAYFFU1AAAgBFMXExAogYAAIxgYqJG6xMAAMCiqKgBAAAjsOoTAADAolj1CQAAALehogYAAIxg4mICEjUAAGAEE+eo0foEAACwKCpqAADACHYDa2okagAAwAgmzlGj9QkAAGBRVNQAAIARzGt8kqgBAABD0PoEAACA21BRAwAARjDxEVIkagAAwAgmbs9B6xMAAMCiqKgBAAAjmFdPI1EDAACGYNUnAAAA3IaKGgAAMIKJiwlI1AAAgBHMS9NofQIAAFgWFTUAAGAEExcTkKgBAAAjmDhHjdYnAACARVFRAwAARjCvnkaiBgAADGHiHDVanwAAABZFRQ0AABjBYWDzk0QNAAAYgdYnAAAA3IaKGgAAMIKJ+6iRqAEAACOYl6bR+gQAALCsApuopaWlaezYsfrPf/6j0NBQNW3aVB999JGysrIkScHBwVq/fr2HowQAAO5il8Nlh1UUyNbnqVOn1LZtW/3tb3/TyJEjVbFiRe3cuVMjR47Unj17FB0d7ekQYYhePTurZ8/OOn8+Q7t/3ae+fQfr5MlTng4LMMp32/Zp8JxYrR8fpTPnM/XWvG91MOmkHA6HIu77p7o0ufey85es367vtu3TxJ6tPBMwLMvEVZ8FMlGLjo6Wj4+PZs2aJT8/P0lSpUqVVKZMGXXo0EEdOnTwcIQwQcOGD+jVV3sprOHjOnr0mJ55JlJTPhijds/08HRogDESk09qwqIf5Pj/BYwpX61XUJkSin4+QuczsxQ58hPdXb2CQquW1+lzGZr0f2u1/Ofdql+jomcDB9ykwLU+L1y4oK+//lrPPvusM0m75F//+pfmzJmjmjVrSpK2bNmixx9/XHfddZeeeeYZHT58WJK0ceNGBQcHKzs72/neAQMG6LXXXpMkTZo0ST169FCHDh1077336ocfftAjjzyiefPm6emnn1ZoaKieeOIJxcXFuemu4Ql316uj775fq6NHj0mSliz5Rs2bN5aPj4+HIwPMcP5ClgbNjtWrbRo6x15/spFeaR0mSUo5fU5Z2dkK8L/4b/23W35VYKkA5+vAlRwu/I9VFLhE7dChQ0pPT9ddd9111dfvv/9++fv7S5JiYmL0xhtvaOHChTpz5ozeeeedG/6c77//Xk2bNtXcuXN19913S5ImT56s5557TgsWLJCfn59GjBhx6zcEy9r081Y1avSgKleuIEnq1PEp+fn56W9/K+PhyAAzjPz0v3ri4btUo8LfnWM2m03eXkU0cPY3euLtT3RPjUr6x20Xf+aebBCq7s3ul4+3l6dChsXZXXhYRYFL1NLS0iRJJUqUyPPc7t2764EHHlBwcLCeeOIJ7d69+4Y/p3Tp0mrfvr1CQkIUEBAgSWrVqpUaN26skJAQde3aVdu3b/9rN4ECYd26TXr77QmKWTBd69d9LbvdoRMnTurChQueDg0o8Bb8sE1eXkXU6sE7r/r6qM6PafXYHjqdnqFpy39yc3SAdRS4OWplylz8y+r06dN5nlu5cmXn1yVKlFBmZuYNf06FChVyjVWqVMn5dUBAgOx2u3JycuTlxV93JgoIKK4ff9io2bMXSJLKly+nYcNeU2rqKc8GBhjg/37aoYwL2Xpq1Dxl5+QoM+vi1+0fqaf7Q6ooqHSAihX1VXj9YK36315Ph4sCwkotS1cpcBW1ypUrq3Tp0oqPj7/q6y+99JL++9//SlKuBMrx/2er2my2XO/783w1Sbnmv0mSr69vrrFL14R5yt9+m779NkYlSlysqA7o30cxMUs9HBVghvmvP6MvB3dUzMD2mtSrlfx8vBUzsL227Duqact/ksPh0IWsbH27ZY/uDa6c9wUB0fq0BC8vLzVv3lzz5s3L1YL66aef9M033zirbtdyaTL42bNnnWNHjhxxfbAo0PbsPaDo6A+09sf/U3zcavn6+WrAG297OizAaK9EhulsRqaeeHuu2o39VP+sfJuebVTP02GhgLA7HC47rKLAtT4lKSoqSqtXr1aXLl3Up08flS9fXps3b9bYsWMVGRmp+vXrX/f9NWrUUNGiRTV58mR16tRJ//3vf7Vz505VrMhyb1zuw6lz9OHUOZ4OAzBahb+V0oYJUZKkksWKamzX5tc9v+UDtdXygdruCA3wuAJXUZOksmXL6rPPPlO1atXUv39/tWjRQtOnT9cLL7yg4cOH5/n+gIAAjRgxQqtWrVKLFi20fft2dezY0Q2RAwCA/OJw4WEVNgeTrNzCr2ilvE8C4HKnlg3ydAhAoeXf2L0bhD9TpbXLrvVp4mKXXetWFMiKGgAAQGFQIOeoAQAAXMnE7TlI1AAAgBGstK2Gq9D6BAAAsCgqagAAwAh2Wp8AAADWZOIcNVqfAAAAFkVFDQAAGMHExQQkagAAwAgm7uFP6xMAAOAWnD17Vi1atNCRI0ckSevXr1dERISaNGmiCRMmOM/btWuXIiMj1bRpUw0aNEjZ2dl5XptEDQAAGMEuh8uOG7Vt2za1a9dOCQkJkqSMjAwNHDhQU6ZM0fLly7V9+3atWbNGktSvXz8NHTpUK1askMPhUExMTJ7XJ1EDAABGsLvwSEtL05EjR3IdaWlpl31mTEyMhg0bpqCgIElSXFycqlSpokqVKsnb21sRERGKjY3V0aNHlZGRobp160qSIiMjFRsbm+c9MUcNAADgCnPmzNHkyZNzjUdFRalPnz7O799+++3LXk9OTlZgYKDz+6CgICUlJeUaDwwMVFJSUp5xkKgBAAAjuHIftU6dOql169a5xkuWLHnd99ntdtlstj9icjhks9muOZ4XEjUAAGAEVz6ZoGTJknkmZVdTrlw5paSkOL9PSUlRUFBQrvHjx48726XXwxw1AAAAFwkNDdXBgweVmJionJwcLVu2TGFhYapQoYL8/Py0efNmSdLSpUsVFhaW5/WoqAEAACNYYR81Pz8/jRkzRn369FFmZqYaNmyo8PBwSVJ0dLQGDx6ss2fPqnbt2urYsWOe17M5rHBXhYBf0UqeDgEolE4tG+TpEIBCy79xD7d+XtNKj7nsWisOf+Oya90KWp8AAAAWResTAAAYwZWrPq2CRA0AABjBlas+rYLWJwAAgEVRUQMAAEYwcX0kiRoAADACrU8AAAC4DRU1AABgBFZ9AgAAWJTdwDlqtD4BAAAsiooaAAAwgnn1NBI1AABgCFZ9AgAAwG2oqAEAACOYWFEjUQMAAEYw8ckEtD4BAAAsiooaAAAwAq1PAAAAizLxyQS0PgEAACyKihoAADCCiYsJSNQAAIARTJyjRusTAADAoqioAQAAI9D6BAAAsChanwAAAHAbKmoAAMAIJu6jRqIGAACMYDdwjhqtTwAAAIuiogYAAIxA6xMAAMCiaH0CAADAbaioAQAAI9D6BAAAsCgTW58kagAAwAgmVtSYowYAAGBRVNQAAIARaH0CAABYFK1PAAAAuA0VNQAAYASHw+7pEFyORA0AABjBTusTAAAA7kJFDQAAGMHBqk8AAABrovUJAAAAt6GiBgAAjEDrEwAAwKJMfDIBrU8AAACLoqIGAACMYOIjpEjUAACAEUyco0brEwAAwKKoqAEAACOYuI8aiRoAADACrU8AAAC4DRU1AABgBBP3USNRAwAARqD1CQAAALehogYAAIzAqk8AAACLovUJAAAAt6GiBgAAjMCqTwAAAIsy8aHstD4BAAAsiooaAAAwAq1PAAAAi2LVJwAAANyGihoAADCCiYsJSNQAAIARaH0CAADAbaioAQAAI5hYUSNRAwAARjAvTZNsDhPTTwAAAAMwRw0AAMCiSNQAAAAsikQNAADAokjUAAAALIpEDQAAwKJI1AAAACyKRA0AAMCiSNQAAAAsikQNAADAokjUAAAALIpEDQAAwKJI1AAAACyKRA2FksPhkCSdOnVK2dnZysnJ8XBEAADk5u3pAABPsNls+u677zR58mT5+/urbt266tmzpwICAjwdGmA0h8Mhm82mjIwMSVLRokUvGwdwOZvjUmkBKAQu/TLYvXu32rZtq86dO+v333/Xb7/9pqCgIA0fPpxkDcgnl37+Vq9erXnz5slms+mRRx5Ru3btLnsdwB9I1FDo/Pzzz0pKStLBgwfVp08f5eTkaNmyZVq4cKECAwNJ1oB8tGnTJvXs2VOPPvqoMjIy9O233yoqKkq9evWSRLIGXInWJwqFS//4nz59Wp9//rm+/vprNWnSRDk5OfLy8lKzZs3kcDj05Zdf6s0339SwYcNUokQJT4cNGOXQoUOKi4tTt27d1KtXL50/f15hYWEaMmSIHA6HevfuLZvNRrIG/AmJGgoFm82mFStWaOrUqfrggw/k7++vlStXateuXbrzzjvl4+OjFi1aqEiRIpo5c6bGjBmjkSNH8ssCuEWXkq6kpCQ9//zzOnLkiNq0aSNJ8vf3V4sWLWS32zVs2DB5eXmpR48e/NwBf0KihkLh8OHDmjdvnlq1aqWgoCANHTpUJ0+eVI8ePTRjxgwFBwfL29tbzZo1k7e3t0JDQ/llAbiAzWbTypUr9csvv2jUqFEaPXq09u/fr61bt6pevXry9fXV448/riJFimjgwIHy8fFRt27dPB02YBlszwHjHTlyRK+99pr27t2rGjVqyNvbW76+vpowYYLq1Kmjbt26ac+ePZLkTNYqVKjg4agBM+zevVszZsxQ2bJlVb9+fY0bN06nT5/WzJkzFR8fL0ny9fVVixYtNG7cODVq1MizAQMWQ6IGo50+fVoVK1ZURESEvL29tXTpUv3222+SLv5yeO+993T33XcrMjJS+/bt83C0gFkOHz6sSZMmKTk5WeHh4ZKkqlWr6v3339eBAwc0ffp0bd++XZKclbVq1ap5MmTAckjUYKz4+Hh16dJF69atU/v27dWzZ0/t3r1bCxYs0LFjxyRd/OUQHR2t8PBweXszEwBwlTNnzigoKEi1atVSamqqZsyY4XytWrVqmjhxohITEzV+/Hjt3LnTg5EC1kaiBmMFBwfL19dXkyZN0k8//aRnn31WkZGR+vHHH7VgwQIlJSVJ+iNZ+8c//uHZgAFD7Nq1Sy+//LLi4+PVu3dv9erVSz///LPee+895znVqlXTO++8o3Pnzqls2bKeCxawOEoIMMal1WWX/tvX11effPKJevTooejoaL322mvq1KmTJOnrr7/W+fPn1aVLF912220ejhwwy9mzZ+VwODRjxgwVKVJEXbt2lSTFxsbKZrOpb9++kqSaNWtq7ty58vX19WS4gKVRUYMxbDab4uLi9PrrrzsfT+Pr66upU6eqZMmSGj16tH7++Wd16tRJjRs31o4dO+Tj4+PhqIGC78p90++991716tVLXl5e+vDDDxUXF6cuXbooPDxca9eu1ZgxY5zn8jMIXB+JGoxgt9vlcDj066+/au/evXrrrbcuS9ZmzJghu92uMWPGaM2aNXrhhRc0efJkWi6AC9hsNv3444/6+OOPnWP169dX586dVbRoUU2ZMkW7du1S586d1aBBA+3YsUOpqanO9wK4NhI1FFhZWVnOv+RzcnJks9nUunVrde3aVQcPHtTQoUOVnp4u6eIvg0cffVSHDx/Wt99+q/T0dJUqVcqT4QPGcDgcOnTokKKjozV79mzn+D333KNOnTopOTlZ7777ruLi4tSrVy9NnDiRP5KAG8QcNRQ4q1at0n/+8x9ny+THH3/U4sWLFRgYqMaNG+vxxx+XJH322WcaPny43nzzTRUtWlTZ2dmKiopSeHi4ihUr5slbAAo8u92uIkUu/q1vs9n07LPPSpJGjBihnJwc56a199xzj+rUqaN169Zp4cKFql27tsqUKeOxuIGChkQNBcqRI0fUu3dvNWvWTOPHj9cvv/yi559/XuHh4Vq9erV2796tp556ypmszZ07V02aNFGtWrW0fv16ff311woKCvLwXQAFU3x8vKpXry5/f38VKVJE69ev1w8//KASJUqoefPmevbZZ+VwODRy5Eh5eXmpc+fOkqSSJUvqiSee0LPPPit/f3/P3gRQwNgcV84CBSzMbrdr3bp1eu211/TQQw+pfv36ys7OVqdOnZSYmKjp06crMTFRTz/9tJo3b67t27dr8eLFysrKUocOHVSjRg1P3wJQ4NjtdsXFxenpp5/WgAED1LlzZ61Zs0Z9+vTRnXfeqbS0NNntdo0fP14hISGaN2+eRo0apfr168vf31//+9//9OWXX6pSpUqevhWgwCFRQ4GQkZGhrKwseXl5qVixYlq7dq0GDBig1NRUDRgwQB07dpQkJSYm6qOPPtKhQ4f05JNPOitr2dnZbGgL3KKFCxfqzTffVL9+/ZSYmKiQkBA99dRT2r59u2bPnq1t27Zp0qRJCgkJ0Q8//KBPP/1UpUqVUteuXRUcHOzp8IECiUQNljdr1ixt3bpVW7Zska+vr15//XWFh4dr/fr1GjJkiEJCQvTBBx84zz906JA+/vhjbd++XR07dlSrVq2ce6sBuDnff/+99u7dq+TkZD300EPau3evxo8fr4CAAA0ZMkQtW7aUdPGZntOnT9e2bdv0/vvvq3bt2srMzFSRIkXYggO4BSRqsLR3331XixcvVlRUlDIzM5Wdna377rtPd955p3JycvTTTz/p5ZdfVlhYmKKjo53vO3jwoObPn6+uXbuqfPnyHrwDoOCKjo7WihUrVLFiRWVlZen06dP66quv9OWXX2rQoEHq3r27unfv7lyc8+uvv2rGjBn6/vvv9cknn6hWrVoevgOg4KMXBMtas2aNVq5cqSlTpqhOnTqS/lhp5nA45OXlpYceekgTJkzQyy+/rH79+umdd96RJN1xxx3q378/f8kDf9H8+fP11VdfaeLEiapVq5Z8fX2Vmpoqu92uNm3a6Pz58xo5cqQCAwPVunVrFS9eXMHBwercubP8/PxYWQ24CPuowbKOHj2qmjVrqlatWsrOzpaky7YDkKStW7fq/vvv1/jx47V27Vr16tXL+X6SNODmORwO2e12/fzzz+rSpYtCQ0Odj3gqW7asc+/C9u3b64UXXtCYMWO0aNEinT9/XpL0z3/+U0OGDFGVKlU8dg+ASUjUYFk7d+7UuXPn5OPjc9WFAKmpqerXr58WLVqkhx9+WKNHj9avv/7qfNg6gL/mzJkz2rJli+644w5Jlz8iysvLS5K0YsUK/f3vf1evXr00duxYzZ0797KngQBwDRI1WNZtt92mkydPOqtpdrvd+ZrdblfZsmVVt25dbdy4UZIUFhamZcuW8ZB14BbYbDaVKlVKpUqV0sGDB51jVypdurRGjRqlZs2aqX///poxY4YyMzPdHS5gPBI1WFazZs20f/9+TZ8+XdLFtuelZO1SCzQrK0tpaWnOMTbTBG6Nw+FQZmamKlSooA0bNujMmTNXPS80NFRBQUGKi4tThw4dFBsby2PZgHxAogZLstvtuuOOO9SjRw/NmjVL8+bNk/RHgpadne2stF1aaADg1tlsNvn5+alXr15at26d8w+lSy79sZSSkqLSpUsrMDBQkkjSgHzCqk9Y0qWELCIiQqmpqXr33Xd17NgxdejQQbfddptOnDihmJgYbdq0SX379vVwtIB56tSpo6FDh+rNN9/UhQsX1LlzZ5UrV875sxkTEyO73a7q1atL+uNnFoBrsY8aLC8pKUmrV6/W+PHjVbZsWZ05c0aVK1fW6dOnFR0dzV5NQD6x2+1atmyZhg0bppCQEAUHB6tUqVI6cuSI1q9fr5kzZ/LzB+QzEjVY0qX/W/55EnNqaqq2bNmiI0eOKDg4WFWrVmXhAOAGBw4c0Jdffqm4uDgVKVJEISEhatu2rapWrerp0ADjkajBoy492ikhIUEZGRlKT0/X3Xffnev1nJwc57YAADyLR7IB7kOiBo9bsWKFRo0aJV9fX6Wlpal27dp69dVXVatWLea9ABbx5+SMRA1wHxI1uN2f/5HftWuXXnjhBb3yyiuqU6eOfH191bdvX/n4+Gjo0KGqXbs2vxQAAIUW5Qq4zdSpU/XVV1/JZrM5l/gnJCSoQoUKatq0qapWrapKlSrps88+U3p6usaPHy/p6pttAgBQGJCowS2ys7N18uRJ9evXT99++62zpfn7778rNTVVxYoVk81m0/nz5+Xn56exY8dqy5Ytio+P93DkAAB4Doka3MLb21t9+/ZVr1699OKLLyo2NlaS9MgjjygpKUmTJk2SJOeTBbKzs1WuXDmVLVvWYzEDAOBpbHiLfHPlis1ixYqpW7dustvteumllzR+/Hg1a9ZM/fr108SJE5Wdna2oqCilp6drzZo18vb25pFQAIBCjUQN+SY+Pl61atWS3W53bq1RvHhxPffcc8rJydErr7wiX19ftW/fXsWLF9eIESO0fPlyFS1aVCdPntS0adOoqAEACjVWfSJffPHFFxoyZIhq1aqlsmXL6pFHHlFQUJAaNmwoX19fSdLkyZOdR+PGjXXs2DFt2bJFxYoVU82aNVW+fHkP3wUAAJ5FooZ8sX//fr300kvau3evqlevrvT0dJ07d07FixdXYGCgmjRpoqCgIP3888+KiYnR9OnT1aBBA0+HDQCApZCoId8kJiYqKipK9erVU+PGjfWPf/xDa9euVVxcnHbt2qUDBw6oTJkySk5OliSSNQAArkCihny1Z88e9e3bV9WrV9fLL7/sfDZgamqqzp07p61bt2rnzp3asmWLRo0aperVq3s4YgAArINEDflu7969zmTtueeeU506dXKdk5GRoaJFi3ogOgAArIt91JDvatSooYkTJ2rfvn36+OOPtWPHDudrOTk5kiQ/Pz9PhQcAgGVRUYPb7Nu3Ty+++KJCQkLUqVMnhYaGejokAAAsjYoa3KZ69eqaOHGiNm3apM8//1wXLlzwdEgAAFgaFTW43f79++Xj46PKlSt7OhQAACyNRA0AAMCiaH0CAABYFIkaAACARZGoAQAAWBSJGgAAgEWRqAEAAFgUiRqAW3bkyBHVqlVLLVu2dB6PP/64Fi5ceMvX7t69uxYtWiRJatmypdLS0q557pkzZ9SxY8eb/ozY2Fh16NAh1/jGjRvVokWLPN8fHBys1NTUm/rMAQMGaMaMGTf1HgCFj7enAwBghqJFi2rp0qXO75OSktSiRQvdeeedCgkJccln/Pn6V3P69GnFx8e75LMAwApI1ADki9tuu01VqlRRQkKCdu7cqYULF+r8+fMKCAjQ3Llz9cUXX+izzz6T3W5X6dKlNWTIEFWrVk1JSUkaMGCAkpOTVb58eZ04ccJ5zeDgYG3YsEFly5bVtGnTtHjxYnl7e6tKlSoaM2aM3njjDWVkZKhly5ZatGiREhIS9Pbbb+vUqVPKyclRhw4d9MQTT0iS3n//fX311VcqXbq0qlSpkuf9HDx4UMOHD9e5c+eUkpKikJAQvffee87n1L733nuKj4+X3W7XSy+9pH//+9+SdM37BIAbQaIGIF9s3bpVhw4dUmhoqDZs2KB9+/bpu+++U0BAgDZt2qQlS5Zo/vz58vf319q1axUVFaVvvvlGw4cPV2hoqF566SUlJiaqVatWua69atUqLVq0SDExMSpVqpRGjx6tefPmafTo0YqIiNDSpUuVnZ2tF198UePGjVPt2rV15swZtW3bVtWrV9fx48f17bffasmSJSpatKh69+6d5/3ExMSoVatWatmypbKyshQZGanVq1eradOmkqSKFStq+PDh2rNnjzp06KBvvvlG+/btu+Z9AsCNIFED4BKXKlmSlJOTozJlyuidd97R7bffLuliNSwgIECStHr1aiUmJurpp592vj8tLU2nTp3S+vXr1b9/f0lSlSpVdN999+X6rA0bNig8PFylSpWSJL3xxhuSLs6VuyQhIUGHDh3SwIEDL4tx586d2r9/vx599FFnPG3atNHcuXOve3/9+vXTunXrNH36dCUkJCg5OVnp6enO19u1aydJqlmzpqpVq6atW7dq8+bN17xPALgRJGoAXOLKOWpXKlasmPNru92uli1bql+/fs7vk5OTVapUKdlsNv35yXbe3rn/mfLy8pLNZnN+n5aWlmuRQU5OjkqUKHFZTMePH1eJEiU0bty4yz7Dy8srz/t75ZVXlJOTo8cee0yNGjXS77//ftk1ihT5Y22W3W6Xt7f3de8TAG4Eqz4BuN3DDz+sr7/+WsnJyZKkzz77TJ06dZIkNWjQQAsWLJAk/fbbb9q4cWOu9z/44INauXKlzp49K0maNGmSZs+eLW9vb+Xk5MjhcOiOO+64LHn8/fff1aJFC23fvl1hYWGKjY1VWlqa7HZ7nosUJGnt2rXq3bu3mjVrJknatm2bcnJynK8vXrxYkrRjxw5ny/d69wkAN4KKGgC3e/jhh/X888+ra9eustlsCggI0OTJk2Wz2TRs2DC98cYbeuyxx1SuXLmrrhht2LCh9u3b52w3Vq9eXSNGjJC/v7/q1Kmj5s2ba/78+ZoyZYrefvttffzxx8rOzlbfvn1Vv359SdKvv/6qNm3aqGTJkgoJCdHJkyevG/PLL7+s3r17q1ixYgoICNC9996rQ4cOOV8/fPiwWrVqJZvNpvHjx6t06dLXvU8AuBE2x59r9wAAALAMWp8AAAAWRaIGAABgUSRqAAAAFkWiBgAAYFEkagAAABZFogYAAGBRJGoAAAAWRaIGAABgUf8PRu8q0Oq0RiQAAAAASUVORK5CYII=",
-                        "text/plain": [
-                            "<Figure size 720x504 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# Confusion matrix testdata\n",
-                "cm = confusion_matrix(y_test,prediction_test)\n",
-                "df_cm = pd.DataFrame(cm, index=['No Churn','Churn'], columns=['No Churn', 'Churn'],)\n",
-                "fig = plt.figure(figsize=[10,7])\n",
-                "heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
-                "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=14)\n",
-                "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=14)\n",
-                "plt.ylabel('True label')\n",
-                "plt.xlabel('Predicted label')"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 199,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "True Negatives: 493\n",
-                        "False Positives: 2\n",
-                        "False Negatives: 9\n",
-                        "True Positives: 431\n",
-                        "Recall: 0.9795454545454545\n",
-                        "Precision: 0.9953810623556582\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# metrics from confusion matrix\n",
-                "tn, fp, fn, tp = cm.ravel()\n",
-                "recall = tp/(fn+tp)\n",
-                "precision = tp/(tp+fp)\n",
-                "print(\"True Negatives: \" + str(tn))\n",
-                "print(\"False Positives: \" + str(fp))\n",
-                "print(\"False Negatives: \" + str(fn))\n",
-                "print(\"True Positives: \" + str(tp))\n",
-                "print(\"Recall: \" + str(recall))\n",
-                "print(\"Precision: \" + str(precision))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Pr\u00e4zision und Recall vermitteln ein viel realistischeres Bild des Modells. Es erreicht eine Pr\u00e4zision von rund 68 % und eine Wiederauffindbarkeit von 52 %. Der Recall ist f\u00fcr den Anwendungsfall eindeutig wichtiger und muss daher auf Kosten der Pr\u00e4zision verbessert werden."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 200,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 576x432 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# ROC-Kurve, AUC\n",
-                "fig, ax = plt.subplots(figsize=(8,6))\n",
-                "ax.set_title('ROC Kurve')\n",
-                "plot = metrics.plot_roc_curve(logistic_model, X_test, y_test, ax=ax);\n",
-                "ax.plot([0,1], [0,1], '--');"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Der AUC der ROC-Kurve ergibt einen guten Wert von 0,84. Daraus l\u00e4sst sich schlie\u00dfen, dass durch die Optimierung des Schwellenwertes Optimierungspotenzial besteht."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 4.3. Interpretation"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Zun\u00e4chst sollen jedoch die Ergebnisse f\u00fcr das Unternehmen veranschaulicht werden und es soll gekl\u00e4rt werden, welche Kunden zur Abwanderung f\u00fchren und welche gegen eine Abwanderung sprechen."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 201,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "InternetService_Fiber optic       3.115901\n",
-                            "StreamingMovies_Yes               1.439381\n",
-                            "PaymentMethod_Electronic check    1.310265\n",
-                            "StreamingTV_Yes                   1.219198\n",
-                            "PaperlessBilling                  1.216093\n",
-                            "MultipleLines_Yes                 1.098867\n",
-                            "SeniorCitizen                     0.395488\n",
-                            "Dependents                       -0.732812\n",
-                            "TechSupport_Yes                  -0.835712\n",
-                            "PhoneService                     -1.207319\n",
-                            "OnlineSecurity_Yes               -1.744166\n",
-                            "InternetService_No               -2.846463\n",
-                            "tenure                           -2.924275\n",
-                            "Contract_One year                -3.457173\n",
-                            "Contract_Two year                -6.329852\n",
-                            "dtype: float64"
-                        ]
-                    },
-                    "execution_count": 201,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# Read out regression coefficients and thus find out importance of individual attributes\n",
-                "weights = pd.Series(logistic_model.coef_[0],\n",
-                " index=X_train.columns.values)\n",
-                "weights.sort_values(ascending = False)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 202,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "AxesSubplot(0.125,0.125;0.775x0.755)\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# Graphical representation of key features that lead to churn.\n",
-                "weights = pd.Series(logistic_model.coef_[0],\n",
-                "                 index=X_train.columns.values)\n",
-                "print (weights.sort_values(ascending = False)[:7].plot(kind='bar'))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Die drei Hauptmerkmale, die zur Abwanderung f\u00fchren, sind:\n",
-                "\n",
-                "- Der Glasfaserdienst (InternetService_Glasfaser),\n",
-                "- Die Online-Zahlungen (PaperlessBilling) und\n",
-                "- Das Abonnement des zus\u00e4tzlichen Filmstreamingdienstes (StreamingMovies_Yes).\n"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 203,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "AxesSubplot(0.125,0.125;0.775x0.755)\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 432x288 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# Most important features that keep customers from churning\n",
-                "print(weights.sort_values(ascending = False)[-8:].plot(kind='bar'))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Die drei wichtigsten Merkmale, die Kunden von der Abwanderung abhalten, sind:\n",
-                "\n",
-                "- Die Vertr\u00e4ge, die f\u00fcr zwei Jahre gek\u00fcndigt werden k\u00f6nnen (Contract_Two year),\n",
-                "- Die Zeit, die man Kunde eines Unternehmens ist (Tenure) und\n",
-                "- kein Abonnement f\u00fcr den Internetdienst (InternetService_No).\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## 4.4. Modell-Optimierung"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Die Recall-Rate ist als Zielmetrik zu niedrig und muss daher erh\u00f6ht werden. Daher werden die Metriken bei verschiedenen Schwellenwerten der logistischen Regression analysiert."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 204,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\n",
-                        "******** For a Threshold about 0.05 ******\n",
-                        "Accuracy: 0.8588235294117647\n",
-                        "True Negatives: 367\n",
-                        "False Positives: 128\n",
-                        "False Negatives: 4\n",
-                        "True Positives: 436\n",
-                        "Recall: 0.990909090909091\n",
-                        "Precision: 0.7730496453900709\n",
-                        "\n",
-                        "******** For a Threshold about 0.1 ******\n",
-                        "Accuracy: 0.9144385026737968\n",
-                        "True Negatives: 420\n",
-                        "False Positives: 75\n",
-                        "False Negatives: 5\n",
-                        "True Positives: 435\n",
-                        "Recall: 0.9886363636363636\n",
-                        "Precision: 0.8529411764705882\n",
-                        "\n",
-                        "******** For a Threshold about 0.15 ******\n",
-                        "Accuracy: 0.9422459893048128\n",
-                        "True Negatives: 446\n",
-                        "False Positives: 49\n",
-                        "False Negatives: 5\n",
-                        "True Positives: 435\n",
-                        "Recall: 0.9886363636363636\n",
-                        "Precision: 0.8987603305785123\n",
-                        "\n",
-                        "******** For a Threshold about 0.2 ******\n",
-                        "Accuracy: 0.9657754010695188\n",
-                        "True Negatives: 468\n",
-                        "False Positives: 27\n",
-                        "False Negatives: 5\n",
-                        "True Positives: 435\n",
-                        "Recall: 0.9886363636363636\n",
-                        "Precision: 0.9415584415584416\n",
-                        "\n",
-                        "******** For a Threshold about 0.25 ******\n",
-                        "Accuracy: 0.9786096256684492\n",
-                        "True Negatives: 481\n",
-                        "False Positives: 14\n",
-                        "False Negatives: 6\n",
-                        "True Positives: 434\n",
-                        "Recall: 0.9863636363636363\n",
-                        "Precision: 0.96875\n",
-                        "\n",
-                        "******** For a Threshold about 0.3 ******\n",
-                        "Accuracy: 0.9818181818181818\n",
-                        "True Negatives: 486\n",
-                        "False Positives: 9\n",
-                        "False Negatives: 8\n",
-                        "True Positives: 432\n",
-                        "Recall: 0.9818181818181818\n",
-                        "Precision: 0.9795918367346939\n",
-                        "\n",
-                        "******** For a Threshold about 0.35 ******\n",
-                        "Accuracy: 0.986096256684492\n",
-                        "True Negatives: 490\n",
-                        "False Positives: 5\n",
-                        "False Negatives: 8\n",
-                        "True Positives: 432\n",
-                        "Recall: 0.9818181818181818\n",
-                        "Precision: 0.988558352402746\n",
-                        "\n",
-                        "******** For a Threshold about 0.4 ******\n",
-                        "Accuracy: 0.9871657754010695\n",
-                        "True Negatives: 491\n",
-                        "False Positives: 4\n",
-                        "False Negatives: 8\n",
-                        "True Positives: 432\n",
-                        "Recall: 0.9818181818181818\n",
-                        "Precision: 0.9908256880733946\n",
-                        "\n",
-                        "******** For a Threshold about 0.45 ******\n",
-                        "Accuracy: 0.9893048128342246\n",
-                        "True Negatives: 493\n",
-                        "False Positives: 2\n",
-                        "False Negatives: 8\n",
-                        "True Positives: 432\n",
-                        "Recall: 0.9818181818181818\n",
-                        "Precision: 0.9953917050691244\n",
-                        "\n",
-                        "******** For a Threshold about 0.5 ******\n",
-                        "Accuracy: 0.9882352941176471\n",
-                        "True Negatives: 493\n",
-                        "False Positives: 2\n",
-                        "False Negatives: 9\n",
-                        "True Positives: 431\n",
-                        "Recall: 0.9795454545454545\n",
-                        "Precision: 0.9953810623556582\n",
-                        "\n",
-                        "******** For a Threshold about 0.55 ******\n",
-                        "Accuracy: 0.9882352941176471\n",
-                        "True Negatives: 493\n",
-                        "False Positives: 2\n",
-                        "False Negatives: 9\n",
-                        "True Positives: 431\n",
-                        "Recall: 0.9795454545454545\n",
-                        "Precision: 0.9953810623556582\n",
-                        "\n",
-                        "******** For a Threshold about 0.6 ******\n",
-                        "Accuracy: 0.9893048128342246\n",
-                        "True Negatives: 494\n",
-                        "False Positives: 1\n",
-                        "False Negatives: 9\n",
-                        "True Positives: 431\n",
-                        "Recall: 0.9795454545454545\n",
-                        "Precision: 0.9976851851851852\n",
-                        "\n",
-                        "******** For a Threshold about 0.65 ******\n",
-                        "Accuracy: 0.9893048128342246\n",
-                        "True Negatives: 494\n",
-                        "False Positives: 1\n",
-                        "False Negatives: 9\n",
-                        "True Positives: 431\n",
-                        "Recall: 0.9795454545454545\n",
-                        "Precision: 0.9976851851851852\n",
-                        "\n",
-                        "******** For a Threshold about 0.7 ******\n",
-                        "Accuracy: 0.9903743315508021\n",
-                        "True Negatives: 495\n",
-                        "False Positives: 0\n",
-                        "False Negatives: 9\n",
-                        "True Positives: 431\n",
-                        "Recall: 0.9795454545454545\n",
-                        "Precision: 1.0\n",
-                        "\n",
-                        "******** For a Threshold about 0.75 ******\n",
-                        "Accuracy: 0.9903743315508021\n",
-                        "True Negatives: 495\n",
-                        "False Positives: 0\n",
-                        "False Negatives: 9\n",
-                        "True Positives: 431\n",
-                        "Recall: 0.9795454545454545\n",
-                        "Precision: 1.0\n",
-                        "\n",
-                        "******** For a Threshold about 0.8 ******\n",
-                        "Accuracy: 0.9893048128342246\n",
-                        "True Negatives: 495\n",
-                        "False Positives: 0\n",
-                        "False Negatives: 10\n",
-                        "True Positives: 430\n",
-                        "Recall: 0.9772727272727273\n",
-                        "Precision: 1.0\n",
-                        "\n",
-                        "******** For a Threshold about 0.85 ******\n",
-                        "Accuracy: 0.9882352941176471\n",
-                        "True Negatives: 495\n",
-                        "False Positives: 0\n",
-                        "False Negatives: 11\n",
-                        "True Positives: 429\n",
-                        "Recall: 0.975\n",
-                        "Precision: 1.0\n",
-                        "\n",
-                        "******** For a Threshold about 0.9 ******\n",
-                        "Accuracy: 0.9871657754010695\n",
-                        "True Negatives: 495\n",
-                        "False Positives: 0\n",
-                        "False Negatives: 12\n",
-                        "True Positives: 428\n",
-                        "Recall: 0.9727272727272728\n",
-                        "Precision: 1.0\n",
-                        "\n",
-                        "******** For a Threshold about 0.95 ******\n",
-                        "Accuracy: 0.9807486631016042\n",
-                        "True Negatives: 495\n",
-                        "False Positives: 0\n",
-                        "False Negatives: 18\n",
-                        "True Positives: 422\n",
-                        "Recall: 0.9590909090909091\n",
-                        "Precision: 1.0\n",
-                        "\n",
-                        "******** For a Threshold about 0.99 ******\n",
-                        "Accuracy: 0.9497326203208556\n",
-                        "True Negatives: 495\n",
-                        "False Positives: 0\n",
-                        "False Negatives: 47\n",
-                        "True Positives: 393\n",
-                        "Recall: 0.8931818181818182\n",
-                        "Precision: 1.0\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# Testing the metrics at different thresholds\n",
-                "threshold_list = [0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,.7,.75,.8,.85,.9,.95,.99]\n",
-                "pred_proba_df = y_hat\n",
-                "for i in threshold_list:\n",
-                "    print ('\\n******** For a Threshold about {} ******'.format(i))\n",
-                "    # Round up if value is above threshold\n",
-                "    y_test_pred = pred_proba_df.apply(lambda x: 1 if x>i else 0)\n",
-                "    # read metrics\n",
-                "    test_accuracy = metrics.accuracy_score(y_test, y_test_pred)\n",
-                "    print(\"Accuracy: {}\".format(test_accuracy))\n",
-                "    # Confusion matrix\n",
-                "    c = confusion_matrix(y_test, y_test_pred)\n",
-                "    tn, fp, fn, tp = c.ravel()\n",
-                "    recall = tp/(fn+tp)\n",
-                "    precision = tp/(tp+fp)\n",
-                "    # print metrics\n",
-                "    print(\"True Negatives: \" + str(tn))\n",
-                "    print(\"False Positives: \" + str(fp))\n",
-                "    print(\"False Negatives: \" + str(fn))\n",
-                "    print(\"True Positives: \" + str(tp))\n",
-                "    print(\"Recall: \" + str(recall))\n",
-                "    print(\"Precision: \" + str(precision))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Ein Schwellenwert von 0,3 bietet ein besseres Ergebnis f\u00fcr die Anwendung. Er erh\u00f6ht die Wiederauffindbarkeit auf ein zufriedenstellendes Niveau von 73,21 %, was zu Lasten der Pr\u00e4zision geht. Die Pr\u00e4zision ist jedoch vernachl\u00e4ssigbar."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Daraus ergeben sich die folgenden Werte:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 205,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "              precision    recall  f1-score   support\n",
-                        "\n",
-                        "           0       0.98      0.98      0.98       495\n",
-                        "           1       0.98      0.98      0.98       440\n",
-                        "\n",
-                        "    accuracy                           0.98       935\n",
-                        "   macro avg       0.98      0.98      0.98       935\n",
-                        "weighted avg       0.98      0.98      0.98       935\n",
-                        "\n",
-                        "Confusion matrix for the new threshold:\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 720x504 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        " \n",
-                        "Metrics for the new threshold:\n",
-                        "Accuracy: 0.9818181818181818\n",
-                        "True Negatives: 486\n",
-                        "False Positives: 9\n",
-                        "False Negatives: 8\n",
-                        "True Positives: 432\n",
-                        "Recall: 0.9818181818181818\n",
-                        "Precision: 0.9795918367346939\n"
-                    ]
-                }
-            ],
-            "source": [
-                "# Threshold about 0,3\n",
-                "y_test_pred = pred_proba_df.apply(lambda x: 1 if x>0.30 else 0)\n",
-                "test_accuracy = metrics.accuracy_score(y_test, y_test_pred)\n",
-                "c = confusion_matrix(y_test, y_test_pred)\n",
-                "# read values from confusion matrix\n",
-                "tn, fp, fn, tp = c.ravel()\n",
-                "recall = tp/(fn+tp)\n",
-                "precision = tp/(tp+fp)\n",
-                "print(classification_report(y_test,y_test_pred))\n",
-                "# create confusion matrix\n",
-                "print(\"Confusion matrix for the new threshold:\")\n",
-                "df_cm = pd.DataFrame(c, index=['No Churn','Churn'], columns=['No Churn', 'Churn'],)\n",
-                "fig = plt.figure(figsize=[10,7])\n",
-                "heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
-                "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=14)\n",
-                "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=14)\n",
-                "plt.ylabel('True label')\n",
-                "plt.xlabel('Predicted label')\n",
-                "plt.show()\n",
-                "print(\" \")\n",
-                "# print metrics\n",
-                "print(\"Metrics for the new threshold:\")\n",
-                "print(\"Accuracy: {}\".format(test_accuracy))\n",
-                "print(\"True Negatives: \" + str(tn))\n",
-                "print(\"False Positives: \" + str(fp))\n",
-                "print(\"False Negatives: \" + str(fn))\n",
-                "print(\"True Positives: \" + str(tp))\n",
-                "print(\"Recall: \" + str(recall))\n",
-                "print(\"Precision: \" + str(precision))"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Erwartungsgem\u00e4\u00df steigt die Rate der f\u00e4lschlicherweise als abgewandert eingestuften Kunden. Im Gegenzug steigt aber auch die Anzahl der Kunden, die korrekt als Abwanderer vorhergesagt werden (True Positives). Wie in der Hausarbeit ausgef\u00fchrt, ist dies essentiell, denn im Zweifelsfall w\u00fcrde ein Kunde f\u00e4lschlicherweise vom Serviceteam angerufen werden und diesen Anruf sogar als guten Service wahrnehmen und l\u00e4ngerfristig an das Unternehmen binden."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# 5. Deployment"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 206,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Separate individual (scaled) customer\n",
-                "customer_df = X_test.iloc[896]"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 207,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "SeniorCitizen                     0.000000\n",
-                            "Dependents                        0.000000\n",
-                            "tenure                           -0.302393\n",
-                            "PhoneService                      1.000000\n",
-                            "PaperlessBilling                  0.000000\n",
-                            "MultipleLines_Yes                 0.000000\n",
-                            "InternetService_Fiber optic       0.000000\n",
-                            "InternetService_No                1.000000\n",
-                            "OnlineSecurity_Yes                0.000000\n",
-                            "TechSupport_Yes                   0.000000\n",
-                            "StreamingTV_Yes                   0.000000\n",
-                            "StreamingMovies_Yes               0.000000\n",
-                            "Contract_One year                 0.000000\n",
-                            "Contract_Two year                 1.000000\n",
-                            "PaymentMethod_Electronic check    0.000000\n",
-                            "Name: 1544, dtype: float64"
-                        ]
-                    },
-                    "execution_count": 207,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# Overview about the customer\n",
-                "customer_df"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 208,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# execute prediction\n",
-                "cust_pred = logistic_model.predict([customer_df])"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 209,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# evaluate results\n",
-                "def check_prediction(pred):\n",
-                "    if pred[0] == 1:\n",
-                "        print(\"The customer will probably churn! Inform Customer Relationship Management!\")\n",
-                "    else:\n",
-                "        print(\"The customer probably will not churn.\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 210,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "The customer probably will not churn.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "check_prediction(cust_pred)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Zusammenfassung"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Das Notebook hat gezeigt, wie eine logistische Regression verwendet werden kann, um die Abwanderung von Kunden im Telekommunikationssegment vorherzusagen."
-            ]
-        }
-    ],
-    "metadata": {
-        "interpreter": {
-            "hash": "07673f924689cda9590af2124148040e2ce6fe7ee39bbd541ae69d9c32a71079"
-        },
-        "kernelspec": {
-            "display_name": "Python 3",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.8.5"
-        },
-        "title": "Customer Churn Prediction",
-        "category": "CRM"
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "BusinessUnderstanding"
+   },
+   "source": [
+    "# 1. Business Understanding\n",
+    "\n",
+    "Kundenabwanderung ist die Entscheidung eines Kunden, eine bestimmte Unternehmensdienstleistung nicht mehr zu kaufen. Sie stellt somit das GegenstÃ¼ck zur langfristigen Kundenbindung dar. Um die Kundenbindung zu fÃ¶rdern, mÃ¼ssen Unternehmen Analysen einsetzen, die frÃ¼hzeitig erkennen, ob ein Kunde das Unternehmen verlassen will. So kÃ¶nnen Marketing- und VertriebsmaÃŸnahmen eingeleitet werden, bevor es zum eigentlichen Kundenverlust kommt. In diesem Zusammenhang beantwortet der Service konkret diese beiden Fragen: Wie hoch ist die Wahrscheinlichkeit, dass anhand historischer Daten vorhergesagt werden kann, ob ein Kunde zu einem anderen Anbieter abwandert? Welche Faktoren fÃ¼hren zur Kundenabwanderung?"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "DataUnderstanding"
+   },
+   "source": [
+    "# 2. Daten und DatenverstÃ¤ndnis\n",
+    "\n",
+    "Zur Visualisierung und Implementierung des Dienstes wird der Datensatz eines fiktiven Telekommunikationsunternehmens verwendet. Dieser besteht aus 7.043 Zeilen. Jede Zeile beschreibt einen Kunden mit 21 Spalten. Jede Spalte definiert verschiedene Merkmale (Attribute) der Kunden. Anhand der Daten soll klassifiziert werden, ob ein Kunde das Unternehmen verlÃ¤sst oder nicht. Zu diesem Zweck enthalten die historischen Daten die Zielvariable \"Churn\", die Auskunft darÃ¼ber gibt, ob ein Kunde abgewandert ist oder nicht.ot."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.1. Import von relevanten Modulen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import statsmodels.api as sm\n",
+    "import warnings\n",
+    "import imblearn\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from imblearn.under_sampling import InstanceHardnessThreshold\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "sns.set()\n",
+    "# remove warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2. Daten einlesen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>customerID</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>SeniorCitizen</th>\n",
+       "      <th>Partner</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>PhoneService</th>\n",
+       "      <th>MultipleLines</th>\n",
+       "      <th>InternetService</th>\n",
+       "      <th>OnlineSecurity</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DeviceProtection</th>\n",
+       "      <th>TechSupport</th>\n",
+       "      <th>StreamingTV</th>\n",
+       "      <th>StreamingMovies</th>\n",
+       "      <th>Contract</th>\n",
+       "      <th>PaperlessBilling</th>\n",
+       "      <th>PaymentMethod</th>\n",
+       "      <th>MonthlyCharges</th>\n",
+       "      <th>TotalCharges</th>\n",
+       "      <th>Churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7590-VHVEG</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No phone service</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Electronic check</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5575-GNVDE</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.5</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3668-QPYBK</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>108.15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7795-CFOCW</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>45</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No phone service</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Bank transfer (automatic)</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9237-HQITU</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Fiber optic</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Electronic check</td>\n",
+       "      <td>70.70</td>\n",
+       "      <td>151.65</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
+       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
+       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
+       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
+       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
+       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
+       "\n",
+       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
+       "0  No phone service             DSL             No  ...               No   \n",
+       "1                No             DSL            Yes  ...              Yes   \n",
+       "2                No             DSL            Yes  ...               No   \n",
+       "3  No phone service             DSL            Yes  ...              Yes   \n",
+       "4                No     Fiber optic             No  ...               No   \n",
+       "\n",
+       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
+       "0          No          No              No  Month-to-month              Yes   \n",
+       "1          No          No              No        One year               No   \n",
+       "2          No          No              No  Month-to-month              Yes   \n",
+       "3         Yes          No              No        One year               No   \n",
+       "4          No          No              No  Month-to-month              Yes   \n",
+       "\n",
+       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
+       "0           Electronic check          29.85         29.85    No  \n",
+       "1               Mailed check          56.95        1889.5    No  \n",
+       "2               Mailed check          53.85        108.15   Yes  \n",
+       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
+       "4           Electronic check          70.70        151.65   Yes  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_raw = pd.read_csv(\"https://storage.googleapis.com/ml-service-repository-datastorage/Customer_Churn_Prediction_data.csv\")\n",
+    "data_raw.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7043 entries, 0 to 7042\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   customerID        7043 non-null   object \n",
+      " 1   gender            7043 non-null   object \n",
+      " 2   SeniorCitizen     7043 non-null   int64  \n",
+      " 3   Partner           7043 non-null   object \n",
+      " 4   Dependents        7043 non-null   object \n",
+      " 5   tenure            7043 non-null   int64  \n",
+      " 6   PhoneService      7043 non-null   object \n",
+      " 7   MultipleLines     7043 non-null   object \n",
+      " 8   InternetService   7043 non-null   object \n",
+      " 9   OnlineSecurity    7043 non-null   object \n",
+      " 10  OnlineBackup      7043 non-null   object \n",
+      " 11  DeviceProtection  7043 non-null   object \n",
+      " 12  TechSupport       7043 non-null   object \n",
+      " 13  StreamingTV       7043 non-null   object \n",
+      " 14  StreamingMovies   7043 non-null   object \n",
+      " 15  Contract          7043 non-null   object \n",
+      " 16  PaperlessBilling  7043 non-null   object \n",
+      " 17  PaymentMethod     7043 non-null   object \n",
+      " 18  MonthlyCharges    7043 non-null   float64\n",
+      " 19  TotalCharges      7043 non-null   object \n",
+      " 20  Churn             7043 non-null   object \n",
+      "dtypes: float64(1), int64(2), object(18)\n",
+      "memory usage: 1.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_raw.info()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "DataUnderstanding"
+   },
+   "source": [
+    "Der Datensatz besteht aus 7.043 Zeilen und 21 Attributen:\n",
+    "\n",
+    "- Zu prognostizierendes Attribut: Abwanderung\n",
+    "- Numerische Attribute: Vertragsdauer, MonatsgebÃ¼hren und GesamtgebÃ¼hren.\n",
+    "- Kategorische Attribute: CustomerID, Gender, SeniorCitizen, Partner, AngehÃ¶rige, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod.\n",
+    "Es wurden nicht alle Datentypen korrekt eingelesen:\n",
+    "\n",
+    "- TotalCharges muss ein numerischer Wert sein -> in Float umwandeln\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>customerID</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>SeniorCitizen</th>\n",
+       "      <th>Partner</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>PhoneService</th>\n",
+       "      <th>MultipleLines</th>\n",
+       "      <th>InternetService</th>\n",
+       "      <th>OnlineSecurity</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DeviceProtection</th>\n",
+       "      <th>TechSupport</th>\n",
+       "      <th>StreamingTV</th>\n",
+       "      <th>StreamingMovies</th>\n",
+       "      <th>Contract</th>\n",
+       "      <th>PaperlessBilling</th>\n",
+       "      <th>PaymentMethod</th>\n",
+       "      <th>MonthlyCharges</th>\n",
+       "      <th>TotalCharges</th>\n",
+       "      <th>Churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows Ã— 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [customerID, gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges, Churn]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# test for duplicates\n",
+    "data_raw[data_raw.duplicated(keep=False)]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keine Duplikate im Datensatz"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.3. Datenbereinigung"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hier sollten die ersten Lesefehler korrigiert werden, bevor die eigentliche Datenaufbereitung erfolgt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 7043 entries, 0 to 7042\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   customerID        7043 non-null   object \n",
+      " 1   gender            7043 non-null   object \n",
+      " 2   SeniorCitizen     7043 non-null   int64  \n",
+      " 3   Partner           7043 non-null   object \n",
+      " 4   Dependents        7043 non-null   object \n",
+      " 5   tenure            7043 non-null   int64  \n",
+      " 6   PhoneService      7043 non-null   object \n",
+      " 7   MultipleLines     7043 non-null   object \n",
+      " 8   InternetService   7043 non-null   object \n",
+      " 9   OnlineSecurity    7043 non-null   object \n",
+      " 10  OnlineBackup      7043 non-null   object \n",
+      " 11  DeviceProtection  7043 non-null   object \n",
+      " 12  TechSupport       7043 non-null   object \n",
+      " 13  StreamingTV       7043 non-null   object \n",
+      " 14  StreamingMovies   7043 non-null   object \n",
+      " 15  Contract          7043 non-null   object \n",
+      " 16  PaperlessBilling  7043 non-null   object \n",
+      " 17  PaymentMethod     7043 non-null   object \n",
+      " 18  MonthlyCharges    7043 non-null   float64\n",
+      " 19  TotalCharges      7032 non-null   float64\n",
+      " 20  Churn             7043 non-null   object \n",
+      "dtypes: float64(2), int64(2), object(17)\n",
+      "memory usage: 1.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# convert total charges\n",
+    "data_raw['TotalCharges'] = pd.to_numeric(data_raw['TotalCharges'], errors='coerce')\n",
+    "data_raw.info()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die Konvertierung der TotalCharges hat zu Nullwerten gefÃ¼hrt. Diese Nullwerte mÃ¼ssen korrigiert werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove zero values\n",
+    "# axis = 0 rows / axis = 1 columns\n",
+    "data_no_mv = data_raw.dropna(axis=0)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.4. Deskriptive Analytik"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In diesem Teil des Notebooks soll das DatenverstÃ¤ndnis mit Hilfe der deskriptiven Analytik berÃ¼cksichtigt werden. Nach dem Entfernen der Nullwerte besteht der Datensatz aus 7032 Zeilen, von denen jeweils eine einen Kunden beschreibt, und 21 Spalten, die die Attribute des Kunden definieren. Mit Hilfe dieser Daten soll versucht werden, zu klassifizieren, ob ein Kunde abwandert oder nicht. Zu diesem Zweck enthalten die historischen Daten die Zielvariable \"Churn\", die Auskunft darÃ¼ber gibt, ob ein Kunde abgewandert ist.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.4.1. Kontinuierliche Merkmale"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ZunÃ¤chst werden die Verteilungen der kontinuierlichen Merkmale einzeln untersucht und in einem zweiten Schritt die kategorialen Merkmale in Zusammenhang mit der Zielvariablen gesetzt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load continous features\n",
+    "numeric_data = data_no_mv.select_dtypes(include=[np.number])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Besitz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x23e942418e0>"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
     },
-    "nbformat": 4,
-    "nbformat_minor": 4
-}
\ No newline at end of file
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(numeric_data[\"tenure\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Keine Normalverteilung erkennbar.\n",
+    "- Keine AusreiÃŸer erkennbar.\n",
+    "- Kunden sind potentiell gleichmÃ¤ÃŸig Ã¼ber die einzelnen Monate verteilt, aber eine groÃŸe Anzahl von Kunden ist noch nicht lange im Unternehmen.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='tenure', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"tenure\"],\n",
+    "             bins=10,\n",
+    "             color='orange',\n",
+    "             label='Non-Churn',\n",
+    "             kde=True)\n",
+    "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"tenure\"],\n",
+    "             bins=10,\n",
+    "             color='blue',\n",
+    "             label='Churn',\n",
+    "             kde=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kunden, die noch nicht lange bei dem Unternehmen sind, werden eher abwandern als langjÃ¤hrige Kunden."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monatliche Kosten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='MonthlyCharges', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(numeric_data[\"MonthlyCharges\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Es ist keine Normalverteilung erkennbar.\n",
+    "- Die meisten Kunden befinden sich im vorderen Teil der Verteilung und zahlen relativ niedrige monatliche GebÃ¼hren.\n",
+    "- Dennoch verlÃ¤uft die Kurve gleichmÃ¤ÃŸig mit einem erneuten Anstieg nach hinten und dementsprechend kÃ¶nnen keine AusreiÃŸer identifiziert werden.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='MonthlyCharges', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"MonthlyCharges\"],\n",
+    "             bins=10,\n",
+    "             color='orange',\n",
+    "             label='Non-Churn',\n",
+    "             kde=True)\n",
+    "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"MonthlyCharges\"],\n",
+    "             bins=10,\n",
+    "             color='blue',\n",
+    "             label='Churn',\n",
+    "             kde=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Kunden mit niedrigen monatlichen GebÃ¼hren sind eher abwanderungsbereit.\n",
+    "- Der Abwanderungstrend zwischen Kunden, die abwandern, und Kunden, die nicht abwandern, gleicht sich an, wenn die monatlichen GebÃ¼hren steigen.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gesamtkosten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(numeric_data[\"TotalCharges\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Die Kurve flacht nach hinten hin extrem stark ab.\n",
+    "- Es sind Ã„hnlichkeiten mit der Exponentialverteilung zu erkennen. -> Test der logarithmischen Transformation zur Erreichung einer Normalverteilung.\n",
+    "- Es ist fraglich, ob es AusreiÃŸer im hinteren Teil gibt. -> Boxplot\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Boxplot fÃ¼r TotalCharges erstellen, um sicherzustellen, dass keine AusreiÃŸer vorhanden sind.\n",
+    "plt.boxplot(numeric_data[\"TotalCharges\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Boxplot zeigt keine AusreiÃŸer.\n",
+    "- Dies bedeutet, dass auch bei den Gesamtkosten keine AusreiÃŸer festgestellt werden kÃ¶nnen.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# logarithmic transformation\n",
+    "log_charges = np.log(data_no_mv[\"TotalCharges\"])\n",
+    "sns.distplot(log_charges)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Auch die Transformation mit Hilfe des Logarithmus fÃ¼hrt nicht zu einer Normalverteilung.\n",
+    "- Vor weiteren Transformationen sollte zunÃ¤chst die Korrelation mit anderen Variablen untersucht werden.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"TotalCharges\"],\n",
+    "             bins=10,\n",
+    "             color='orange',\n",
+    "             label='Non-Churn',\n",
+    "             kde=True)\n",
+    "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"TotalCharges\"],\n",
+    "             bins=10,\n",
+    "             color='blue',\n",
+    "             label='Churn',\n",
+    "             kde=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Die Verteilung ist Ã¼ber die gesamte Bandbreite der Kosten sowohl bei den abwandernden als auch bei den nicht abwandernden Kunden nahezu identisch.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Korrelationsanalyse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# correlation between continous features\n",
+    "feature_corr = numeric_data.drop(\"SeniorCitizen\", axis=1).corr()\n",
+    "sns.heatmap(feature_corr, annot=True, cmap='coolwarm')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die Korrelationsmatrix zeigt, dass die Attribute \"Tenure\" und \"TotalCharges\" eine kritische positive Korrelation von Ã¼ber 0,8 aufweisen. Diese Beziehung wird spÃ¤ter im Zusammenhang mit der MultikollinearitÃ¤t erneut untersucht und muss entfernt werden."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Streudiagramme mit kontinuierlichen Merkmalen und Ziel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='tenure', ylabel='MonthlyCharges'>"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(data=data_no_mv, x=\"tenure\", y=\"MonthlyCharges\", hue=\"Churn\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Das Streudiagramm deutet darauf hin, dass Kunden im oberen linken Bereich, d. h. Kunden mit hohen monatlichen Kosten und kurzer BetriebszugehÃ¶rigkeit, am ehesten abwandern."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='tenure', ylabel='TotalCharges'>"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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rphQyc3QOAwoTSU809ZLlv0E5aVwCY/unARHfRofDT6JVx+LJhdHxvuaCmepb3/o648ZNxOl0ctttNxEOhxg1agzLll17WdZVUFC4vKhUAgIyLsmMfdQdmJoPous8gyd9KB1pA6hoF3D7QvRLjufYyUYCwTCDi5ORJJlthxv58oJ+vLzuFG5fiIEF8SybkEb4rWORuR0N6DQqynITWLO9BotJw9XTShAE+rTKrdJP4zxKP43Lw+clRh8UWS8XnxdZL6ecAjI6Zy3ug+8jeV1oB83g99sDVLcFSE0w0tDmYvmsMtrsXkRBIDXRSEVNJ+lJZvYcb0YlCozqn0acWUuCWUMgFMZmUGN3+UiUu5Ac7RgyC1l9xMWqbdUxa3//phGUZFgvWdZ/laehnDQUFBQU+ogLSckfReeqx396D7qUHGRJgq46rh+Yyg9WBeno7mTaiGwEAdbtrqXbFcBi1HD7woE8/vKB6BzHqjr4+rLB/OjPewBQq0R+cF0/DjVJ1DsSmZcfz44jp3utXdvspCzLqjjCFRQUFC4XgijgC0poVQKXkuLgC4WpbXZR1eiIZHunWzFoIn5QQRDA04W36iDBzqbImM5I3Lx7+NL8XDocAYqybGzYG1EYAP3y4tl04FyvdQ5WtpGTaqGuxUkoLPHMuiruG+9neJwWrZBGok2PPxBiYFEybm+Q41Xt2Cw6pQmTgoKCwuXC5Q/zwb5zbD3UQE6qhWtmlJBuM3zsCQIivbvf3V7LeztromOThmRw9bQS2u1eTAY1qd2tUYUBQFo5bx4OsmbvsejQjXPK0WtU9C9IIjfNxNtbY81MAFqNSPC8aVoUBa4YnsvrNXYOne2if3Y1N80p5/DpdvaeaMZi0vKVqwZSmBnXp34NRWkoKCgonEcW4PWNp/nwfD5Fl9NPRV0Xj9w5DptR87HPdLkCrN1VEzNWlB3PQ3/dRZvdR0aSiZ8O64x9JnMca1bF5lS8sfE0P711CHjtGASRWSPS2XO8mQsuWbVKYOKAJDbtj+R2TB6axdaD9dQ0R/wwR2tlMtI7eGNjj4nqZHUnP/rSaBJNHy/7f4KiNBQUFBTO4/SG2HakMWbMHwjT1OHGZrR97DNefxhZjvgYEuMivS8On26jX34i6UkmdFo1/uQEYE30GXdI9THzhKhr9/PX1bWMKElkecZpHlxYwIFGUIkCQ9PCpHbtJyfNRl2zk+JsG5v295iwhpensHF/rEkrLMnUtjjITTb+5z/KR1CUhoKCgsJ5VKKAXqvG648t9KTT9N7kL2Ayqll6RRFqtYqmNhcFmXGkxBt4dcNpthxsQBQFnJMKmDbn24Q3/B45FCAlwYRGbY+amgCyUy0crbbj9gbpcPoJN1ZiO/cWM63JyHKY8KlOhEFX8KUrxwOgVgsxZid/MIxB23tLV4t9m46nJPd9yjz++C+4//7vxozt2bOLZcsWKhVvFRQ+YSx6NTfOKYsZK8mxkZZopMPlp9MdiLZXvYAkybR2enllXSVbDjaw7XAj2w43UtPkiF5fsfksZ+Vs2qf8gM5pP6JdTORb1w9jUFEio/qnMaIshUWTC/nwUMT0dPqcA3f2OABCjjbCzk7ExBzOpM/ht68d4id/3cWByjZmj8mLyrH3RAtzx+fFyGY1aUmM0/fpb6ScND5l7rzzbm6++Vq2bdvChAmT8Xq9PPbYI/zgBz/CaOzbTE4FBYV/jiTJDCtO4vs3j6S6oZskm4H8DCsVdXZqm5yIIuSlWynNtqE/XyXC5w+z9VBDdI7CLBs7PmLiAjhzzs77u2vxB8KMGZDGuIHpJNmMVDV0M6QkGZ1GZPqoXKwmLT5/iC0tLhZMvgn/vpUgqnCM+yq//PvR6MmiurGbgYVJfPfGEXj9IQw6NS5vgO/fNIJ9Fa2Y9GrMRm3MaaYvUJTGJRI4vYPA3jeRXR0I5kS0I5egLR73X89rNBr57nfv45FHfsLw4aP4y1/+yIQJkzAYDNx11234/T7i4mzce+8PycjI5JVXXmDt2tUIgkB5eX+++937+uDbKSj8b3OhdLgoCuw/2c6fV0Y63/mDYQoyrJTk2KhpchKWZN7fXct3rh9OUboFQRD4qPWnvtVJfkYch0+3xYxbTFoCwUjduMlDs/jLO8eiXffqmh1845qhnK23c/qcHatJy9IriqlPyKMiJ5WwBDa7GBMBNbAoCa1axdNvHcXu8mM2aFg2rRhRgIOVrfgCYTISTdx+1YA+DblVzFOXQOD0DvwfPovs6gBAdnXg//BZAqd39Mn8I0eOZvTosfz85w+xd+8uvvSlr/Dooz/jwQcf5m9/e5Frr72BX/ziYcLhMC+88CzPPvsCf/3rC4RCIdraWvtEBgWF/1WcvhBbjzbzqxf2sf90e9Ss5PWHkKRI99DinATUahGTXsMNs8vRagSO1drZfrwFtUZFaa4tOt+xsx2MH5SOzdxTKHB0/zQa29zRzdvtC8a0aR3ZL43V26s5fc4OgMMd4JlVx/H4w7y6vYU3drag18b6VdLijby6vjLamMnlDfLi2goS4gzMGJXDwkkF5GZYOVvf3ae/l3LSuAQCe9+EUCB2MBQgsPfNPjltQKRI4ZIlV/Lznz9Ga2szjY31fP/734ped7vdqFQqBgwYxK233siECZO59trlJCd/Op0MFRS+CPhCEo+/cpDa82GrWw82sGhyAV+5aiBdTj8atUhaopFfv9iTma1Ri5xrcfL+rloA4sxavnvjCHYdbebw6TYGFCaSGGdg6ogsdBoVoiiQkWTmiYuyu7UfcaznplvZ/ZGmbrIcMX3dfmUpogB5qSbKcuKpqOsCoNsdwOkJxjwTCEm0dHh4dcOpqIJKnVWm5Gl80lw4YVzq+H+CyWTGbLaQnp6By+UiIyOTZ599CYiUQu/qisR5P/LIr6moOMb27dv59re/wQMP/JShQ4f3mRwKCv9LNHV6ogoDwGLUEGfW8+e3j0bzI8YMSGPswHR2Ho0k5xVn23hhbUX0mW5XgOdWn2DJFUXkZ1qxmTX87d3j1Le6iLfo8AXCmA0avnfjMLYdqMXll0mO01GYGcfZhsgpwOEOkGDVR3t9X0CnVXH6bCchGbRaFYunFuHyBnH7gmSnmtFpVfgDPa0SRFEg3qxj2bQS1CoBfyBMbppFyQj/pBHMiR+rIARz4mVZLzc3D4fDweHDBxk8eCirV7/DunXv8dOf/oKvf/12nnnmBcrLB9La2sLZs6cVpaGg8B/y0WKmk4ZmsXLLWTQaFXnpVjq6vew61sxXlwwiO9WCLMvodb23zTPn7JgIYHScxqBOZlhRPNNG5tDU7saoV2PSa/Dau7iqJEBYUNPc6WRIaTLLJmWjkf2E1EYKM+P444ojhM/LNGZAOqFQmJONPiQZxg0zs2l/A3uON2PQqynKiuO2+f3541tHz5vR4JZ5/Xh5QyW1TRFFmJZoZPSAvq3GrSiNS0A7cgn+D5+NNVGptWhHLrk862m1/PSnj/Lkk48RCAQwGk3cf/9DxMfHs2DBVdx66w3odHpycnKZN2/hZZFBQeF/gSSbgWSbgTa7FwCTQcPEMjOTU+yYmjcRKMghkDWC9afsbNh7DlGAOWPzmDo8i2BYIt6s5+CpVhItWmxHXyVcvRdBo6N8yo/5xasnoutMHpqJNtNKtT0ZtUqkNNfAiIR2LIf+Tri9BlXuUMzFc/h/1wyloc2FRi2SkWRCkmVy0iyoRBEQSDaE+emiRLSyH01CAodbw3xn+XCcngAWg4aWTjdTh2WRFG8kFJJYta2K6kYHySVJinnqk+SC3+JyRE9dzBtvvBv9/wEDBvHnP/+91z3XXLOc5ctv/NyUcVdQ+CzT3uVl9tg8GtvdnGtxkptiJE/aRWjXKiKviEfQn91BTtFtSJKMBOw50cJNc8t5df0pDna3MnZgBjMHWmB7O/Sfhcpi46VNddE1RAEGFSXT3Olm/Z46/IEws0ZlM91URbjlDADhqr3Ee7tImX43FnsDRlUYt6Y/P3mmxw9iErws0O3B98H2yDNqLWMWfo+vPleBLxBGFODBL49h78kW3tx0BotRy+IpRRgNKsU89WmgLR7X50pCQUHh00WtFnlj42munJBPeX4CVtlJ6OjamHskVyf5hh6/x8zRufz6pQNR09bGfXUMKRrMEdMSDhx10D83jtnjknnm3ROEwhJJcQbCksTrH/TUhFq9s5bEyfmMMtkIu+0ABJvOoPd34zWmozGqOVZtj5FjbEYA3/bt0c9yKIB3y7MsHHsjr26pZ3BxMgdPtUX7aTg9QX7/5mF+ePPIvvzJlJBbBQWF/13SEw3cOr8/mw/U83+vHYpEI/2Lt/JAMBzjC5k8LJvXN1Wx7kAr7XYfWw63sHLLWWaOiTRBU2tEahodvebZeNINWQOjnwW1lv1VLn755hnuf74CpyfIsNKe6Ei91LtCRKi9niF5kYZJYwels/VgQ8x1WYaqRkc0D6UvUJSGgoLC/yweX5in3zpKW1fEp/Ha7g5UA2fF3COaE2gM26KfP5ovkZpgiInAAmjt8lKUGUdOqoW0BBNpSb2rO2Ql6hFcPQmAwvClvLW/R7ms31NHeX5C9LNTjO81hypvCNWdEQXW2uklwarrdY/NrPTT6BNkOZK080XkC97BV0Ghz2i1eynPS2BQcRLBkIRGJbLTZ2XSFVmEz+4kaMtBWzKOziqJe28YDnIkLyM33Urt+SRAQRA+Ng9Cq1GxZGoRwZCExx8kLdFIc4cHiCieiUOzsDunYcgZjTYhnb/tcNHYEXsisRi1DC9LQaUS0aRmop54C+HdryAHfKjSilCNWMaqFZHKtqfrOpk3voDfvX4oehJKjjeQmth3FW7hf1RpqNVa3G4HJpP1C6c4ZFnG7XagVms/bVEUFD4RRFFAEATC4X8/OMRi1JKaaOTF83kXohBphnRKl87qLj0JIT1Wr5eibBtbDtSjVotMHprFnLG5qFQiHm+Qoqw4Jg/NYvOB+ui8w8tS0GlEDtd24vYGsRg03LZgADVNDsKSjF6rQhZEHtscoMspM3+Cjm5fb/NTaoKB+RMKAJmWbh9i+miMV5YiBXxI5kRe2HqOW2dkk6D2I1qT+cM7p1g+qwxfIIRaJRIIhjlV20leslFp9/rfEB+fTFdXGy6XPTomiiJSX/2ql5l/JatarSU+PvkTlEhB4dOh0x1g94kWmtpdjB+UQUG6FY3Y+0VQpRZBhnBYosMd5Fyrk1BIIiXByIY9PZFOkgxvbjrDg7cM5YaRBlR6Ex2ShccuygjfdbSJb143jCdePoAgCNy7fDh6nYqvLR1MQ5uL9CQTWo2Ko2c72LS/HqtJy6wxucjA+t21+AJhZo/N5Wi7m7ED0zEbtbR1ebhpVhFPv3OShnYPeq2KL80tQaMWeeBPO5GBa6cXUyQ2oDr0JvhcaIpHc9vIwfg2/wapuxVNyVjiLeN5/r2TiKIQPW3cc92wPlMY8D+qNFQqNUlJ6TFjyckW2tqc/+CJzxafJ1kVFC4X3d4gD/11d7SUxrbDTdyxaABjy1OjJtpAWOJUfTcb9p4jyRapyfS71w7R2B55q792RkmveV3eIN6wyJZqkeFlZjbuq4+5LslwsKKV0px4PP4QFpOGzCQzf1l5FJ1WjS8Q4sY55ew92YLbG8TtDfLc6hN8+/rhDC1NRaUSMBs0rNxaFc3mHlueQPKxl7h31lQ6ycOoDmM99jrqlMXMHpuHKAhMyZPwrfgNYTmiAUwiuDc8heSOZJUHT+3kqivGUXlOh90ZqUc1rDQFlSpSVFE5aSgoKPxP8I/qJtW1uHrVXnpl/SlG5OkxOmoRzQmc6tDz+MuHAchKNpGZbI4qDACVKKIShWgWNkDG+ZNCos2APyij+ki4kE6jYmhZMnENOtrtXjq6fRyrascflPAHI9kdz60+wdJpJbyyrhKIyN/a5SHZpicYkpBluGvxQFZsOkNdi4sZA6x0SJN5bGU73a5GRFFg+cTpjFVpyUnTIQoCSA6Qe3Z+lSmOsDu2GKFx6+/47lU/5kiThEoUOVNvp7PbB8T927/7P0JRGgoKCp9JQpLMuXY3Z+u7SYk3UphpxXRR5JIsy4wrszG1UIVa8tMcNLNifzee41vx7n8DgIzR11GUEY+gUlOYZaWlM9ZvsHn/Oe65ZiBPv1OBwx0gK9nEV6/qh8VZwwxrE2HRinV0IXtP9lSTXjS5kL++c5xuV0RBbDvcyNIriqmss0ff8ENhuVeJklBY4pX1p5AkGbVK5FvXDeX+m0fiD0kYxCA//vvh6JySJPP8lmb0tiT+vPIoAAML47ll+LWo978SmS8uC0GlQQ73KE45FKSrvZNX1kfkNejUzBmXp5inFBQUvtgIAuw62crf3j0eHRtYlMhNc8ppbHMjigKlaRoK6g4h79wKQJLexLBF3yS4aQUXSvjJrWeYN34Z2462EghKDC1JYd3uHh/GvCFxFOx/kofGjsartWF2VmE8dQpHyhDqPFYsQYkM1x6+fd0wth9tRa0SSI43RDf3C6zbXcukoZnRxDqNWuRi10paohF/oCe/IxSW2Lj/HBnJ5XR0e7GatNS39naEd7t71jl6touTRUX0G/8lfGE1QayoRi9H3PEcF5JLNGOuJmBKZexAFRajlvQkE9UN3YojXEFB4YuNyxfmpfcrYsZGlqfxwNO7ov27H1kUh7Fya/S65HMj7XkVXVo+Hkcbot5MRcoMfv/6seg9Hl+IG2aX8cbG0wRCEiPSQ3gPnENsP4cJQKunduw9/PLt5qi/4arRacxOcVKTbCIYkqKNlC4mGJKi+Rt6rYrbFw4gwarnxjnlGPVqDDo1v3vtUMwzrZ1e3t5yhs0HGpg/IZ+sFDP1ra6Ye7TqWNvY0Xo/21wWKmq7KMpqZeGkQcTN/yUeXxCrSceJc05efOMo2akWPL4g7bt9fOu6Yf/Wb/+vUJSGgoLCZ46QJOO/aHPOTbNwqq4rqjAAtMHewSCBlmoMeQMAAalwPG/s7oy5vutYM2PL4nnkln7IogZ8H+lhUTadP27ujCk3/tbuNoaVpbLrWA0qUeCmueUYdOoYWWaOzqUgI46rp5cgSTIvr69k1ug8Xl5XgSTDrVf2i/GbAIzolxqN3Hp/Vy1fv3oIf1l5DIc7gCjAkiuKOVAZ22QtK8XC0bM1AHR0+3D7wvzf6yfwB8OoVSJ3LBpAaU48FbWRnhsjylMx6NSKeUpBQeGLjdWgZsLgDD48FOm1bbPoo5VoL+AS47B95Dl93iBa4wdzZngZiUnxDA44OXyqnZmjc+h0+tl1tJHURBMn6rrwB8OUF2RgKRhJuGovAH5jSjQ7/GI6PdDQFjkFnK63c8u8fhw50057t5fBRclkpJh46s0juLw9/gWrWRvtybHzWBNfuSri+Pb6Q8wem0dHtw/HefNTYpyB2mYHV4zIjjZuspljc61GlKXgC4Tx+CLKauqIbJ5bfTKqXENhiaffPsoDt43G6QkiSTI7jjZR1WCnOMOsmKcUFBS+uAjAsqlFJNsMbD3UgEmvZlT/VCrPv0EDrDoZ5o4J1+HZ8TpIITTJOfgGXcV3/3bm/B2N/PSWQSwvtuM9/hIqSyLTll/Fj545hPv8xiuKtTx483Lis4ehlbzI8bnkZ7RS3Rh7ijFberKq3/jgNHctGcSI8sgmnhxv5N0Pz8YoDJ1GhcWgiX6uqOli3MAMRg9IQwDyM6y8sbGngOG4Qems+rCawqw4+uUn0trlYcWmM3zzuqHMHpuHIIBZr+Ghv+6OPqMWxZjTDkQc8M0dHp5/7yQWo5bZY/PITDYpJw0FBYUvPmadmvnj8pg5MgedRsXqnTXMG5/PB3vrUKtEcrJTOKTOZvxNIwgHvHg18fy/3+6JPl+YaSXHcxz7hmcAEPVmDgqjowoDIlFKK7bVMqK8gA8PNTJ2oJZrZpTxzLvHaen0oNOoWDS5EI+/x1wVlmRO1XVx9EwHLm+AxVOLGNkvjdpmJ3anH6tJy9IrimN6gGvUIqIooFaJaNQi+062sHBSAS2dXtKTTCTG6Vk2rZjK2i5WbD5DWoKRG+eU43T7eerNSPTUjXPLGDconR1HIh0EZVnGqFdHTx4AapVIl9NPYpwBjy/IM6uOc98to/r0z0VRGgoKCp9JZEBytkF3GyGjhcGFcfzfipPMHptHWJLZtL+eO68awMazPjw+geJsEFUihCMb/O0zs3Fteiw6n6DR4vL1dmI73EGOne3gVF0Xp+q6mDA4nZmjc/Gfr2a7+UA9U4ZlRe/Xa1WU5yWQbDMSCkuAQGunh/GDMjAZNPgDIepbXQwoSKA0N544s47JQzP5y8pjdJ0PybUYNdy5eBAnqzvYX9HCrfPKOV7VwcFTkQKGje1unl19nHsucmI/v6aCry0dRGGmjUAwzIDCJJLiDTy76gRefwidRsUNc8rQqlWkJ5mIM2tJTzTR1O6mINXcZ38uitJQUFD4zCEIAuGuehqau2h0qYjTdZNlc3HHvGIeeekoKpXAHQsHcqCyjbW7agGIt+gi7U9XHEGSwagBxJ7oo7Czk0FDBN76yFqj+6excmtV9PP2I01kpVh4bcOp6FhOmoVFkwsRRYFBhYl8eLiRD/ZGCgXazq8bDEnUNjsxG7W4vG40GhXjB2UgCBAMhlk6tYj4OAOCAEadikf/vj9qXrK7g1GFcYFQWO4VTbXvZCv+QIjDZ9rJSDLx8rpK5o3PRxQFSrJtONx+Xt1wmuHlqbi9QVZsOsM3rhn6DxMk/xMua2n0jRs3snjxYubMmcPPfvYzAHbs2MH8+fOZOXMmTzzxRPTekydPsnjxYmbNmsV9991HKBT5MRsbG1m+fDmzZ8/mrrvuwu3uHcusoKDwxUIV9nCg1sOOGgmDKkSLE17e7yPdKvP4Nyby2NcnYDSoowoDoMvpZ8uBepZeUYxOo6LdHcY6Yk7MvEk16/jBjcMoyoojM9nMHYsG0mb3xvgGBEFAfT7UVSUKLJtWzLsfVlHb5OD0uS66nIGowgCwO/2s3l6NWR1g7c4aXnq/gvyMOJ564zB/e/c4f33nOB8ebsRo0PDbVw/yqxf2s+VgI1eMyI7O4fWFsJp6FxkVP1JQtSDTSn5GHMumleANhLA7/byx8TSvbTiFw+2nsc3N0NIUPjxYz+m6Lq6aWkS3y//56Kdx7tw5HnzwQZ566ineeecdTpw4wZYtW/jhD3/IU089xZo1azh27BhbtmwB4N577+WBBx7g/fffR5ZlXnvtNQAeeughrr/+etauXcuAAQN46qmnLpfICgoKnxEc3jApRokFvrcp2PtrRp79I8vKfHS5Jcw6FSatik6Hr9dzFbVdjBmYxo9vH0NeRgKiJYnE2bdjLB6BZfA0pHG38vQ7J0iMM1CQGcerGyopzYmP2VQnD8uiKMvG1dNLuG5mKaU58YwekE4oLKMWRdzeYK91T9V1IWp0jOqfxrQR2ew53hxT4mR/RSsysHhqEVdPK0YQIhV1E6x6ALYeqmfBpIKYOYeUJOPy9iT3FWbGMawkhf6FiWQlmyjMjGPm6Jzo9SSbAV8wzNqdNTg9wYiJa9UJdNrPSbvX9evXM3fuXNLS0gB44oknqK2tJTc3l+zsiIadP38+a9eupaioCJ/Px5AhQwBYvHgxv/3tb1m2bBl79+7l97//fXT8hhtu4N57771cYisoKHyCaMQwyDIh1EiyTKcriMMTIDtOIPnEa4RbI2YjyetE/eHTJC1+MPrsx72Z9y9IpMvuRSWEqeiWKdIbkE6uxVw+DlkKc+hsFx3dPjq6e/Izdh9r5M7FA9lf0UZ+upVguCeBL1LsT+C51Sdi1ui1bn4ijZ1+zpyzs3ByAXtOtMRcnzYym/0VLWw/HHFi56VbmT4yh/K8BLYfaaTbFcBs0HDX4kF0Onxo1CKpCUYa2lxcO7M0GnG1fk8d687ndpgNGr5743DSk0zUNbsw6NRRJ/nFtHR46J8T12eK47IpjdraWjQaDXfeeSdNTU1MmTKF4uJikpN7SnanpKTQ0tJCa2trzHhycjItLS10dXVhNptRq9Ux4woKCp9vREFC23kGx663kP1ezCPnUxHO4pGXjyPJ8JOrc4lrORv7kCyh9XUiE2mjmmTVM3tMLut21yLJkYZDCycV8tbGUxyr6SIj0cRVUwvRZV5JYncXWksyXe5QL1k6HAEIh9FrVWw73MCVEwuoaXIgChG/wrGzHUwcksmHhyKtVI9Xd7BgUgGrPqyKrjt5eBZPvXEYSYaKmk6Gliaz5UDkfrVKINlm4JX1PT6SmiYHtS0OrpyYx5gBaRj0anYdbWbD3p4SJzqNinuuG0rN+a6AkiRHFQZEqvE+t/ok6UlGKmvtzB2fQ7xV1+sEZtB/TpL7wuEw+/bt4/nnn8doNHLXXXeh1+tjmh5d6J4nSdLHjn9cd71/t2lSYuKlRw0kJ1v+rbk/TRRZLw+KrJeHj8rqrTtO0+uPcKFmUufq35Ew/stcMSKHBKueTl+QBJONsNse85zOEofl/FyHqzs5U9/NNTNKkSQZpyfAH1YcZlT/dI7VdNHY4ebZ1SeZNSaXxzc1Al5+dNNg3t4eW+p8zsh0OlwBnJ4AN8wpw+UJ8dqGU4TCEdmmDs9iaElyVGkcrGyjPC+BJ7899bypSuaBP+2MJvKV5yfi9Ydoavdwqq6LZJshGjV1MSeqOpElmQ17z5GVbGbOuDy0apFAKLLDXzuzlD+uOIrdFXn2upmlveaoauxm2shsbBY9KkRmjMyhuvFYtMZVSryBBKu+T/+uXDalkZSUxNixY0lIiPS4nT59OmvXrkWl6qlS2dbWRkpKCmlpabS19UQOtLe3k5KSQkJCAk6nk3A4jEqlit7/79DR4epVbfLj+Dz1qFBkvTwosvYtEuANhElJNOF29rz9iqKAdGo/FxTGBQxnN2GwXs1rH5xiVL9U+k+5Dc+aJ6LlwE1lYxDU2uj3DoVlztTbOVNvj85h0qtRX1Qp0O0Noj5f21yjFvEEBL519QBe3ViFNyCxYFwWKUkW/G1eirPjMeu1/Pnt41GFAbBpfz0j+6VFP6tEgeLseFZuOUNju5ulVxRHN/oLa+493kxSnJ5hM0oAAbW698vu4JJkEq16rp5egtMdYP3uOiYPy2L9njrSk0yca3HiC4QYOzAdSZLRfLRGOzCgIJFOh49N+87R2OpiwaRCbpxTjtcXQq0WCAQlNCrh3/q7IorCP33ZvmxKY+rUqXzve9/D4XBgMpn48MMPmT17Nk8//TS1tbVkZWWxatUqlixZQmZmJjqdjv379zN8+HBWrlzJpEmT0Gg0jBgxgjVr1jB//nzefvttJk2adLlEVlBQ6CM6XAH+/t5JjlV1UJabwC3zykm26IBI6GdYFXEAi3ozgkpN2G1H1hqpb4/00N5zogWRZO6+9gHkznOIOhOBtlqCjk60xmRkBFITDei1KnwX1Ym6YmQObrebW6emUdEcYE9lJ8MKrORdNwizXsWuk+1sOdTEgon56LUqzrW6qWxoYOfR876A6SUf62B3e4PMHJ2LWiUyqn8q+040k5poIjHOQCAYZtaYHN7fFTEdHT7VysJJBai660mQzhJCSyg+m/GD0tl+3ucwrCSJvDQLz64+gccXItlmYNHkQhKsOvLSrVjNOk5UdbBkajHbDjegUomMGZDOsiuKWbH5DGFJJj3RxKShWTz15mFkGQ6caqO6ycHtiwayelsVZqOWgYVJnGt1UZDWd2VEBFnuS796LG+88QbPPvsswWCQ8ePHc//997N7924eeeQR/H4/kydP5gc/+AGCIFBRUcH999+Py+Wif//+PPLII2i1WhoaGvj+979PR0cH6enpPP7448TFXXpDEeWk8emiyHp5+CzL6gtJPPiX3TG1orJSzHxn+XBcniBGvQqxuxGvvYMTnTp8IZmBKTJGg5bvvtLAxf9cf7ksFd0HvwLANvlaRL0V5953QVRjn/hNzraFOFHdSUe3j5HlqUzOl/Hveg254QRiejGGCTdwpENHS6cXjVpFVoqRR57bH51/+awyXryomu64gem0dHk4W9/T3EgQ4Hs3juC5NSeRJJmZo3I4Xt0ZLSaoUYvce8Nw2u0+qhq6GdkvhRRfHeIHT4B03qEen0Fo6jc42hppw5qbbuUnf90dszelxBu446qBbD3YQFqCkYwUM795+WDMb/ujL40mLEn4A2FsFi0P/WXP+QTDHq6bWcrx6g7c3iBn67v59vXD6J9ju+Q/v0/tpAGwdOlSli5dGjM2duxY3nnnnV73lpWV8cYbb/Qaz8zM5Pnnn79sMiooKPQtHQ5/jMIQBfjSgv6s3HqWbYcbsZl13LVkEL96rxav3w7AmwJ85aqBQEP0OYNOjdFsAksSctCHgEDn+09Hr5+rb2XLcW8kW1uANEOIwLrfItkjwTLhhgqa2l386e3T0dNIeV4Cty8cwJ9XRsqlq1SxZqNdx5v54S0jefG9k1Q3OTEZNNx6ZT+cngBN5zv+SRBTfTYYknjhvQqumVGMyaDB6XCSXfk2Qemi0iNdjZjd55DkbMKSTLfL3+tltrXLy6k6O1sPNtAvPyFaqfZithyop6Pby/HqTu5cPBDpY44PGrXIkdPtQKTda5xZ26fJfZekNM6ePcuBAwdYunQp99xzD8eOHeNnP/sZY8aM6RspFBQUvjAYdCpEAXLTzPTLNJCYFM/uY81s2h9xQPuDYfZXtMYk1EkybD/SyIDCJI6ciWx4312UQ4LgwF8yEnV8CmpLEpqkbIzFI0CWyMlIpai7i7+9ewxJhgcWJBG290RXivnDeWWPI8Z8dbKmk+mjcrh94QBCYYn8DCs6jSpaKXZkeQrdLj/5mXGM7J+OPxCmrsVJSXY8S68oRpJlpI/ZfevbXGjUKnRaFakWFZKzvfcP43PQ7QogCgIZSapely3GSAkSAH8gjDW+d0ixxaQlLdFI/8IkqhoczB6bx5odNdHr4wamo9equHZGCaIocLa+mxPVXeQkGT9ZpfHggw9y9dVXs3nzZlpaWnj44Yd5/PHHefXVV/tGCgUFhS8MCSYtv76lGFXlB6gaTxAa+QN+9Pea6HWtRozplXEBjy/IrZNTmFioJssCGb4TtK1/LXo9bvRCDPmD6N7zLoKoJkVjJOBKiZqz3EGRREGMOs7D1gxqT/Quc97l8BEMSfiDYfTtHr5zw3CeXXWChjYX00fl8ue3j9L6kfLo37lhOG9vOYtKFLhzyaBec44qT2XviSYkWeS91gA395tKeG9swZKgLQe9R0U4LBMOSyybVszrH0Qq3apVAkuuKOatzZEKvWcbupk6PJt9J1uiTvmirDiKsuJ4bs1Jul1+RpanMn5wBiPKU/H4QpgMGipqOnn67WMx6w4rS+nT5L5Lygj3+/0sWLCAbdu2MWfOHEaPHk0w2DsrUkFBQUEre7HWbsWWloF54EQsUjdJcfro9Xa7j5KsuF6lLeaNzSXO10hp4Dg5STq6d62MXhN1RhBFHHtXQziEHPTh+vBlZuX7UJ2Pllp91AMD50afUTccZnz/pF7yJdkMvLrhFG9vOUt7t5en3jhMSU4kA1yW5V4KA8DhChAKRxTNmu3V3LFoACZ95J17cFEi00bl4PFJHKxsJRSG2pSJ6EfMR1BrUVuTCU26i0MdZl7/4DSvbjiFTqtGqxFZPquMq6eXcOuV/XG5/TFtZNftqeGe64Yxf0IBCyYWsHx2GU++egi7048sR4IF9p5oYevBeh57cT9/efsoJTnxJFp7fuu8dCvpScZe3+e/4ZJOGoFAgPb2djZv3syf/vQn2tvb8ft7xx0rKCgoqHxdCLJE1+aXzg+s5MYZP+Fnrzij3euKDN38YEEmbx9y4Q1IXDnEQr66jS3nbGTZEjBIGuTQRf0pMorw1R7rtZap9ShZKUOpbXZSUe9ie/5AxkwvAVcHTpWFgeYU2l1h9p1sRa9VseSK4pgOeipRoMvpj5rOZo3JJS/dQk1TbJDBhVauAKfP2cnPsHLTvH50dvsoz0vgb6uOU3c+CW/P8WaqG7u5Y8FMGoPl2D0SH6zvYtHkiP8DIBAM8+Laypg1rhyfz01zy9l+pJHMZDPDSlP43WuHUKtEZFkmPcnU6/vvONrE4ilFADS0u3n8pf187+aRVDc60GlErCYd7V0+suL7TnFcktK45pprmDp1KnPmzKGoqIgpU6bw1a9+tc+EUFBQ+HwhqgScLi+iKGAy6pEuymsQwkGchzeiTS9EzB2KVHeEpJ1Pcv8N91LZ5MNi0mJp24rqwHt8LXcIslqHdPAAmvh0ZqUV4t6xAYbMwtRvHGFnF/rsMiQpTNjRjr/xdIwcqsRs7McjL7A2iw5rQjzPHWgmL6OAuhYHh04d5md3jGPiwFR8QYm3tlQxYUgWapVIKCwhirHHnQ/21nHX4sG89sGpaD+NG+aUsWp7dcx9VrOOFZtO09Lp5ZvXDo0qjAu0dXkJyzKbKz0kxum5fdHAmFIkF9elusCq7dU8dPsY1CqRjCQjq3fUnPfHREx5Rp2m1zOp8UYu/gpuX4iT1R3sPNqM2xfEqFOzfHbZP/mT/Pe5JKVx/fXXc+211yKeLzP81ltvER8f36eCKCgofD7wB/ys2lHL2j0NqESBJZNymTosE7U64riVJJnAgofZXB3kyJF2+uf3Z8JIA6HmWl5Z302cScPo6XEghQlV94S/qgwWwp2R9q6eQ+tIWfpdnIc2YN++Ak1CGvEzb8d79hCSL1IuXLQkQPYQ7lpsxOMNkp5k5myjA1mAtTtrKMq28b3lQ0ht24Xt0HtgiCNv6jwq/Wry0i2o1SJN7W4GFiZy9GwHEEkYPNfiYOKQzEgSoiSTkWSi/aJosCSbnuwUMy2dkTGVKHxsdJIsyyTHG1GJEcd2u70n/8Ns1KBWCTFJhOmJJmqaHKzZUUNmiplR/dI4WNmT9KzVipTnJXCypjO67tzxebg+ooCyUiyMGRAJne12BTDo+jZI9pJmc7vd/PrXv+bs2bM8+eSTPPHEE3zve9/DZOp9XFJQUPjiIoqRcNPVuyLmnLAk8/LGarKSjZTnpwLgMmXx51VHOFVnB+BsfTdHzlj58pWlwF4yUyzYrVno+k2jM66MsCyQ4K3DmJJEx/pIlz1D4VAcu9/Fdy7ydh7sbKLt7cdRz/oW7tZmZEGgyhuHuk3Ns6sP4fWHuf/WUazaVkVzRyRB8HhVB03tbn402o1obwF7C0Z3NwXTfkhlggmPL8TAwiQMOhUzRmTS3NpNdmYCG/Y1sL+iJ6T2prnl3DKvH8GQhCCASiXScFGfC41aZNLQLLYc6ClPMqI8FVEU2XO8GbVKZFBRMjfMLmPd7lp8gTBN7W6+ee0w/rLyGHaXn6wUM7fN78/Pn9tLMCTR0e1lwqAMbp7Xjz3Hm1GJAsGgREayiWFlKYRCEhq1yI4jjWRcVCJk3vh8JFnm4Kk2rCYtQ4qT+Wjm/X/LJSmNn/3sZ6SkpNDR0YFOp8PlcvHAAw/w61//uk+FUVBQ+OwhCOAJhOlyBkiI07L5SFuve/ZVtDO4NINwWKKmzR9VGBeoaXLg8EU2L78/RCc21rUNYd+2yFw5KVl8I16N5vwGp0svwL4tNm9L9rnobmniB2su+FO9jBskkJtmpaK2i7oWZ1RhXKDT4cNpziFuwBxUfgf2jNE8+MyBqF/j4KlWvnH1ECrr7LR2ejjXGSAQDLNkahEqlUicWcu5Zid/X3MyZt4bZpdx6/x+eLwh9Fo1YUli+ewyOrt9xFt1NHd4ov6LUFiiurEbXyDMwKJkdFoVR063odOquGpKIXFmLYlWA25/iEWTCzlR3UlaohGDTkVYUpN9vuueTquirtkZ7eUhCnDHVQOxGLUkxunRqiMmN7NBw9RhWWg0IjqtmtpmF5mftE/j5MmTPPLII2zZsgWDwcBjjz3GlVde2WdCKCgofHZpdfipOVNDvODCZzAzvDSR0+fsMffkZVjwV2wl1NVEfs5AhuRbOFQda+e/EOXkC4bpdgfYd5Hppa7VxaaGDObEZxDuakTyexH15qgp6gJ+UQ/0BOHkploYVJjE4OJkrEYtokBMRvm0kTlUOjWsO5JDnEnNgvxMUhK80UQ9gPd21TB3bC5ub4i8TCt5aVZ+8+pB4i16MpNMdDp6B/2cPtdFS6eXmiYH+RlWhpel8tL7FZgNGtzeIJOHZXNxsY11u+v4xdfGY1P5EGQJu5zOyWNn6G9sQ1PfiDG3P++eFll/2E5hZhzHqzr4YO85fnjLSPrlR0qxh8MSJdnxTBuZTSgkoVKJ7D3RzPyJhQSCITKTrJiMWn7+7N6owspJs3D7wgGffHKfKMZG5obD4V5jCgoKXzxCkoyq4yz9jv0ByesEUUXO+OuoKk1kT2UkYzkt0UA/XROuDX8GwLd/FV+58l4appTjcAewmLQcP9tOaryOR786HrNBw7vbqnqtdbTGydRZX0HlbqPZkEDChBx8G/4Qva4tHMHJbiMQKfFRlGUjLzOO379+GIc7wJRhWSyYVMjbWyIl1ZNsemxmLS9uiOQ+tNnhN68e5oY55TEnBxUCJoOWjGQzcSY9R860M39CAVUNDrJSzeRlxFFZF5udPXV4NrIc8VsEQhJVDd18bclgWu1etGoRtUpEpxEZNzCdUDjM2PJEEkKtSL4AsiwTr9Yw0VqHc1skD8VzYj3TBs7lRFIex6o6ouvYXX6eeuMIAA/fOZacNAtvbDxNh8PHqH6pLJhYiMsbxGTQkpVq4eX1lSyaXIhKFFCJIg1tLhrb3GQlGP6bvwYxXJLSGDlyJL/61a/w+Xx8+OGHvPjii4wePbrPhFBQUPhsIgRd6PY8F1EYAFIY74cv8JWlP2biwBREQSDPBqG3foTalorKaEXSGDjoSOYvK/cgyRHz1i3z+hEneLBabYiiQHZqbKluQYCFkwvZ0+CmqcNC/wILBzqhbPy3sIa7CKiMuI0ZGFwqrp1pRSCSeb7/ZCsOdyS3YfOBeq6ZXsK9Nwynze4lN83Kk6/G1m6S5EjxQY1ajL6Nzxqbx+MvHcQXCGExarh90UD+8OYRvP4Qu47B1dNLGDconR1HmhAFuOf6YTR3eHhz02mcniCDipJYMLEAvVZNa5cXQRDwB8I0tLupb3URZ9FRlGYkULWT8IG3kENBVKWTMAyegWhci+RxRIQ7/j5zh3+L3zZETldqlUC8RUdinB6VKOD0BPnjW0eiJ4ZDp9oYNzCD1z44RX2riw/21HHbwgH85uUDuH2RzPLyvASGlf17lcH/FZekNL7zne/w9NNPY7FYeOKJJ5g4caIScqug8AVCEAUE6FUPSS958dhbEfOG408fiMbVilC5kUB3O69vkwmFJB6arsIx4WvsbNTS2B1m0fhinvnrnqiZSJbh72tOUvSVsaRHCt1iNGgYMyCNXcciHfSum1HKqm1VVDdGNtAPDzUwc3QOz1cGqGkKA06gkh/eMpKqRgcyoNepWbe7NkbeNzae5sY55Ty35gTjB2dgNWqxf6SXRWKcgQmDM/D6Q4wbmMG726rwnS/f4fQEeW71CaYOz4qW53hr8xl+/OUxDC1JRhAE4s06nnj5YPS3OnKmHZ1WxazRebz2wSlsFh3TRmTz8vpIHoa20426CwK7X4nKEK7YTMCahHn4HBwfnq+sIYVJsenOh9yamDUmF6sqwANDGxAkiePOohgT07SRObyyvpLBxcmMG5hBnFnLO1urogoDImVTZo7O+eTbvWo0Gr72ta/xta99re9WVlBQ+NSRgXPtblZtq8bjCzFvQj7FGdZoTwpRbyE47Vu8eCDIwQN2UuOzuXv2d8k1+flh0S4ElQZd/Hj+uKKV2hY7KlFgyshg9C3+AmFJxu70k34+M1yjElGJAt+4ZkhkHUGIKowLfLD3HEuuKKamKTIeZ9biC4R5ZV1kM/64/IPsVAsmowZZhr3HW7htYX/+8GbP23linJ4km55gKFIp2xcI9fLPdHT7MBt76j5dO6OUX790IFoyPSPJxLIrinl1Q08nvv0VrcwaHekoOHVYFlsO9kRS5WfEITf3VNKN/vZnd8LoxdHPuqIRnPIbWTipAH8wjEYlYjAZSCjshyCImD2xeRo2i4554/N5a8sZ2u0+Fkws4FxL78rH7d2+T96nccUVV8R0zBMEAYPBQHFxMd///vf/7cZICgoKnw2aurw89NfdxFv0aNQiv3phP/cuH055dmRTDQpantkX4ni1HYA2uxet30Hn+7+JzuE7vokfXPldHB1hNCEPokWiKDOOkf3TCEsyapXAnuPN2Cw6ECKKKs6spX9hEi+/X0EgJHHD7PJeskmyTEFGHCU58RRkWJk0JJNXN1y0+coyI/ulsvd8P26dVsWccXnUNkSUjEGnxuMLcdPcftidfnRaFQLQ0Orm5XUVqFQiS6YW9Vo33qI735EvkjvR2O6K6bHR2O7G6w9hNWmjprGMJBMub5Crp5cQZ9ai1/ZsreeanaiGp/PRdD4xKYdufSZN4+4lOcFMVVeQzUfa6JefgMcX4q+rjnP3ssFYcwcAYG1zU5YbH61+m55o4s8rj0ZLjxw+3caIfqlsuKglLEBmkumTP2lMnz4dt9vN8uXLEUWRN954A7fbTWlpKQ888AB//OMf+04iBQWFTwRRFDhe3cHN8/rR1O4mEAwzb3w+O483UZ4TBzI4nC6OV/c4gfvlxWOq2UxMp20pjKZ2N6aao4S621BXb2fW2K/xhxVHkSQZUYAvL+iPTSfz8PP7SYk3MHloNt1OP9NG5iBJkT7WHy3fMXZgOlsPnsOkV3OsqoNASGLpFcXMHOFBEASsViN2V4Dls8qiPSW0KoHi3HiuNmpITzSyens1Z+q7MejUBENhQmH5fLVaCPhD7DjaxPWzSnll/SkkScagU/Ol+f3pcvopzrYxblAGu4419frtWrs8JFj1ONwB1CqRm+f241xrRPZgMCLnb16J+FM8/hAtukISErIId0ZOIKLOSLh8Jt/8SyQPRa1qZfnscupbmzl+3hGemmBEr1Xz7s5aVKLAwKIkhpQkM3Fo5vlsdmJqVZkMGkqybTS3uzlW1YFWLTJ7bB6qj+n4999wSUpj3759rFixIvr5/vvvZ+nSpTzyyCO8+eabfSqQgoLCJ4MsQ3aKhd+8cjDarnTzgXq+s3w4bd1+ut0BUqxaLEZNtOyFIIAg9a5QSzgMQmRz8hRM4c9v9/SplmT426oTPPylwWjUIi5PEF8gxNpdNdFNT60S+e6Nw9lxpInqxm6GlaWiUYu8dpEJqL7VxeDiJJ545SgA184oQSUKJMbpEQWBsCThDYT5+5pj+AJhdBoV188qxeEOxBQhNOhUFGXbUKsEOrt9DCuMoyhzJF3OAGmJBrrdAcJhiXGD0tFrRUaWp1L5kd4WAwqTGF6mIhgKo1aLdDl9PP/eyegb/ZcX9OeORQOoaXJg1GtQm23YR99BgmxHkIJI1gzu/XtPSZRQWGbFptMsmFhAY7ubNruXcQMz+Plze6O/44rNZ3nwy6M5XtVBc6eH/PS4mJ7i2akWXnq/kuJsG9fOKCEUltl5tAmLUUtJhuWTNU+53W5cLhdmcyTJxOVy4fP1bomooKDwOUAAtz+MTquiutER09+6LDeBU3VdPHu0idR4Iw63n1vmlvO782GfJ2rs6G6cTbDu6EXziWgKR+I6tgUAp6QnEHLHLDm4OJlGR+RNPtlmQKdVxTjdQ2GJtbtqKcuxRU0/F9dqiiIT7WuRn2GhvdvPxn3n6Oj2cd2sUv7yzvFo/wx/MMwLaytYPKUo6n8wGzSUZpk5XWfH7QuyfEYhmw81s2pHxKRjMmi4Y9FAnl9bEZXv4TvHMWFwBjuONCIIAlNHZJOfbuW+P+4A4OppJby95UzMpvy3d4/zreuHk5kc2TNr2vw8/faZ6PWrp0fKmV/MFSNy8PhDHK/qIDPFjMmgwahT4zpvKguFJfYcb2bp5EISEkyca+7mhjnlPLvqOJIMVQ3dDChMZNvhRvae7OkrkpJg+OTNU0uWLOHqq69m9uzZyLLMunXrWLZsGc8//zwFBQV9J42CgkKf4g9JnGt309TuJj3RSHK8kfd21vDB3nPkpVvpV5DIuIHp5GVYkWTISTXT2O5maEkKDW0uRvRLo9sT4Mmvj0D0OVHrjWw97aDf5G9gqfsQWaXBkTWBynYLSZPuxemTSElJIDO5noa2iOKwmrT0L0jkyVcPR+XaerCBa2eW8uyqi4r4uQOkJxoJSZENMi/Ngk6rpl9BAm1dXkwGDZV1XazbXYtKFNFMLqS+1RXNa+hy+GIaO0GkqmxWipnJQzOxmrQMLknh9U1nOXy+0dPRsx18aX7/6Bv7jFE5PLvqOHqtisJMG9kpJt7fVUtbl5el00pAhr0nmzHrNZTm2Kiss4NAjOKFyOmqoc3Fy+sqSY43MLgoOea6RiVGa1sBlObG02b3sON8D/HWLi8VNV0snFwYdfwDuH1BGru8nG50oFGLbNhTx3WzygiGJLRqEYtJy7kWJ7XNTkQBpo6IdAoURfqsR/glKY1bb72V8vJytm7dilqt5kc/+hFjxozh2LFjXHXVVX0jiYKCQp8iIbN2dy2Sz0V+gkBDvZYjZ9Ss2x15qz7b0M1Nc8t5feNpXnr/fETSrFLW7amj7bw550R1B48uL0S/53l8Z/YhDryCHVX9eb6hm+LsiYTDMmf2tDN+kJZzLR7qWpyoVR1878bh/GHFUTodPqaPzOa9i7rLAfgCYURBQCUKhCUZm0XH4skFvPD+KRraXAwoSOSOxQNZ9WEVq7dVk5Fs5prpJTz99lFCYZlQOMyrG06xfFYZu483EQpHigPqtaqYTn1qlYhOGzFHdbv8/PbVgyyaXBhVGgAfHmxgaGkKu483U56XgNMTJN6i40R1B6V5Cby/q5bKuq6YBL+MJBOluQlU1tnxB2Kd4tBTSn3ywCTcPokkW0+PC4AP9tVxx6IBPL/mJG5fiNH903jhvdhSJV5/qFfI0+j+6WzaH2n5OmtMLnUtTl5c2xMcUJJt48sLB9Dc4UGrEQkEw1hN2j5TGHCJSmPp0qWsXLmSiRMnxowPGDCg7yRRUFDoU+yuIKNTPFgPvkT4bBMqWxru4Tey6Xypi9QEA7XNDo6d7clADstEFQbA+PIETEdexVcfOREETu9mcvkITp+zU1lrj96Xl25l9/HIW3IoLPHC+xX89KZyHHYnpoQkPjzc2Es+UYhEALm8Qe5aUMavXzkUfWM/VtXBX1ceJz3JRCAkUdPk4PGXD7D0imJevujNu7qpm9sXDaSz20c4LHHNjFJeWVcZCVlVi1w9vQSPL8Qzq05gNmi4ZkYJBo2KpVcUI4oCh061UZxtozjbRn6GFYNeTYJFx+sbI/6Gc81Ols8u40R1Z4zsowek43T5mTsuj5IcGwMKk/jjiiN4fCH0WhU3zykjz3cSbWAdssmGK/NajPP74QuEkSQZjToSyTVzTC4atYp4iw6dVt3rpJQcb6Q0Jx6VSmDG6Fze3VYV/fNKTzKRnWrmXEskGVCnVTF5WBY/+tPO6PMWo4Yf3Dzykw+5NRgMNDc3k5aW1jerKigoXHYsgovA7j8RPp9xHLY3Y9z+B64d+1X+urGZxDgD1Y0OEqx65g2NQ6cWCWpiI22GZWsI7z2BaLCgSi9B7m6hX+gEV44dxNo99ahEkYWTCjjb0B1T5ruxzU3gzB6Me15GPfUOZozKjZ5mIBIOm5po4nsTJWRUNITDvUw8Zxu6GVGeGv0cDEm9kg8zkky89H4F3a5ARI56OwsnFQIRp/3+k80UZccjSTIOd4CDlW2U5Nh4d1sVwZDE1dOK6XYFopFOggDXzyojwaqn0+HDGwhhNetYNLmQjfvOoRIjm3ecWYvd6SMjyUQoJFPbZOfq6SV0dPtIjTdSoO9A3vCX82G2dahT+rG9MiWqaG0WHVdNLuStzZGSJ8k2A3PH5fHmph6/R06qheqGbkwGDZIs09bloTxF5LpyM1rZR5fopmRqIbtOtHL4dDuLpxTy/q5axg1KJyfVgiRHGkLVNjtJ/8hJ57/hkpSG1+tl2rRppKWlYTT2VEt89913+0wQBQWFfx9BAA0hZAGCUuw/Z1PQjs8TmzAn+Vyk6SKVYE+fs3P1xCwWptSiOvQCcihIePo9rEsy0tQeuafbD9KIqzkYyGPbGR9FyRqmxAeZbhEpyh1CWJLJsKl57YPY5kjj+ydjcO3GD4RckXavN84p50R1B3FmHfkZVlSCTK2+FAQB3UWd8S5g0KkJhmMViVbTc19yvAGNWhWNwFq1rZqvLhnMgcoWjp7toCwvnklDs3jmvN9EFKAsN54XLjLnqFQi7+2siX6WZXh78xlmjsnlrc1nKc1N4L0dNTS0uZgwJBMpLLF2Zw3N7S6GlKTg9YewWbQcPtPOax/0RHpNHZjIVXkjkWv2Iqi1nJWzqaztcU7bnX4qarsozIzjbEM3bXYvVY3dfOOaIZyq7SLOrCMj2cRvXjkYPSFcNSqR+PrVyJUR+VMFkdDUu7n6igEsmlyISa9Go1ax82gjDlcArz9EeV4Ceq3qk3eE33fffX23ooKCQp+glv0IDUdx7HkHQa3FOnYJoaRiwuf/Wau1GhBVcHGIrCCSkprMtTPS0KpU5IgtOHe/yIWtWVz3GPcu+Am76mSqGrvJyMnmvZNq1u1tAOBMPew8reW+m0p57vlDqNUiD88zc/ecTJ7d0orTE2RkiY35ud3ogzb8gNh4lEZfMWaLmfyMOFSiQCAoIYZ8nKl3o1aL9MtLYFT/NPYcb46Ket2sUt7d2lPYsF9+AmV58dy1eCCCIGCz6Pjl8/ui18OSzFNvHuZ7N4/EYow431durYpmpyfZDDR9pHR6MCSRm2Zh/OAMQmEZWZZZv7sOjTqinALBMDazjk6HjzUXde+LM+tRq0WCbgkEkU37z8XMu+loB9OXTiUrMwcEgY6u3kqxucPNzfP64fGFCIXCeHxB3tx4Bpc3gNsbYuGkAiYOzuTDww0IQIbQgafp4uRGCd2BV6jwf5VnNtTxneXDCIbCDCtN5fDpNlISjGQkmzDq1Z+8eWrUqFHY7Xa8Xi+yLBMOh6mrq/vXDyooKFw2xJYKOlb/X/Rz+4pfkHT1A8gJhZGuFGo9cWMW0L3jreg91lFX4jNZqGtpYcrQLPzVG2MnlSXUTcdIjBuOWiWCILBhX0PMLd2uAK2dXr4/CWQBRK2W0pNP85PxVxDSWjA0bkU80Uo4PWImCjecYMyAhTz5QR0y4PEFmVQeh6Ugn/TkSASRqBIx6dXcMLuMQEgiO9XM/pOtzB6bF/VPxFt0vLejBn8wjBSWGTMwLcYkBpECfUdPt7J2Vy2N7S6umlLIEy8fJBSWcLgDZKfENo5LsOgYVpYak9x387x+WE0aRpankpJgZEBhIvsrW6OmMY1aJCvFxOMvHQAi+SIftyEHZDX2HW8h6gzMHH8NJ/MsHKmJJADaLDqumV7C2fpu/MEweq2Kgow4lkwt5MPDjaQmmDDo1EwdnsmCifmIgoCu4yCej6wR7m4j4PXg9gZxe4OEwnKMz2ffyRa+dd2wT15pPPnkkzz99NMAqFQqgsEgRUVFinlKQeFTQgoHcR14L2ZMNFqpc2tZvfMkLZ0epo/IYnxGOUnz0gg7u1CZ4yNvp1Indy7ojyCA1x7rp5RLpvBSdRq7KyN5GBGHsYj0ETOR2tmEfmekFLrXkoBt7FV0rvsrWiAsiCRc+TXcLjdanw9/UgknW0LcPTsDv9uJlgCqxHR+8dKRaNKdQafm9kUD+O2rhwC4akohu483s/l8N7w4s5ZlVxSTl25l57Em1KKIJMONc8p4YW0FshypK3XdrFKSLDquGJaNQadGliW+df3QSLVZkw69TkVmspmGtojzOCTJvLW5x4/g9Yd4dX0lX182mMKsOIx6NS53gJvnltPY7kYlCgwtTeH3b/SED7fZfWSlmKm/qJtfsk2PrfsUyBKSz43rg79x25zv8q06F2FJ5kvz+1Hf6uL1jafxB8LYLDqWzyolM9mMyxOkrrmZLqefn31lLA53AFEQ0NuKABGVxYbKFEeg9Rxi4Wi2VkZCmwVBYOO+2BNPMCTR0uWhNNN6yX+3/hWXpDRWrlzJpk2bePTRR/nud7/Lrl272LJlS58JoaCg8O8hCCKi0Yp15DxEvREBAW98IQ+9dJaCDDNpcRraWjvwuo9hLBqBqDGCwYz39H4sqQUEZBkBAVXOQERLEpIzEoLakTyU3bt6oql2H29mxqicGLt/RpKRVH9PdVnJ2UnA4yZxwT2EfW5UKpFOycheXwZOUy4GSU1KqoH6bhfrD/soTDVgc/tjsrS9/hD7TrTw8ztG4HM6EY0WdBoVr6yP+AmKs2zIEOOPqKzr4p7rhvGl+f1xeYK4vEFeXV/JN5YOJs54obifirKsOFLPh+PuONrEkJJkJg3NRCYSkvtRupx+KuvsfLC3DotRS36GlU3760mw6pEkmawUc0zl3M37z3Hj3HJO19k5eradslwbS/qBauuzMY1WNfZ6rpnRH0mSMRm0vPT+wWglYLvTz2sbTvPt64cxtDQFjVokN9XC7984HM13KcuN5+alT7L/2DkauoKMnxiHRquhYnfkZOFwB6KNri5G/Q96mP+nXJLSSEhIICUlhYKCAioqKli0aBF//vOf+0YCBQWFS0YUBUKSTDgsYR06g/b3/kTYEdnwNakF/O6mG/AfXIPK3YaxcAEaVTEdb/8ayedC1JtImHkbxzp0vLHlICkJBmaOyoGJ/w+VvR4RCbchHehRGudanOSkWvjmNYM5cqqFvDQL/U3tiJtWxMgV7mrklyfSOV7lZ+rQdErzk3hu9ZHo9aElyWQmmynPT8Ri1Ebf9C+moc1F/LkPCR1eg6pgBLoBV2MxRnIORvRL4fWPONsBDp9qxekNsPdEpKe3ShTwhyQM6ogy6PaG+OPbR6istZMUp+fry4bwm1cOYndFNv1vXD2k14aaZNNTlBVHdUMcKQlGBhUlsb+iNVq0MCXBSEKcngmDMjDo1Jyq66Klw8NNUzMRx0f6icjbn8MfCsTIakhIxuTUEAxKuDwBPhIIRpvdS3Onh9c2nCI53sDofmlRhQGRrPpHXjwczQfZebKDG2b3RHqt213LlRPyefrtYz1r6tRkpfZdCRG4RKWhVqupq6ujoKCAffv2MWHCBPz+3i0QFRQU/jtCsowsg1YlxrQLhUjpj10nmtl6qJHibBtz+unQeXs232BLFcbGI3gbTxD2e4jTaGhf/QfkoB/RYEbyeeh472lS53yXkzWdnKoTyE6xcLKmm5KcHGQg1aRBp1XhvyhBzu70UFj3NiXuauQKP6YBk+mWY81VXfH9Oba9E0mS2bC/EX9YIC/dGi1rfvBUG+MGZfCnt44Sb9Fy45x+vaqxTi03Ezr6PJLPDSGJZ1ad5FS9A6NeTVgKYzbElgYHMBm1dFzUjnVISTJhWabN4cds0LBuTy2VtXZsFh2SDDUtDmaOyY0mFZoMGq6ZXsKbm84QDElYTVqumlxETaODDoeP0+fsxJm0LJ1WjMcXQgACgTDXTC/hhfdO4vQEGd0/jWsHizjf+Qnh7jZUlgTiZ3yZUEd9VKELKQW0qFKxmrTIMtjM2l7fJc6sjbahTU80cabBHnNdFIWYBEKI9Pr4wc0jOVbdSVqCkYwkE19dMogDla1YTVqGl6cSCIU++Yzwr3zlK/zoRz/iD3/4A7/5zW94++23mTx5ct9IoKCgQFiWOVXv4JX1kcS0RZMLGVachPa8+UQW4M0tZ6M2/nMtTvad1PLApKvR7P57dB5BVBE/YRmS340gyxiLhqFNyibk6kJtthHsaEQjuPnZ4hRktQ63RsfJGqLO0wEFiXzrumG8sfE0NU0ORvdLZVGBC2nrNqTzxpawox3zyAW4D64FUYVq2CKa1VlIUo8Dds/xZuaOz6O22RF9y+1w+AiFJdrsPipqO7l5Xj/e+OAU/qDEvLHZDJIPR7vYeRKKObXr/P/7Qnyw9xzfvn4Ye060RB3SWrXIsJIk3tsRiWoqzIpj+sgcfvzn3XQ6fNgsOq6fVcodiwZQ1+wkNdFEW6eX1durI4UXiXTl23ygnqumREqk+wNhXlpXydXTiqlq6CYxTo/3/NgFfnjzSP7wZs8pqjRJxrHq8ajsYWcnHat+S9zi+3C1NRMUNFQ6zag9eg6faUEAirNt3DSnnOfXRoocatUi184o5bUPTlGQGUeX08ew0hQqanqy0D/usCDJ0O0O0Nrppjgrjr+vOcG4gRksmVpEKCyzels1g0uSkfswxe6SlMbUqVOZOnUqEPFv1NbWUlbWuwGKgoLCf0Z9u4dfvbg/+vnpt4/xjWWDGVqUiCyDwxNiy8F6zAYNhVk2Wrs8NLW7aSGJrPPPGEtHE3J24NoRqTydNO+rIMt0be3pGGcsHY1KCmDZ/DtAIG3ElRw2FkevH6vqIDfNwpXjc6lpdjEsS43mvV9x8ZblOrqZ4BX3UDnwHkKSzKod3UwaGsag68lonj0siSlp3UycAx6VlUOdJtrtPT6MNTtq+OqSQXxl8SA0KoFCXRfeFe9Er+v8XdgsSVHfQU6qhQznUb53/WAOV9lRq0SGFMSR0n2UG+eU4/WHSI438NSbh6MVee1OP3975zhXTihg7a6ID+bWK/sBEXOUTOTt2+EOxFTT7ZeXwKCiJO6/dRTJNj1PvXlRcUaI8cUApBv8PS1bzyMHfNg77dy/2ofVJLN8djKiKOIPSHh8QfQ6DdlpBh66fSxdzkhCYG2zg9lj8jjX4sRm0VGSEx/tbigIkGIzYNKrYzrzzRiVQ1OHm4376hlQmMR1M8tYvaOav7x7HJNezcJJheSmmelLLklptLe38/rrr9PR0REzfv/99/epMAoK/4uIohBTlfQC7+2sZUhRIqIoIIowZ2weOq2aE1UdlOUmMHN0LjrdefOUIGLIH0ywo4H4SdciSyFEnQn3yR0xc3oqd6PPKj3/Scaz710WzP0O7x3ouedYVQc2q563Np/ldJ6Vu0rGE6r8sEdeg5mz3Rr+/EFPn4nWLg/TR2YTCsu4XB6ujD+FZ91KVIAFmDFkNgcSJjD8xhEEQxIf7KvDZtHRZvciy5CaYcOQP4RA9SEAVCfX87X597O/2k2cSYfNrEXb/Dbp1S+SXX4FhAOE3tuMqnQcO+v0tNm9LLuiOKowLuALhEmI07NgYgFOT5BDp9r48oIBvLHpNL7ztZ3uvnoI72ytoq7ZwaDiZMYMSCMYkihINSOKAsPLU+hy+pgyLIuwJKPXxeZcGK02BJUGOXzR2oKITzTi9LTh9ARp7fLy/JqThC9qEXvn4oE88+4J/MEwggDfWT4cX1DCZtFhNmiobXZw4+xSlkwpREBArRa5dmYpZ+q76bB76V+QiM2ixesPc/X0EuItOnYea4oWPXR6grywtoLv3TiCJLPuX/wtvHQuSWncc889WK1WysrKYjr4KSgo/PfIcqRb3EdJjNOhbj1BqP4ECcVj8QfD0b7VlXVdxFt03H/TYIR594EoIGr9ePetIdgeMWHZJiz72PUkf+ybsuhqO1/cLmL0HlCYRFe3F5UocKzGwZ6c0UyakIm/Yjtich7Bkmk8+1KPPyI1wcjoAem8ufE0Hd0+HlyciWf1OzFr+A6tpWj2cO5/9Sw2s45rZ5Tg9gZZs70GfzBE59AsRg2/BTIq0Aa60aVkYNHrOVBRTWuXF51GhXrelZR2NBM6vh5kGTkUgAFzmJWhxeMLkp1qQaMWY1rNiqKASa9hw946rCYts8fmEW/V8q3rhiHJMkgyP/nbboaWpjBleDaVtV08+eohfvn1CZHfSpIZ0z+V3DQrp891oVaJpMQbGd0/jd3nExHr/UbiRl2HsPN5LpzI5OFLqfFGckJsZh0tHZ6owrjA+j11TB+VTZfTT7vdS22TIyazPi/dysCCpEi2vABxRg2FmXG0dHpQiwJxZi2ZyRYe/ftePL4Qc8blsetYE5OHZZGeGKncsfdkC43tLsqyrJ9s9FR7ezvPP/9836yooKAQgyzLDC1J5u2tVdE2o2qVwNwBerre+ikAAVUym/bFNj/qcvqprHcTcoYIhmWmZbqjCgMg7OpEk5hJsKMnOU9tSyXs6Y5d35xEKNQGQH66hRHlqdS3OLlqShEGnZpj1R3EZ4zmkC6dhtogA7UixdlxHDkTsTwsn1XK4y8diPoa2tq7SP8YC7y9y4E/EKal08Pv3zjMd5YPj4a+igJUNvk5UW3lQIWPH9+UyZ/eOR01BfmDYX7/dgU/ue2b1NY2oVIJ5Oek8sKWc9HIqdsW9GPJ1EjvDFmOlFi5bmYpLo+fBRMLEEWB2iYnOSkWshIMAJxtdiLJkR7fF4i36AhLEmebnVgMWnzBML98fl9009dpVNx/6yjGDEjHGwiRkWTi/1YZWTruW5glJx7RzMojfgaVRf68JFlG/EgorFYtMntMHkfOtFHX7GTJ1CL+vPJYzD01TQ7aur389tVDCAJMHZ7NVZMKWDA+D0GlIhQM853ffRjty9Ha6eHaGaWs3VnDlgP1iAJMH5VDXlrfKQy4RKWRnJyM3W7HZrP13coKCgpREs1afnr7GM40dBMMSRSnaNC//9Po1iuIKkRRQrooA3r+iERGBffiOf4ugkqDKmVpzJzOI5tJmLIcd1MNofpjqDL7Yxk6A/ua30XvEcqmErBm8+3lOYQlmTizlne2VsVsojfOKee9HdWR3hHA6XoHd189mH4FSQAE/IGYQoLVDh2ZcSlI3T1zqCwJ1Ln1QERhLb2imL+8c4x2eySMVa9VcduCAdF2pR5JQ11LT+vXCxyvdfDqhogSNOiauXp6SVRpnGtx0W73cseigZHTm1VHMBTmqTeORMull+bG4wuEOFDVgccTpLwgkcQ4PR3dPXLcOLecB5/ehS8QpijLRkqCIeaU4A+G2X2ihT3Hm2jt8jJjZDYJCRYeXXXBxOijX34CjefDih3uABlJppgue/Mm5PPS+xV0nffZNHV4elW4BaJyyzJs3HeOoqw43vmwmpZON+MGpjN/Uk+/DYc7wImqDqobI/4VSYZ1u+sYUpLSa97/hn+qNH72s58BkSzwZcuWMW7cODSanrA3xaehoNA3yDLYjBpGFEc2Ym3bCRxaHaYJS0GW8aVkMG2ExPvne2GY9Gqmp3Xi3vFG5PmgH9FgIhITdH6Dk8L4u1p4yTmK+NSRmIxW2vb6mTXtXoz+TmS1jnN+M021Pv6+5gSyDDfMLuPY2Q6uGJFNks3A6XNdvLezmruvHkpblwejTsPeE83EmXS8tfksGrXI0vGZMd9lxe42Biz+MslV7xGoO442sxR76QJee7MxKrsvEI4qDIhsjntPNDNpaCZpiZF6Sck2A232WFNaRrKJa2eWIpx/prXLE930txyo594bR7Budy3Hqzopz4tn7MD0GIVWWduFZ1SIP604TDAMN80tZ974fM61OKlrcTJ3XD6vrKuMbtZ56RZaOj9avAMcbj9pSUZau7ys33uO2+b3Y0hxCkfPtFGen0iSTU9zh4dlCUbUKpGMZCM3z+tHVaMDnz9ETqolqjAAjp5pZ1S/HpMXREqd6zSq6Pc9VddFe7ePRZML8fpDeHxBmtpdTB+Zg06rIjFOzzsX1eq6QE2Tg37Z1k8m5PbCyWL48OEMHz68b1ZUUFD4l4gJGZj6jce+820Ih/CNT8YfSuD+5f3RBhyY48wYDr7MxVuqFPCSMO1GnAfXE3J1YSodgyYuCXVbmJV7OjDouvjW9cPYcKyFHUfbsZl1zBprQiUIUfOFWiVw49xyVm+vpqndzbDSZG6bP4BfnLebiwIsmVpMVrKJB24diSAI+LpayEoyUn++Mm4wJOEIqLAOv57GbCcqgwG0ZoLhiOnMbNT2yjcAaOn0sO1wI7uPN1OYGceiKUX8fXXEUQywYFIB2w83sudE5I0+MU7PVxeVMyrVT8jnQbSm8Px7J6lpipxQ9p1spabJwawxeby7rWcz1evU3Di3P7Isk2Qz8OQrB0mON5KWaKTL4YuJjmq3+xg9IJ2jZ2ODgAYWJrH6ojmfW3OS394zmUmD0gmFQrQ5A/xl5XHsLj9GnYpFk4t4YW0FqQlGdBoVuemWmPlO1nSyZGoRqYkFbDvUSFaKmYWTClm7syYaJHHdzBI6uv2s2BQJ9zUbNNxz/TA+2F3HzmNNTB2WRUmOLfr7XCAtwfjJNWH6+te/DkAwGIyeMNra2khOTv5njykoKMD5qCeBUKj3v1hBAJ23lXBXI4JWD7ZsAioTKpUYyVD2eHDsXYM8cB4BfSJGLcwqFkisXYXWmkC4zYG2ZAyh7jaCnU2AgOTupnvPKkylYzAaLXiqDuM+uYPFi39KUkoi8WYtR8+0RcNPHe4Af115jBvn9ovKlJJg4omXD8RUhn1m1fGo3VyS4fWNp+mXn0BRqgEZGY8E3xovURdIwxkQyDEHSIkLcc8zJ6Jv7OMHpfPDW0bS1B7pKGfSq3vVSRpenhpN+AtLMq9/cIqFkyNFD1PjjXQ6vLxz0YY4IFNPZv0G/AdXA6Ayx3Pr2Dt46C1nNNu63e7DZOjZ5hZOKuCdrWc5dd7UFm/RcfuigefLdbgwGTTkpFmoa44onpM1nUwYnMHy2WVsP9yIWiUyfnAGgiDEmM8mDsmkpsXBrmMt9MtLoH9+Aj+9fQytXV4sJk20CdWFU0tLp5eirDjO1Pf4lzy+IFazjuFlKaQnmaiqt8dE1SXbjLy8ric02OUN8tLaCvIz4nB7g6zaXs1XrhrI6XP26ClmRHkK8VbdJ1dGxOl0ctddd3Httddy5ZVXApEy6T6fj6eeegqz+V/H//7iF7+gq6uLRx99lB07dvDII4/g9/uZM2cO99xzDwAnT57kvvvuw+12M2LECB566CHUajWNjY3ce++9dHR0kJ+fz2OPPYbJZPoXKyoofLoIArQ7A+w61kxTp5tJgzPJT7egucgZquuuof31h6NhmoaS0ZiHz8HZVIcsSViTU3BMvZdfr2qi0+HjpivSmZnagjfgoXvPTjSJGehS8zEPm03XhmcAGUFnQg76cR3rqQunG7OMGqcGCKLTqnuZWiQZtKLMr+brUMkh1OpOTAZNND/CZtH3yksAcDuduPf+DkGtQTPuS5wNJpKp7iBXDNAlJ1DhiQd6TC3bjzQxol8abm8Ar1+gqc3FD24eSV2zk7AskZlspqnNzZyxeciAxaSlJDs+mj+xcFIBFbVdMTJML5Txb18d/Rx2dZF4+h3GlM9ix4meTnvxlkgDIqtJQ2KcIaowIBJMcLw60nujoqaLI2fa+NrSIfz57aPR7y0LYDaoKc62ARBn0pJg0XHj3HJ8/jApCUbUKoFfPh+JW956sIFBhUl8bfFAclNMiKJAUZaN1AQj4wdnoBZFDp5q5aopRbTZvdQ0OshINlHb5CTOrOPwmXZSEwzsP9XjE4JIf/CPUtXYzaIpRWzYG1G2z646wQ9vGUm3y49araK+xYk/EP7kHOG/+tWvKCkpYcaMGdGx//u//+Phhx/mscce48c//vE/nXznzp289dZbTJkyBZ/Pxw9/+EOef/550tPT+cpXvsKWLVuYPHky9957Lz/72c8YMmQIP/zhD3nttde4/vrreeihh7j++uuZN28ev//973nqqae49957++SLKyj0JYIg4A9LiIDLH+LHf9kVTcLacaSJuxYPZFRpCrIsoyFA9+bnY+L6tYWjOFzvZ8V+I76gzIIhIvkpMt3naySVpKhxn9iO71ykj3SwrY7OTS+QuOg7BMZ9GUlQIafnoZpwM9Le1yEUROw3ha3Bgbz04qHoOvPG51OUZeNMvT06ZpIcaHf+BZBBreWnC79Np92FXvKgtnmoK0tkV0WseSZJH8JfHyke2FDo5rdrIs5pURSQpHbSEj2MG5QRe5qQ5WgBwutmlvLHFUeib8Ratcg91w/j1y8eIBSWsBg13H31ECxGNVsONnK2vpvxgzKovEhxJGk8fNTIFW46xcChC9kR6bvEpKGZFOfY+MEtIzEbNGw90MBHOXPOzq1X9qOyzo5OraKmsZub5pUTCsvo1CqOnGnjynH5DC1ORgCaOz288H4lp8/Zo9/5lnn9YsqvHDnbTlu3j4zzUVqhsMQVI7JZsTlSrmTSkEwCwTB2p5cT1R1sPRTpmTFrzBgyU8zkpplxeUN4fSFG9ktDlmXiPibXojwvAZen51fwB8NIMohiJPQ4J81K3ieZ3HfgwAFWrlyJStWTzKLVarnvvvtYtGjRP53YbrfzxBNPcOedd1JRUcGRI0fIzc0lOzsbgPnz57N27VqKiorw+XwMGTIEgMWLF/Pb3/6WZcuWsXfvXn7/+99Hx2+44QZFaSh85giEJQ6cbmfF5jNo1SoWTCqMydoFeHXDaQYXJqFVCYjhAMHOnp7ZKksCNVIyv1jZU6L79+87+X/zcxlRmsDukx0Y1eGowriAHAoQ9jioFEuQkWlr19DoL8E6+gG8/hDpyVZ2XFT+AmDNjmqum1kaVRrpiQayQ3VE8wtCAfSn15PqceBviGzwd87+Ki3dVqqbHKhVArdMzyFd7aBbowNZxhuQUasEhpWlYjNrOXSqjS6nH6upp75SUZYNnUbFlGFZBENhAsFwjCM4EJJYv7uOouw4Kmq6cHqCvLLuFF9bNogx/dOxmrW0dHm4ckI+a3fWolYJqCy9zeSqjDJy89JYNNmA2aChptnB6XN2/vDmERKsepZNK+71zODi5Kg5Tq0WibcYePKVQ9GxKyfkoxaF6EnR5QtFFQZEcjne21HDuIEZ0WZMBp0aUSVwtsmJTqtCFIWYPhebD9STGKfnRHVX9ESzcFIBB0+38t72GtKTTNyxaCAeX5C3t5xBFAS+d9MIFk0qYNX2akJhmYwkEwsnFfLXd3pCdW+ekU+69wyhM7uQDTbIH4UnoEer71236z/lnyoNjUYTozAuoNVq0en+eYbhAw88wD333ENTUyQ7sbW1NcYXkpKSQktLS6/x5ORkWlpa6Orqwmw2o1arY8YVFD5LCAIcr+mKVhYVBLA7fb3uG1KSTG2rk9ZOD2mJJrIm3Exg4x8BUFuT2VfVO0LnvUN2ls8qJTczEUmrQq0zIvtj7/OpzZys6USjFpk2MpsX152mqeNCfwW4eV6/mGggWYaUeCNLryjGoFMzJM6Oen1sxeqwowO1NTH62bnpWR6Y8RVq2kwYxQDmc2/SknUrFf3+HyoRitLjuW1+fz7YV8+Zc3ZG90+jINOKyxNgQGEi+elx5GdYaWx3c7Kmk4LMuI8NL+10+Eg7n5Sm16pYOLmAI2facXtD6DQqkmx6vL4Qty3oT1iSOeMLkznoSji6BmQJlTWJ9qL5HDnjYM326mh4a166NTq/xahhxqgcNu47R1iK5McML0vhr+8co6HNzawxuby77WxMguCqbdWMKk8lJ9mELBPJJP8IrV0eJgzJIN6iw+kJcPvCAew72YpWI6LXqRGE3vah3cebWT6rjKNVHRRmxNHS5ea1DZHkvpZODwcqW1m3+0ISpcyvXzrAD28ZRW66FbcvdL5Uu8SNc8tpaneTkWSiv6qGtg4XLfFj0IoyqbUHMRVrQJ/Za/3/lH+qNHQ6HZ2dnSQkJMSMd3R09KrAeTGvv/466enpjB07lhUrIiWUJUmKySaXZRlBEP7h+IX/Xsx/ko2emHjpR7PkZMu/vukzgiLr5eHflTUQDLN2V08NDlkGlSjG1GHql5+AWiXwyHM9rUnnjctl4dir8e58nbDPiTVF32tuq1mL3RXAoFPTKZtJG3kNbHsmel2TP4y3j/jZfiRiOspINkUVxgVZ1u6siTETxZm11DQ5ePfDKmRkyufHof5IxVpj4RAc+9+Pfpb8HsSuOhJ2R2padUy5l588eyzaNe8WfQLPv9dTIuO9nTUsnFTAyZpO1CqR41UdGHQqVmw+Q0GmjdomB8XZOb2+74xR2fiDEZNKTqqZToef51b3nK5Kc+JZekURDz+7F1GAu68eyh/P5jJ31LfQEqDaqaOpGurb2qIKI9mmpyCpZ5vrcvoZUJRIWW48YVnGoFXj8QXJTbPS0OYmzqyLCQWGSMa7Pyixdm89KpVAeW4CNosupqfGzfP6YdSrGT0gjYKMOOLMWl5e1xANGZ40NJNJQzPZerDHPJadaub93TXUNbtIsuqiCgOgMNPG8arIn2tKvAFZjpROX72tCplIiRCVCGmJJtbtrkOjFpkxOJH4IhM/Xd+NLxCprluelclduSEy+vDf4D9VGtdccw3f+MY3eOSRR6Jmpbq6Ou677z6WLFnyD59bs2YNbW1tLFy4kO7ubjweDw0NDTGnlra2NlJSUkhLS6OtrS063t7eTkpKCgkJCTidTsLhMCqVKnr/v0tHhysmTvsfkZxsoa2tdzLRZxFF1svDfyKrqBJ7lbl2ePzcffUQfP4Q/mCYzBQzD/15V8w9q3fUMuK2SajmDiYYFii3xmPYVhdVNKIoMH9CIcerOuly+jAbNFR0ZjJw/D3YRCey2kiFx8qa9yJvooIA4XDvv+fdrp4Wp/npZr48KZ5jjd7oBv/mMZlvLPgWHZ0OJASSzFqC9UcRkvMQ4rIQG44i6AyEAhG7uSatiPcrQzFtVn2BUK8SGe/vquWb1w6lqtFBXpqFg8dq+flcPebGLYSNSXTb0rhj0UDe2HgafyDE9bPKaLd7eWvLWSDSFyPSdrUnPLeyrguXN8i1M0oAqGtxMGt8EX9bfYJul58xA9JZND6TlVvP0mrUUJ5tZslgHT5R4NoZJahVIhaDht+8fJCMJDM6rYrqxm4GFydTkGFheFkK3U5fTEl3gPkTCnjk73tj2r3+4OaR/N/rh7E7fVw9rZj6VudFp4JI1FJehjWqNLYebOC2Bf358FADshzJVelfkMRfzmeB210BzAYNrvMVAdq7vYzqn8aYAenUNDlQiQJZKWZEEbJTLLi8IWwWLT/5627mjc8nPyOONCu8uqkmeqoEOFnvotaZifnf+HstisI/fdn+p0rjqquuoqWlhYULF2IwGJAkiVAoxJe//GVuvPHGf/jcM8/0vA2tWLGCPXv28NBDDzFz5kxqa2vJyspi1apVLFmyhMzMTHQ6Hfv372f48OGsXLmSSZMmodFoGDFiBGvWrGH+/Pm8/fbbTJo06ZK/uILCJ4Esy4wblMHBU+2EzrdELcywUtVgp7bJgdmoZeexRm6aW84zq07EPCuGfOQFzoDWRJsvxAO3DONMXQfBkExpURp/fec4VQ2RkMz1e+q4bUF/6rGwr91Kss2ALF4sB2g1qvOO6J4NfM6oDEYFdjNwXjzarhPIH2xjzMxvs9qkJd6iYeHYTNY1BHl9U+d5Z20q04dfw1ubTlN11MvYsqGM7peC3nkONZH8kc762JPJhRIZmclm4sxazpyzYzVpqWtx8tqGU8wancP87C7k7c9wwfVvPbWNIYseoK5fKhqNigSrPqaMRliSeWPjaaYMy+KdD3vyIQJBKepMv3FOOc+8e5zxgzMwG7QcPdvOhwdquSH+AP60eFSd+2gPzeXhF3pORblpFq6bWUYwFEYUBMwGDWP6p3G2vhu7y09Gkpm7Fg/kdH03DncAq0lDIBiK+U2DIYnDp9t49Ktj8QUk/IEw9/7ftpjfZN/JVq6fVcrei0KE/cEwX10yiEBQIj3JxGMv7qc0J570JBOVdZ3Mn1gQ9Xs0d7jpn5/Ao8/tjYYPq1UCD9w2hqwEA8nJFo6faePe5SM4VtXO+j21zB6TS11b70i3NhefbOe+O++8k5tvvpmqqipEUaSwsBCttncDkX+FTqfj0Ucf5e6778bv9zN58mRmz54NwGOPPcb999+Py+Wif//+3HTTTQA8+OCDfP/73+cPf/gD6enpPP744//2ugoKlwNfSMLu8mPSa0iJN/CVRQMwGjQIgNmgxeULc/RsB7XNTkYPSEeSZfrlJ3CiOhIKmhpvwNa8l669rwJgLRmFeeR8tOp6JI0Khz85qjAgYiLpdPh4a/PZ6Fhpro3po3KiuQ0b9tTyvRtH8NqGSjq6/cwYmclY9QlC+1eioae4ebwuzE9Gt6Hyd3POk8KL66ujc27YW4dRr6ay0Y3d6eedXV7OdYa4ZXwitvFLCPs8zOxn5ORFfR7CYYmvXDWQytouOh0+llxRTHqikfrzXef+P3vvGSBHeWV//6qqc5qenpxzljSjMMoBoYACQYgMIuOIzWIb29hmnb327tper/8O62zAYDICkRVAOec4oxlpcg49nVNVvR9q1DOtkb3YBnv3XZ0voKrqqurqqec+z73nnmOXIojHNzBeOUuNhpAGzrFxXxRZUclJmziz9fgjmAxjQ5RBJxId51UejcmM+CNxEUeAc50SS5bkIOz7I0LZPP64z5ewKmrt8bJ8lo7HX28kKivMnpQ12h9hYHplBjFZoaljhN+8eiI+yE6rSGd+XTY7joyRF7yBKD2DQQZHQiQnTUwtAhMyHCkOM4MjQWKyQo5gY92KSg419HPq/CCTSlJxWA185d563N4IyQ4Dm/a1J7j7xWSV3ce7uGWx5v1hs+p58u2GeBorNclEfVUGGy8yt8pK+zs6973zzjsTtrW3j1Holi9f/t9eYO3ataxduxaAOXPm8Oqrr044prKykhdeeGHC9pycnMtCiZfxPw4Dvgi+oQEMOhFPAHRWB+19Pt7Y1YJeJ/Lpm2r53YaT8ZTNhu3nWDWnkOUz82jt8VJT5OKmOgNs/E38nD7VzKYTUV7cFkVRwiyc2snV84t4bYc2oM+vzea1necT7qOh1c0Ni8sozUlCEAT0ehFFVclwWSnLS8bnC2A19TE+Q2+qmk/QnI4uU0S0WDlyfCL3/1jTABX5yXFJi8ON/dxaC+6dWk2jqMTNx669hld3tqPTiZTnu/j+UwfjqbVjTQPcuqycQFg7tzjRhhsggWGW4bJMWCVlp1pJcRixmnTkpttYNy+F8/3DOG1GFFVFusSJHTYDoexqQrOcOAvK6DlxasIxA6M6U6oKu493k51qZc+JHjr7fdy0pCwueHgBhxr6uOOqSnYwFjSmlKbyo2ePMOQJ8bnbp01IaWW6LPFzCAJcMS0XRVV4ZmMDMVnlpivLeGdfKyM+LfW25UA7Q54QV80qYN/JHqZXpsc74ROeWTCGTi8SjsoMuMPxgAFap31qkonplekcaujDZNCxam4hwVD077fS+HMDtiAI7ytoXMZl/G+HIILbH8UfjOG0G1HDfl7c2cXJVg+FWQ4WT8uJp1BkRaGlxzshx7/1SCfLZ+bw1RsLSHOa8D3/VVR5dNAUJTpSZvHsGy3x49871MF1C4tx2oy4fWF0kphgwQqwfFYBB8/0sWlfGzpJZPmsApLsBoKRGEPeEM0dI1jrp7NiZS1y33mEtALc1kJOtkbpHTRiNQvUlqXx7sGOBLZQdmpiQV2vE5H8Y81yhqQ0TjX3UVOSQkxW6Oz3TWBDvbGrhfuvreHmpeUIgHXWDXg2/mLsmRpMiGlFyErz6DXgY9dP5ok3TuMPRslMsXDftZPIkEb4znw3kucM7DtP8cJHmBvQo5NEakpSyE610jUwdq+3LqvgX58/zbA3TH1VN/NrsxNWIgB2iz5eKAfNP2T13ELc/ggZLsslJU5sFj2luU4kSWDu5CyaO7UU3JAnRFe/j3m12RRk2jnTOkxJThKLpudy6Ewfty4rRxAETp4bZNgTjq96JEmIB4wLONLYT2VBMvtP93KwoY97VldzuKE/4ZjpVRn8cdNZzrQMc8eKRCO89l4vA+4g0ZjCzUvKicQUth/p5L6ra/5+K43Ls/zL+L8ORVU5eGaQ37x6gkhM4ZGbq3hlZwdnO7XCYqrTzLajYzNQjT01keVnM+vRnX0XYc8zRPOqMBVMJnT+GJbyekDlaN/EzxxrGmD1vHw2H+jEaRGYVZ3G3lPaIGK36LGadfF0lazIbNhxjkduncyszBPovD34Js9iV79Eq7EIf2oRGckWzrQO8/jrY7PvZLuR25dX8Pgbp+PnnV6Zzv97/mj8mJuvLGHYEiVp0acRTHZiKTmUGL0Me8OIooBeN3HGLwiQnarRZ3PS7exsjFE+/+PYOvcRM6cwkFKHqE/je5/MRJYVBkZC7DjSxYM3TCGmqKiKyi9eOsbDV+dhO/QSCgLuK77ANx4/Fl+NvLOnhc/cPo3z3VrXs92iZ9exLqZWpLPlQDtHz/bz5XvqCUVibD3Uid1q4NZlFQne5GnJZpbNzOcPb53B7Q1z1awCJpekJGhNSaKAPxjFOSqZ/uSbp7lteWVcRqTPHeTsaJd5ZaGL9l4v2w91Uluexr5TPThtRpbW5/PL9WMOgMmOiSktnSRgMmpDsqKo7DzaxWdum8o7e9uQRIGZNZlEYzKv72wBYOfRrrizH8Cp80N8+uY6nnnnDMeaNPbUoqk55Kf/A5z7Wlpa+MMf/kAgEEBVVRRFobW1lWeeeea///BlXMb/Ygx4I7yy7SwfW5KGRQiT7DDGAwaA3aLDZLDSPE5DKBKVJyi0rltSiFVoZmjOPUQGm3HWXYmuZCaRU1sRXLkUOh1A9/hLU5BpZ5FwiFlVHsTjL1Mx4zbSkgvYdbyXK2fkcqRxYML9HjrdTXHgBJHBTgwtR7l68T08e8LAvlO9PHRLHW/tbkk4ftgbxmrR8/G1k5FllcosA8L5vTy0Io9uj0KJSyU3eABvyjLahhyYRT3mIPzhrdPxWfMdKyqxmvVxLxCAtYtKGHIH6ez3oyhwuivE708EKc5ZgNcfoXuwl/uvTWVedYb2zGSF480DHG8eQBIFZEWlMNuOYLBwbtG3cZhE2twKijL2jEJRhQOn+zja1I/bEyYSU9DrRO69pgarWY/VpAN3Nzfpt3L1VcVIkQEUayZvj6uLrFlYwn+9dCz+Xd7Z18pH10zGqJc4cKaPDJeFm5aUEYrInBmlEN97dQ0HzvTGqbX7TvbwkTWTOdrYz+HGfmqKXFQXp/CrV06Qm26jrcdLVqo1zowCTdqkoiA5ocN96cwCbOOa8BrahrlqTgHXLywGQUAShQRL4M0H2rlrVSV1ZakcaxqgutBFpsvMrElZ2Mx6RFGgo8dHJKZg0v+JHOFfgfcVND73uc8xadIkDh8+zOrVq3n33Xepqan5wG7iMi7jfyrCwSCfnxlE3P8bkGNIaY8mUCNHfFHmTs7icENfPEe/81gX915TQ0uXh0A4SkqSmb7hIGdTC3m9oZ8k6xzuGWjDu2WUZdh2gso52eSl22jv0zwYHFYD1UUpRNq6UJv3gj2Ffn0OeRkC9xSkYNSLDI2EEuRAAPKSJeT+cSJ4e18ir+pTeAqTaekaidcZxiMS1XyrFRXkQBDjgWcpEUTKTBbU5gieq77KmXYvbT1eyvOd7DjWmVBcfundJj590xQa2tx0D/qZPzmTUETmR88dI8NloXcowPzabMrykjndMpbmctqMtPb7kWWFdJeFe6+uJhpTCIZj2K0GctJsPPa7ffFi8IK6HFbOKcDtiyArKocb+ghGYuSl20lNMtPS7eGTN05h8/52zrQMU1HgpG6mg2jzfqTYTgDEw+t5+IbvsrdNk9uwmvQJ30VV4RcvH+exe+spznUy7Anx61dO8OhdM/jCndMREEhNMpGdZuPtPS0MuENct6iEvHQbkwqc3Li4BJNeYtfJXiJROU5mOHq2n/uvncTzmxsJR2T8wSgluU6mlqcx5AmTnmzmbLsbY5Er4flkp1hJd2iN1Cfb3HHhSNC6zqtsIzhPPE+tqqCG6/j+q7mc60r0K59clsr00hQ+KLyvoOH3+/nGN77Bd77zHRYuXMhdd93FunXrPrCbuIzL+J+KfJOXQMM72K+8h5jBii7QycfWTOWHzxxBVeHk+UHmTM7kmgXabFAAirPs/OsfDiIIAga9hD8YxWzU8a37prKsykdJjoPAG79MuI5+z+95ePVXOdCjOdnFYgrBYJjkSXOR88sIpE2ivUXzfL6Ah26pY9+p3ngAy3SZmWQdQgn54scIgkBasoWu491kplhYNbcoQc5CEgUMOpFfv61t255m5Z/mfRTjjv9CCXiQZt/GnvOReM1m2BsmclGBNhiOceBMHx5/BLc3QjCq0NrjY/W8Ilp7PEyrTCcaU5hRlREPGjOq0mnv9cbtTa+aVcCgJ8iB02MifWsXl5LhGquvbD/SyRfvnMEzGxvQ6UTuubqa3HQ77x3qIBJVuGtVNSebBuN1gCONA7T1+PjnWdchHnx+9Kwq5r4TvLhFTzgq87nbp034za1mPaGoEhdLnFySwumWIWKyiqKqZKZY+N2GMQXfpg43iqyydHoOsqKlNKOywq3LKzh8pg+HzcDymfk8/U4DC+pyMOglzCaJE039zJqURVqyGbcvTCgSw2E1Mqk4hYwUK3MmZZJqH2Oq5mfYWD23EKfdRExWKEw34zjyMyI9o5Tk9CpGLlGPuVSN5m/B+woaF3w1CgoKOHv2LFOmTLnsFX4Z/ydgUEIMLnyIFw7009wXYX5FNrNrVL511yQG3QGcyTYG/AKKCtsPd2DQiaQkFY/OXtVxEuMm3tzfQ2O7hxvnGCkRLkoXqAq2YDcvbAkjywqzJmUxqyaDjW0WDrakcsuVdl7eeijhI79ar82AewYDiKJAWboe6ZWvML6LQq29lt9ubMeglzh9foh5tdlcf0UJe0/04LQbWT2viN+Pq3F09vvZ1pfDiuRs5OEu/GmTeOudsU7lMy1D3HhlWcKKASA/08Hjr2tGTivnFNI94OdwozZ47z/VS3WRi9uXl2tNdjoRp83IT8bVTdJdZt7e25pwztd3nOeaBcW8sGXs+sPeMK2jsuUzqzPjPh8Ax5sHWLOoJMGJb8gTYlCfwXiVKr0rk8/clo8sKzjtJhZPz4trRgkCmq9FsplPrJ1Mks2IKAocOtPHlgPtiKLAdYtKqCtPi9cSAE6dHyQn3UZrj5fcNBsNrcMcauijutCF2xumfyTElTPyaGwbJhiWmVqeytXzi/nthpOEIjIuh4mPr52M067ngWtrkEQBm1GXoLyhl0S6Bvy8PlrYl0SBL193M2kDP0CNRZBa9rKqbjJPbh27L1GArBTL37dPA7Rg8Z3vfIfrr7+er3zlKwQCAWKxiforl3EZ/1shSsIlpXHculS+88dTVBW4mFbp5OD5IQyGQZbl+MiNtCAF0+jxZvD6zl5mVGUQjcl09vkSPBlEAdatrKJnwE80phASLAiLPg6v/cvY9Y0WrBm5PHyrppN06vwQ67eeIy3ZzPHmIRbUBRNqBqDN8LsHNb2imKxQW5rK3dd8kXDDTgRvH8bKBTR4rHx+Tiem4XNIWeXsHRxky8FhppSmoZME9p7sof8i6fPjLR5WXf9PyAMd6OzJ8aZF0BrUzrQO8ZE1k3l9xzn0OpGr5xezcV9bfFASReIB4wJOnR8iHNVUbjNTLFQXpaCTNPaWQS9htxpYUJdDfqadaEzBqJfYd7IngVQgigLJdiMXNqmQkK4BrQnyyul5CaZLZv2YEoVQMoeWSArledpzbuv3k59h444VlURjCgadiNWsRxCgvjwNSSfw9v6OuAcJwLMbG7njqkoOnO4lJqvkZ9gpzE7i3/8wVm9YNbeQwkwHhxv7SUky0dnvI8VhoiDLoTkxRmT+66Vj8dTbkCfEr9af4J/vm4ndpN3vxX+PHf3+hOcqKyq/2T7MZ1Y+RnffME5DjGlKL8qyMt7Z34HDauCK6bnYLPq/v0f417/+dbZt20Z1dTU33XQTO3fu5Fvf+tYHdxeXcRn/IERiCqfa3Gza10ZmqpXlM/PJSDLFX9jekRg3Li5j0/42Dpzu5Yrpucy3tzGw4Yn4OSbn1bB08lW8sk9zphMF+KdbptI/EiQaVagrS+Gl987FDXW2HelkZmUKH7vmcyhntiJZk4jlzeD5IxE27DqEoqjMqMrgmgVFcT2ijj4vVYWuhBm+ThLJTLEye1ImoiQyqSiF9ScGSHEsRDGpFBvs5LtfQT6xCQVQGrZRXziV1sJFvHekE50kcsNoo9h4TK/M4NyIgfMDaUxziSyclst7Bzvi+xvbhqmvyqA0z0lMVvAGIjS2jRV0L7ZovQABha/cU4/JoMMbjFCY5WDn0S7C0RCr5xWy90QPT41Lv91+VSWMrpuS7UbuXKV1ct+4pBxRgIIMe4LGF2izb0VVsZn1BEJRFk7NpV8wEZ79KWKCnq3nFBZIY548NoueA2d64z7aiqqy83AntSVjNYBDDYm+FqClpHLT7bR0e5g7JYvnN59N2P/m7hbWraiioW2YYDhGTZGL//fc0fi93ry0nIvVjfrdQYLhGDbDRJFYALcvPGFbz2CAvZ0CL2zR2F53LCnEYhaZUpZKMBQjElHIcJoveb6/Fu8raPziF7/g4YcfBuD222/n9ttv59vf/jbTpk3MB17GZfxvgSgK7DvTx4vvNTGpOJUBd5Cv/2YP3/nYHNKt2nreYDHzxLMH441WNWkKgV3PJpwn2n6SJVet5JV92r/TXRYGPSE8vggmo45ITE1wYAPYd2aQa8qdpHoGCfe302ybxys7WuL7D5zupTDLHk9v2c0GHlhRxB/fFegcDGE2iNy5tBC7GGZKjkHTC7Jr4oDnRwuhoijwpevqydBtQ41peW255TBz5y7mvROax4MgwIK6bLaPdjtX5CdTlJNEXoaNZLsJl93AzbNc5Dj1bD8xQHGOnblTcvjRM4fJS7cTUxTMRh1Ty9Pis+Cufl9C9ztAcU4SWU49RoMJURRo7Irx2w0n4/sbW90cPJM4OL/07ln++f5ZfOVerXnRoJP4zu/2xX8Lm1nPbcsrEs5z3cISstMsmAwSOWk2Mlxm/t9zR+kZNZ9aMaeAwnEUVJdNz+q5Rfz8pWN4A1GyU6w8dEsdRp1IKCpjlnQUZjo4M64D/sL3qS1LpbXbS1G2Y0JfjqpCbrqVL99dj9Wk43TrcEJwy0qZaCbnsBo0xtefgNM+UVm8vjqTzBQLNy8px6AX2XW8m3tXV1NeqK0kkyz6MSmADwh/Nmj8+Mc/xuPx8MYbb+DzjRXXotEoO3bs4LHHHvtg7+YyLuMDhiBAWFaRZRWLUWS8oGsgKtM7HODuVdV4/BEMeonr5uXi8jTg2/gSaiyCZ+qDCZ25OS4janTijM8iydy6vAJRgLLcZM62D7PlQDv+UBTXmsmXvLeYq4jIpGvQ25I5donO7AOn+/jKXVORgz6SHWbEo6/w0JQcwn0t6J2ZYM/hC0+fRRBEYrLCNfPFeMAAjev/xE43Xyibj3p6S3x7XpqFW5eVY7caEICT5yLctrwCFfAFIgy6g/zsBW1WnJ5s5tPXVzK35RfMKSxB8HTjNzzEnSur2HeqB50kUl3oIiPFwozqDBRFRVFUrp6ZyYEmF4caB6ktSWZetQu9QRv0JEmc0LR2qe7nUESms9/H7187xYzKDBASj/MFo7i9Ya5fVEJ7n48ppalEYzL/+sRYmmhebTZfe2AmA+4wRr2IyzaW3gJAFchNs/KJG6bg9oY1D2+DyCs7zrPtSBf5mXZuvLKMHUe74oSDFIeJ6RVp+INRku1GXA4TVYXJFGQl4bAYCISj7D/ZQ6bLit2o6YEdabr4+8a4dkExG3acQ1W1BsobFpdOCD7jkWJSeHhNCb/d2I7HH2FGuYuVs/P55m81hpkgwJpFpURlhRzb6OriAw4Y8N8EjdraWo4fP44oivFiOIAkSXz/+9//4O/mMi7jA4SKSkOHl3cPtCKikp5qZ1l9PtbR5b9OFJhank7vUIBgOEZUVihKdeN56Qfxcxj8ibPfrpCJ4oI65NYj8W2CwUTYmk7voBejQYesKjw9jqHU0KZ1CTeP05IqznGw7fgAJ5rDqGo3C6bmTrj/qvwk0j0nUNx96MgnarIwvPn38f1S6g4+u+ZTvH3Mi14nXFJWo88dQrZqjnMA+owi3joV4pW9Whf5/dfWxPsjQOu5eH3nOVbOLUQniXh8YX79ZhOfm7sW9d2fgSgx3NfPL9aPyQkdPN3LF+6cwe7j3QyNhKivzmTjET/Xz8thcY0TWdCBzoSIMCoVomC1JJoCCQIJzncAZflOjjT0EwjFcPvDRKMTvdb7hoN4A2F8wSiyovD6zhZuWlKGMMpke/dgO33DISxGHTpJRBITa1fBqMz3njxE18DYpPiKabl0D/oZ8oQY8oRo6Rrhq/fPom8ogCAKZKVYeOm9JnYc1XpGjHqJL9w5gyfeOAkIhGMy9103CYdJQlW14F2e72L5rAJSnSaUUfn8k+cHuX15JVFZQRQFNuw4R2meE7vx0sNyaqwbw4Gf8c05y4gaHEh2kS89cTCe5lJVeGVrE/VVGZf8/AeFPxs0Fi1axKJFi1i4cCFTpkwhFotpdpX6D84F6jIu48PCgCeMzd/OXbadiIEBgo75tLbqqCnP15pUVQG3L8xvR3WislKszA0k5qZTh48zvbSGg01uAN4+0MPN9WtItqYgtOxHcOXhrbyasJJEeb4ORVXpHidrAZokyG3LK6gpTuHkuUFKcp1MKU3lxLkBPIEIyXYjlQVOinOS4rx+l8PEvMnpBA6sJ9R2Eufc6xnZ/3rCeeWBDiJ9bbx7UBvw7l5VPeEZLJ6ahc3aSTSjCDmrBmPZDEwntFRNTFbo7POyeHou747WLBwWA8tnF/LSu00EwzHSks2sWViCxxLDDhhyKnnjuC/hGoqqGQoNuIP0DAZ4ZVsz1y4oxmQ0g9FMSoqN3j4Prf1+jpztx+UwUVeWxus7zsdTNm/vaeELt9fx5FtnaOv1M70yneriVJ58Q2N2NbQOc/0VpRNYW9Mr0xl0B4nKChnJFq5dqCnFhiMyBp3IzUvLCYRifO1Xe9BJIrctL2fBlGxtRaSqDHnCCQEDYNvhDm5eWh5vvHP7IvQPB6jOdwLQ1OONBwzQVj+97R18fnYYtesUEVcJp9rayHRWYDfpEEWBaFTmXKebd/Zqv+/NS8oY9oR56u0z6HWaNeu0inSMFzfhCTASiCEAKSEPSsCDcOhFDMDwrE9MWKEpKnj8Ybr6fRgNEgUZNmx/Igj9tXhfZ8vOzuaBBx5gz549yLJMfX09//7v/05Gxocb0S7jMv4W2MJ9yDv+EzkWQQZ03U3kzbyVsJyLQRSIygqvbjsXTwkEIzGiOivjsxfCmc3cs7CIK2dMZtgXJSfNwoBPpsl2JcaqWYQxYAxZKHSJDHvCiKLmjDceqgp7TnRTWZCM027keFM/FTlmclNMrJ5bRExWCAQjzKhMZ1ZNJiqaO9yGna18JL8E2k4i6IygTJxpp9gN3Lq8AgFN9+qTN0zh2U2NuL1hrpicwqqSGGdCJezExbkjAcL7O/n89cU8u1OjZb62s4VP3VRLRUEy4mhPxy9eHmP19A8HeW3Heb54c4X2PAymuJvmeFwsNrhxXxsrZuVjGpUYOd0+wg+eHqMMVxcl86W76zlxbpBIJEZ9WRKObT/gs8XFyFNzkVwij77YGL+PcESmpcvD3aureHXbOXQ6kWsXFJPi0PSfBAWcDhP/7/mj8dVKJKbw7KZGHrt3JqAFyYNn+rBbDDyzsZFQJMbV84q4ckYeA+4gmSlWGlqH8Ac1U6abl5aj14mcaB7EMI6BdbFm1OwKJ5OHtyAf3AuAjt3U5VQjR3LBlISiqAx6QjSNUw14eWszj95VT++Qn+HRtJjdakAnirT0+bCZNZmYDTtaeGdvK4Ig8B+3Zydc1yEP4bDaE/owjHqJ810enh3tMclKsfLondOx/5layV+K93Wmb33rW9TV1fHDH/4QWZZ58skn+frXv87Pf/7zD+xGLuMyPmiYvJ34YokvuHTyTaxTFhDFiiAkMn3c3jB9tnIyDSbUyKg2rCgh2VPYeaSf/EwHrR2DVOfbOd0SIBCWQQ2yZEYK//nMIfrdIUQBHr51GuX5yXFGkV4nsnJOIfm2MO4eN8lVOrKdAyjhEIPyCDo1yua2Qtbv6Um41/K8JCJurYDuF62I5fNQGrbH94sWBw0jJp4ZTYVlpVq546pyvj7fT1QRMbRuQNncTmj659h9ekxLyRMZm83OrMnA5TCxeX8boYjM/NqcCaye7kE/XR4F25Kv0OeHJZUZnGoZpq48jZiscLxpgIJMB4MjIaqKUjh2th9JEuJ0Wbc3xIvvNbF2cSkuu5GYouKwGNh2uIOcdBsWkw6HrwVlsA0G2xABXXoBd131Mf7rlTE2lT8QZnKuiZrr8wGVUx1DtAFOm56jZz2U5jonCCdGYwqewNjfwIyqDH724rH4vzfsOM8/3VLHG7taNHXZqnSmVabzg6cPxanGi6fnkuUamwhkuhLZSAtK9Ch79iZsUzpP4ZSHUEhCFAX6hhJtemdWZ7JhxzmOjKPQrltRyXMbG2ju9CAIsPaKUjr6fdrvoar8cpuHz1x5P/7tT6FGQ5j7T/GF2z7CD587wZAnhMNq4LblFQlMru5BP+e6PdSO6zT/W/G+taf+8z//M/7vhx56iNWrV39gN3EZl/FhQK/XIRRMZyRnNiFZIFXuR39uB/qIF4Yb0aVWM3tSZjw1A/CfG4f40d3/jLHvJGosipQ7mXMhB6rSzrsHO1gwKQ1TaJAbCoYJpFRhUoM8s7uF/lGLUGXUljM/0059VQayoiCJIp2dA1QNPYOpvwWAYZOV1FUfR9z7MxRgyoJHWH/R/a+amkxs7xEARkJwUjeLadMzMLXvh9QigsWL+MUzY+J73QN+eodClLTtRelu0lZXKTnEhLGuYkEAs9XKR9dMRpIErEYd3/ndvvhqa2Z15oTn6LQZcdjNnDpvISfNhlEn8vHrJzMwEkQFltbngaBpbjW0DjG/Loep5WkYJAFV1QrWd1xZhN3fhqXvJKrFiSF7KpsH/Wzar9VGUq7OoWBcsDbn11DT8RJfu24h3SEzSQaZLN9pUocGCbWcAEVm+sggw9MfwC0mY9CJWMz6CRRcvU4kyaoV4NOSzXT0+Zg9KZPinCQUFbJcFn7y/NF4kVsvifz21ZMJvSnvHuxgfm02I94wgihQmGnnE2un8PvXT2mSJ+ZLU2R1IkTQahpVhS427BiTti/McvDU22cSjn92UyNrFpXQ2uNDVhRefLeJdSsqOTEqnniizcsf0/Oom/U55EiI490q1SNRvv3ROXgCYSwmPf/+1CGGPIl2tX/XjvBIJILBYCAWixEOhzEatYcfDAYvd4Rfxv84CILAoDdMe78PSRSoSCvhVcXApte0ly7Z7uBLt38R0TuIEtNjDA1TmGln3pRsdp/oxmkzcvX8IhoGFPJ3r9e86lOn8PLWcyyrMLA0X2RQDrPhjEC6K4enXjjMgrpsetyJzKddx7u4cnouz20+i9cfYcGUTK7N6UNuaIkfo4T8qOPSTblqN5++sY439rQRjsisnJ3PlOww4eQsIgMdJBlVth0KssGfzKT8tWQbkzm0ZWCCXHp7r2csjSWIBKfcSP/w2Lt67fwint54lq5+H1azntqytATGjihqHd1vjgob6nUiN1xZyrGzAzy3+SyLpuYwZ3IWZzvcbN7fjl6nSbLnZdjiFNsN289h0ksUZ1hRVS1lkhVsQt768ziZJ3bqLZZN/iQnR3vwfrG5j3+58XPoDj+P3N+GlFqAcuANXOcP4BJEUBVM+dVI2QtQgl4QdTinLkFvU+gYipLiNIOqctvyCp56+0xCTSMa056RPxhlWmU6G/e28fSobMqdK6sShAQdNmO8m3w8GtvdPDvqGGi36PnWR+bwb5+cRzgmk25RCZyrIdw6Rv3VpxcgODTGlyAIJNkMrJxTyMZ9bcRkBZNxYqApzkkiL8POtQuLMRt0DHtDWEz6ePoxFImhyPCrLe3xFNm+Jg//+sm5pDtMCALMm5LFMxsTrV2Ls5MmXOtvwX/rEf7yyy+zatUq7rnnHtauXYsgCLz44otcddVVH+iNXMZl/K3oGQny9V/tjRcHH7l1MpuOjqVlhr1h/rjlPPcn7UQ+t4+YzsCsKz/K8YiJGxaX4vVHePm9Jr6w1IoaDqAC/pjIPUUdsOdlQCVNb6Jw/ifw27RmsAOn+7hmQTFn293x6zR3jHDHLBvfmD2MbLCTWeJg4I8/IAGihGi00jfr00QFA1tORbh6sYX6qnQUFWa5hvG++AOs5TMxl04nMniO25at4cm3GtlxcpDMnhDLZuZPECycXJKCN7YKQ5VMX9TC6T4bMydnUJjlwGkzYjPpcI0KKFrNenLSbGw50B6fWfe7Q5zrGol3SIuiwCvbmrlyRj4AuaOiihfMoYJheGHLWT52/WRy0210jAouvrbzPIun5WDSiehiAYTDLyXcpxL0kavrRxS01ZkvGOWHG0dYXHsHsRQf+kEzM3KriHacBlUBUYe5eCoDb455coQ7zqAuf4QXNvfisBp4eWszH79+EmsWlmgd0AJsPdTBpJJaPrF2CjazDl8wytGzYymhi931Ovt8FGU7EqjLAGnOsfSUNxBl98keVs7Mw6pIxAD7lQ9gatpLqPkAxsJadGVz2XMuyJu7z5DqNLGgLoeOPi9rryhFEDT2lNWki4tcuhwm6qsz+OG4uk9prpNZNZn88lfHR+/BzH3X1PDK9jH3xnBUJiqrGCWtdlZbpgkgbj/SicNq4Or5RTjtHyxx6c8GjQvUtAcffJCsrCy2bduGoiisXbuWG2+88QO9kcu4jL8Fgijwxs5WPrI8l4qkEKogccIzcVl+pm2E6MJ8RPahxiJEtvySG5Z/hV9s7sGo17FuRSX28JjvQYY+gO/Q2ICnRkNYDjyBZeWjgCbl0T8c4NG7ZtDe50MUBIoyraQc+i20H0EHSPmPkjT7OkZ2jZ0nada1BAQTTx914wv4GBgJctuyCPW5EjFFRWexISDgP70LgOjsu/nRs8dYNiuPaxcUEwhH8QYifPnmMvS+XlRBRHFkIVnN7D6bhicQoSzPyaQyC9/8zV6CYc1A6p7V1Qle5SlJJq6/oiSeB9dLIk3t7gTJ7oIsO72jOXlFgUMNiY2KAKdbhki2G+NBw2rWabkwwGoU8cQm/haiImsZi9FxpqIwmdf2ddE/HGTtFSV0FK8hS9ChtB/HUl5P4PzRCecwdx9m+ewraekaYX5tNqdbhtHrRd7Y2UKq08T9107CH4zy85eOUV3kIvsiW1lfKMLkktQ45XjPiW6+cOcMnnzzNB19PiwmHdcvKiV6EUupd8g/mm3R7j2sT0asWYm15ioUQWLbiZ6453lTh9aseftVlTwx6ltiNen4yJrJbN7fxrnOEW5bXpGgAaZ9zs2wd6wnqN8d5OT5wbgxF8Di6XnoJe05K8CTb57G7Q2zbGY+/lCUp99uwGGZzOTC5AnP7q/Fnw0a4XCYU6dOoaoqFRUVVFRUxPedOnXqsjz6ZfzdEVM1VozFICY0Likq3DjdivTu/0Me0Qa19Cu+AmgFYrvFwLnOEWoL7ej69sdF/dRYhDxzkK8tiiJLImfcA/TqciitWQCSDn3Ew8WQvYP4hsf3XCTxn88eiefSU5JMfHnVCsiaRFQy4+nuwpZZQsqy+5ADI0iWJER7Cm6/n0dyDxCzpEDFYl7Z1cGWo9oseHJREh+59mvwstZAqyBy1ax8oorKhh3nyE23cccsB8KWse+rzyrjRO5aXt6q0UE3729n3YpKxNGCdF1ZWly59QIGR0KkJ5tZe0Up0ZhMaW4Sn7q5lpi7D5MaZkS14khNpbvfx81Lta7jrDQrDeNkQwAyU6wcOzvm77Fufip2/ESxEZKs6OpWI+/4w9gHJB1qSgGCoPWL1BS5mDs5i93HtXtPshn53qvNzKpYwrT6lciSmcmRNyb8FhHByOb9bfQPB9lzooeblpQxtTyd6qIUFEXFIInxVNTZdjfz63ISPm8zG8hOszK5NAWvXzNa2nOim2sXFDMwEiIaU3hzdwvLZuYnfG72pCxkOZHNpigqCgIRRWb91uaEfTFZJRCKxem14ahCapKJf7qplkhMIRiVCYQmNnheXNgfcAdZUJfN8eZBplakk5NmxaSTUFWVqKzRvYe94bgqMcCQd2K67W/Bnw0a7e3tfPrTn05ohhEErTlGEAQ2b978gd7MZVzGn4IgCHQMBvj966do6/UyZ1Ima68oxTFKJdRLYGrdQWikD0N6AaocI6N7B19cdz37Tvfj9oZYt6KSGlcQ5fWxlYStbily33mCJ7cj2ZxMnbwYWWfEd7IJJRZFKF8w4V50yVn4BC1dUZyTRMNFEhGDIyEODNp5Z6+FwZEQk4pd3LOigCSjilFvRjbaUVSF6IbvAiCaHTQnz4kHDIDj50fY3uzk2lWfIHByO/a8Avp3h9h1TJP7kBUFmo7FAwZAtPssJXmdmI2G+P28taeFeVOyeGdvG2ajLiF/H/9cVEYkhhyLIQkypdGzqEcfRw0HkJwZ6PM/QUtUQKeT8PgjLK7LYu+Jnvg1nDYjs8qdFKab8YVi5CSJJJ9ZjyjeAYDXH+GNnnQWzbkHy/ltKJYURgqvQDZl8cB1SURjCqU5dhzbfsg35k4lqHeQJJ6hoTKVXacG2H1aW7T88t7lxM4f5EJbvyDpkYrr6X9vrMD89p5W5kzKIhZTSEkyseNIR7ypMhpTCASjLKzLYduRTkDTqjpwupfF03Jx2AwMjmjBJy3ZEg+woihQlO0gw2VBEgXWLCqhONN+yb9TURQQFa2OczHSnGY+dVMt4YhMYaadNIcRVQWTTsRskJhdk8XuE2P9HwadSJrTHK9p9A4FqC5y0dw5QmluElaTjrKcpPj4bNKLLJ6ey7YjnUyrSMcfjLH/VA/FWX/HmkZpaSnr16//QC94GZfx12DYH+Fbv90b93bedqQLTyDKQzdqMv2iGkMMjuC68k5CHWcQdAb05bN4+rUG2vu0ZrvDjf18/JpyJjlSkT0D6FzZSGY77u2allR0qItQRyOuxXcQKrkCRB39QwrOOXcj7f+jJj9tS8Zdtw5zUjJfuq0Gs83K4282TLjf9j5vfOA4cW6IF7a1say+gC53gLpcPcLr344fa8ws4kR7YMI59jcMs3zmdAZttRgMEruPj9FtC1LN6PobkQqnYMyrABWCzYcQ3S2kJE2Jp4mCYRmjXnvND5zpZUFdDq/vHBtkJVEgHFN54b0WACalREja84v4wCy7exHe+yUppffzq7eaSXWaSHdZ+Ozt0zjfpQ3G6U4zr+7uYOthLaBZTTr++Y7rMYsWUMFpN3GqK8bGoyJTi9fg7o8yNTWZDW8eijN7dJLAV69bS9J7/4YVlRhww6y7SHUVsv1oNzOrM3i9SWLqgs/gdDeApGPAVsbJcyLJdmM8jSPLCu8e6mDD6Ez71uUVFGTa43Lqf3jrDN/++BxWzikkJivo9SKKovLiaDOjy2HintXV8RpPmtPMgzdOxmTQs2JOATpRpCjLgX6UGXYBwahCU+cIDW3DlOY6eeC6SXz913vi+21mPWW5STjHdcJfrDy7dGY+dqueXce6yU6zceuycl589ywnz2kEg9I8JzVFLnoG/ThtJt7a04IoCFw5VWtWVBWVBbU5CILAuwc7sFv0PHTLVLJT/wGChZdxGf9odA/6iYwWZk0GiUAoxuLaNDbvPc/2E/1ML3dxY+l0Bl77ydiHzuzh/vkP8/X1Yx3az21to/6eRzGNtKFLzqDvue8mXkiJEVT1PLZVIhJV+KebnPx63wBrpv4TNjFCh99AdsxIdcdGgg27MWRXsGTa8gS7V4CaohR2jfMOP9zYz7KZBQRDMaJhMFwQwRJ1xHwj5JRM9HEuy3Pyxt4OXt56jjtWVKKTxLiAYWOXH9N11xA9sx339udBELHVLEAoqKfvwFiaaPW8InaMzqqHPWFqil2asN0xjS22al4RT4+jfjoUDwkCXUBsqAuroj3DAbfmZPe5O6bF0z7rVlTGAwaAPxTj8U3tfO62VEQ0U6N1Kyr59u/2suv0ADaznoqiaAIVNCarvHoixH25VUQ7tNy+/vBz3HLXv7NqThEmo8T3/nCQ9duGcTm0QdLt62XOZBG71RAPGkvq89l1fOxentvUyJfurudc5whGg4TDasAgiWQ4TYBA70iIp98+kyBR/uK7Z/nKPTP5xkdmYzRIyLLCY7/YFXf4MxkkvvGR2aSNCggqwIvvNbFlHHV7RlUG3/3EXHYc68JlN1Fblkqy9U9LlHuDURrbhrFbDSyflY/RoOPkucF4wABoanfTW55GXWkagXCMxdPzeGN3C7OrMzDpRc33o7GPF99tin+XHzx9kG9/bC5Zzome5H8t/mzQmDFjxgd2ocu4jL8FZpOOdQvSmJLkQR8aQkor5FCfj6c2a30K+elmfMe2JH5IiZHsPoPLkZ3AXVcCIxALo/g9iEZzgtMdwEhIiPs0PLWxiVuWlPHMxrMoqMwqN7GobxOeBm0WGR3qprrOyXULanljdyt6nciKOYUYRl9iZXSgKciwoygq/lCUs24j9QvX0eHR0R4wkmzVkWoxUVXo0ma/OhGPP0J5fjJbD2sD0dZDHayYU8CG7doqQVVVYsM9BJtH2Taqgu/EVhx5k6gtS6VvOMiMqgySrAY+fVMdnQM+XA4Tz21qoHcoSF15GioqJ88NxIvcAEHJxsWJF9HiYDA0lm7RArcunjbRSRPp92c7RghHFcyjshgFaRa++4l5dA74cFqNHGycKDc+6JNRnI74v5MW301EsmNQAUVl9qQszrQMJ/yWcyZncaK5H5tJz6LpuRxvGmDAPe63VlRauz1xt8LrryghI9nMH7c04Q/GmDM5i5JcZwL7rW84yK7j3Ty7qZGSnCQyXJYES9hQRGbvyR6unVuIoqgMecMJAQO0wveahcXctEhjcqmq+mc9LURR4FzXCPtPaenGKaWpRGITRRzPd3sYHAlxrnMEi0nHXSuruCBhEIrKvDFq0HQBqgrNHW6ynBP7b/5a/NmgcVnF9jL+p6DQIZM+9BrySe3lV4G6mbeS4UqidyhIYYYFtW/iSyagMl7H75NrqtnR3M2RdjMzCgUWzbuZ4Td+Gt8v2l20xlysnBbDoIOtp0fwhWNcPz+X9v4AkwodBM7pEqVGjqxn7uIidAs0x76thzqoKU4hw2Wls9+HySBx89JyWno8GPU6AsEop4wl/GDD4fhA8vAtKSyfXcCe412EIgrXLizBoBM5dU6jDHf0+chJs/HgjbWcOj9IbbGT2Mn/mvB9fc1HsZoXkCVJvLO3lXBEZu3iUp7Z2Kh5UqysomdQY/7oJJFkh5FN+9riM+03T0f4xKzrCex9efSB6IjOupv1b47NeG9bXsFPnj8aH7zvv3YiIWZaRRoj/gjHer1kDQbIdJpIsRlIsblGf7/0eAC8gJWzC3CmO1HKp6NLyiBqzx4baAXw+MJct7CELQe04vmyWQX0DPq466pKZFnBG4ol+HGA5qMdGtfL4rAa+dqv9sbTT9uPdHL36mrOd3ni25JsYzUhs0l3yeY4tzeCipaW+lPKtDFZmUDp/VOIySoHxsnnt/Z4WDQ1d4Ike0GmI97sFwjF2LivlRkVmi+hJGqOiBf3mdjMf0fK7WVcxj8aeiGKoKrgbsXbnVg7EA+v54b6z7DtbIiz3QGumrKIcMe4YwSRpMrpzIkodA8FuXZ+Ic9tPsuJ824AjjZBcHE+y2/4CuH2UwiWZMgsp7irhWrPKyixKCuWXM27g0M8t10rUL6+B26dP5f5rhbkobHZZSgciacFAMqzrSybmoYvGCPVZedYs5un3j6DqsKiablsPtCeMPP0BWP8dsOJ+OB99Gw/919bgyiKKKOD2dl2N6lJJk6eG6S1e4SHi0ugZ4wlAxBJKqD9pA+jXiIUkSnKdqCTtCa3rgEf4ZjMO3vb4pTNq+cV8c/31fPGrhaCYZllM3NoIZfMayYhhL1YUjM53CvhC2rkgZw0G939/oTZ/r6Tvdy6rJwXtjRp3tVZdlbOKeTL/7UrfsyquYWsWVCM2xdGrxNx2Y08dEsdHn8ERdFMk4qyHQQsqUSsCma9lBCYVUUlM8XKs5saWTQtl5is8tbuFh69awYnW4fxB2Pkptv40l3T+OEfj9LvDuJyaP03r+08x6wababdNxRI6PYG2Huim9qyVA6e6UOvE1l7RWn8t2xoHeaWpeWcODeY8JmZNRl847f76XcHuOfqaioKnDS0uuP7c9NsZCT/97UEWVWJxFR0kojFONa3MeKLIAgC0yvT4z4j86Zk4QtEEkgX57o8hCIyFoOEJMDtV1Xwnd/ti/8dpSaZKM75OxbCL+My/lGQiCH1NxBpOaqZIWWXTDhGnnINekcy04u9GM1mhpNzcC25m+D5owh6I+bCyZwdhoONgyTZjEhyOB4wQJPiNtns3PfrkzisqQQjMdKT23h4ipvokBYk5G2/Z+bce3nVIBGNabPKF/f0MWPZEvR7HwdAl15Mk88GaMXWgnQrtVmgvPRZHIDn6n/ljd3nWbOoFL1OJCfNytlxlNU0p5nz3Z4Jmk+7j3czqSSFI439GA0S911Tw283nGDIE6bfDZGl8zG2H0bxajUMfUYRQsFkcvs9DHtC3LK0nIqCZH674STnuzwsm5nPnuM9CQ5wr+08T16mHV8whl4nsfVYP3OmZPHQE5rft9EwwiO3T+PuVdUEwjFy0qzxxr4LON48QEmOjX+7JZuQ34/VaqDHO0R6stZECPDGrhZy0238cv0JHFYDD94whca2Yd7Z04qiwtSKNMwmHRu297DjaBczqjO4bWk5SaOzZFWFSUUuhmbm8/LWZnSiwCPrZvDspkaOjlJ99TqRL62bylem9xEwpmA1wOFIlPm1Oew/1Ut2mhXTJRRfVRXWXVXBNfOLcVj0PLv5bHx1EY0pnDo/yEfWTOLNXS1IosCiabm09noJhKP4QzF+9uIxPnPrNGqKRth/uo/aslSunJ6LUZooVX8BggDd7hBPvHGa810eFk/PZd3KKn7x8hiz70zLEA/dXMsto1LviqryxZ/uTDhPXVkapnFMrcJ0G9/+2ByauzxYTXqKsx1xhuEHhctB4zL+R0AUFEyBHuSRXkSjBcFoIerpJTLQjipHMRfXIhjMqBFtENIVTGEgpYai3u3o2w6guAoYMS3GmFmLIykNzU1DxOOxsXCakWhUJkSi89nsSVm8PTpoXRhI23t9dOrzKRh3XKro41+vzUDxu/HqU3j5hEw0Ixf9pJVEbRmcCKdTWlzAl9OsSKJAfqqJ0L7n8c3/FFF0mE0mrltYwrMbG/CHYlhNOu67dhKBUBR/MIrNbKBnKFFOHUAvwn2rK+mbW4jdauR3G45z05JyzRdCEvj9zjY+uvJR3O3nUJGwZhXwtSdOxesxx5oGuPHKMtyjRWKTQcfZ9uEJ1+kdCnC23R3P26clm7lpfjYpZpUDLUF+/tIxPnLdZM53exjxRZhakZ5QAwAotIaQNv4Qq6xdO2vqdSydOTNeLIcxDaRMlxlvMMpbu8d8tw839FOY5cBiNqCo2urF44vw2VunEorIGPQiZr3InEmZZKVaEUWBQCgaDxigDfC/f6uRL8xOxrrt10hWJ9R8Lt642NLt4d6rq5FEISGltGZhMUlmPaX5Lvr7vdy8pAyPP8LpliGcNiMzqjLZtL+VwmwHiqzy7KZGplemYzJow6eqwo+fO8JPPruIVbMLNM+O/yYt5QnG+NZv942jRreybFY+X39gNq09HlwOE4WZNvqGg2zar6XjVswu5N6rq/nDW2eIxhTyMmxaH864JZkAZDrNZH7AFq/jcTloXMY/DLKqySCYDCIWz3kGXv85smcAEHBMX4Glag6S2Y6qKIgmK+lrPsvI3leJDrajm3sXKXufRT63nxjAcDdJXSdQr/4yfS+NGoQt+yzPbjlH14BW6O2ty6amyMXJUY0kh9XA8CUan4IxAUb1jmw1Cwi3nYK25xGBJOAj8+/mheMjBMOTaT3tpXugh68VFSAZjNqAFAvTWnoL/Z4o/lCUkpDA7uNd8dSDPxTjV+uPc+3C4rgH+CdvmIJOEhNSJ9fOTMNpNWAz6vFHZG5aWsEr285xrGmAzBSLFhAUM197XSvk37EiQpLNyOp5RYiCQGe/j037W5k7OZs3drXQ3OmmqtA1agqUTCQms+NIFxajLh4wku0GrswNoD/wFPJQFzX5U+hcvJpz3Z5438KqeUUsrMth+9FOREHg6jl5FIzsAXlcI9rJdyhdXB//p8thwh/U9pfmOzlzkS8GwMHTfSyaPtZ819LtYcAboncwiMWkw2kz8q3f7o33mty6rGLCObr6fUSSipEAuWQer+5qT9j/2o7zfPHO6ew60oY/JLN8RhYl2WZGglEGWgaxGiRcVj2fvaUObzCKUS/ROeDnXKeHc51jjZ4V+cnsPt4dl0FJdhhBABH+24AB0DPac1FZ6CIaUzDqJbYf6WRZfR4LJmmptNZ+P9/4zZh67vYjnfzLJ+bxbw/OQxUErAYJvTiRhPBh43LQuIx/CPq9YX6z4SSNbW5uWJDLMt9rowEDQMVz8E10SWkMbXkCANFsJ/36z5J8xe2gxIiICoPn9iecUwn6MIYG6Jr9KSRBxR1y0DUwNhPdcaSLdSsqmVaRxvHmQbJSzCyZkZegPioKkJ1ijtNO9SnZ+E5uT7hObN+zrFvxacKHXydSU4yn6Er+sLEhntP+3O3TeOLN0wmy63evrqal2xufWWrF2bEX/pmNjXxpXR2HT3UQiirMLTZikuRRUUMBQYTntzTFB9uewQD/9dJxvvbAbKZWpBGLqWS6LMyelMkrW5uJxBRKc53csLiMrn5tFXPq/BCP3TeTDdvP8eymRsxGHTctKcM9LnDeM9+FtOVHyBc8xduOkRMN4Cu7J37MGzvP87X7Z1NfnYEkCVQbexh5aVPCMxKNZnwhBaNBorrIxcK6XP7rJU2S/GTzIIunJ3ZYg+Zm2DeoPTO9TuTu1dX84KlDDI6E0OtEbl1WQYbLgm+0Wc9wsWERMKMyA3PXQSKAENO81Mej3x3E09/LLZEXEfQ6wm81oyz/BF97JYQnECXJZuCRO6aT6zLHeyoKM2x89rapvPxeM3qdyPVXlDLiD3Pb8gqiMc0jvTDTgfGi3o0/B4fVgNWsTyjc335VBebR9JkkiXHRyAtQVNhysIM7lpbhclnp708UJvx74XLQuIy/GyRJQBAEPMEo333iQDxt4tDFiPa1IM9cR7/gwiQpOLv3ooTHUjaiyYoaizC8/Tmig51Yy2fiXPYA54M2+kJGHIYYGcPHcQcU/n2ztrJYe8VE1sj6rc2smFNAMCJz8twQ5XnJLJ2Zz65jXbgcJpbPKiCoM2CZfTeCKhM1TywiqpEQ0tB5Iu2n0Hn6ORqpTiiC+kPRhIABmjPdnMlZbDmgzXz1OjGh0BuJyqRLI9xYPAKqTMCUwYDORstAkBFfhNQkc7wgfgExWaF3yE8gFMOgEzEaJF5+b0y+oqnDTarThDy6iijJSWL3se54SicYjvHEG6f56JrJTC52YtSJZBr8qBfpRMndTRgKxr5fVoqVNKeRolEVW9kfRbS5UHxjqwfT3NuYUpLPD4tzSE+10dTuRqcTCUdlWnt8lOYmUZY3RnXVRP1y+e7j2kRg3pRs3th1Ps4EisYUnnzzNOtWVMY7vHcd6+aeq6t5fvNZ/MEok0tSuGFxMfoT+5HtLqw2G2sWlfLTcf4ZDquBrHQnkT1jE4Xw4TeYUrSGHScHGfFF+PGzR/jmA7MwjhpI6USBglGvcEEUyHBZeG5zY1zUUBDgC+v+svaEmKyy7XBnwraX32tmds2fp8b+AxYWE3A5aFzGhw5BAKO/m/C5A6hhP0NZi+MBA+B0d5iyRZ/hu+s78AbcAFw5eR63OMcGfdcVd9C3/j/ifgveI5swlszg9ZF57G/UitbLp9ex0JnHbcv9gEC6y0xpjoO6igwkUaCt14vDamDn0W66B/1ULizh6XcaSEs2s6Q+H48/wlNvneHahcV8fyuo6PjEUguleiNqdOx+jUW1hHtatH+kFnOwJYBOEqgqcCLLygS5ctCUUW1m7XXTSQJ3r6pKWOF8eqkL9Y3vMnLB/EkQSbv6i3zyd51xefFP3VzHnuNdZKRY6RsKsOdEDzpJpKF1GEEgzhAaj0Nn+nj07nqKcpKYXpHKN3+zL2F/XpqF+uQhprm2o0aC2J3L8BdMItR6Yuz3M5gpzEvniml6CrMc1JamYdFLcTrp4W4BoeoBCoUudCE3PmcJzx8TuTVHwaKXMBl0uL0hVs4pRCdpPuH7T/fywHU1dA0EkGWFrBQrLV0j3LKsnGhMobIgmfcOJfY+AETG+YQ3dbi5ZkEx3/robEIRhRS7Ab0oIM25C9OsED7FwK5XTnH36mo6+rxYTXrsVgOtnUNMkvSospbmkuwuKjPSyc5KoX84wPYjXXiC0XjzXr83nDDJuXtVFSlJZuZOziYmK6gqPLu5kUfXTcfwPkd1/5/QmQqFY9hGGwpXzilk36kxGq4owPza7AmaV39vXA4al/GhQBDAIHuJ+WKYIiP0BxSiyTXEQkEcZl0ClVAWDDy5y403MPYibT05zJWzpjF85XeRVRVntGfMTW8U4eYDzKlfzP5G7XrVZZk0d/vj9pxWs57F9fk8/topIqMD0bwp2by9RyvAnu8eoaIgmWNNA3HZDdBopSlOC9GYTMCQCss+h+7wC8hDHQiFM2jPWkBG324A1MEWVkxZSpFJxdayGVUy4sm8Y0KxdcmMPJbWpjAlSyQlLQVxqIWz2Wb6hwPkpNsols8RGP/9VAXl+FvUFC7gxHk34ajM7zac5IbFpfzutZOjPRtT0EsCy2flE5NVHLbEQj9AYbaDAXeQ5zY1MrMqlZx0e9xREOBjC6wEXvkeF9QfBzvO4LrqAcKdjfEVh2XhnRjSMrl3ZSaKokxIwURjCr94uweTQY/ZmMWwdxizUcftAoiSiDcQ5ok3ztA9OLZyzEmzUVnoYsfhTvzhKFfPLWJScQonW4a0xjVjWgL76gIyUizodSKSKLBidgH5GVaSTHoYrfsqqkqPO8ygJ0yyXZMwefz1U6QlmwmEYviDUR5amY2qaIFddGRwrvRWnt/QwIgvQm66jQeuq8Fm0iYsOp3Iu4c6EiY5STatdnXBRMls1LFuZaW2onufQSPdaY6LF15AQaadJOuYYVZ+moWvPzCbzQfa0UkCV07PI+t90Hg/bFwOGpfxgcMg+wl3n6V1JIIkCjjziuHsDnTH30KHiuTM4v4lD9LcMYJeL5KXaeO1ixq9blpSxo9fPBUfNL51TRJJkh5L5Sz0SWmE2s8Q7jlPVoaTm5cmkeEyE44qCbLfe050c9eqqrhe1ZnWYVKSuuJWrMebBvjiuun0DgXoHQogCJoBUWefl/L8ZPQ6EVWFh5/uZF7VKgrK9Ow7F6BxdzvfW1WEGU3xdk6ah+HXf8qFkGcbbOexux7lmXdbGfaEmD0pi7k1aexuGMYbVFiTqxB49yfclFbItStnYZR8iJ4BLoYQ9lKZ7yQ9xc6ZliG6BvyM+CLEZJXWHi8Hz/RRmOXgwGmtv2BWTSYzqzPZd0qzjTUbdVw1qyA+WI/4Iiypz6O1x0M4Ims6Ur6zBEmMAr7j23Dd/BjBvi6k5EwEZy6xWOLsVhAgEFEQBG2w00kCoYgcb6R7+LY6Wnt89A4FSHeZuWZhEb9af4L0ZAuBUJTls/L5tycPxAPQmZZhPnf7NOZUpTOvJgNfKMa9V9fw4+fG1IOvX1RCUZadr943C0EEl80Y9yC/gN2n+/jNq2NmSLcuK6ckJyme0spwmakozcFqug01EsSdv5Af/vJwnAjQ0edjw/ZzTCtNHf2iTPDWEASBvSfHrHmD4Rib97cxszJ9wm/4p5Bs1fPlu+v52UvH6B8OUp7v5KPXTUoobAsI5KdaeGB1FcA/fIVxAZeDxmV8oBAEGB5289vdCoebfehE+Mkd3ajH34wfI7u7EY+8wkM33sG+hkEqC5Jp7/XFJRTsFj3BcCxhlnnabWHVVffj2fca/lO7MBfXknrdw3x/fQdtfX5uXFzKqYsYObKi0tnvJyXJFM+NH2ro57FbK2jvMpFijJLe8DyfuvpqTnbHEASBQ2f6uHp+EXq9hKJCusuCoqhsPznEhXK402bElpWPVDwdY9lMAscS1Z4VTx85XVu4b+VKQpEYFqNA20CIQU+IxjY3OlFg/uLPoH/7e+g6zyADhivvnPAs9VOuovVokPNdI9SWpbF0Zj5dA1oAMBt15Kbb+MO4Qup3H9/Pt+6rY2VxmGBMIMMSY0QdZsSozU4b20fYd6qH668oBVUlyW5ACE9kMaE3ErLlctadRJreTIqoS5ChD8UUdhzr1volJIH7rq7isftm8eKWs/S7g9y7upr+4RCPv36KaExBJ4msW1HJV+6pZ8fRbjJdFgbcwQkrlld3nKO6YBqqrGI16ohEY9x/bU08PXeuy41ZL+H8Ex3O7kCUxy/ypHhuUyPf+OgcOno1b4zCTDtmkw61chmCINAzjmp8AZ39fob9EdLsRlRFpb4qI8FfpM89UVyypctDOKpguURx/lJQVShMt/LNB2YTisjYTDouocYC/M8JFhdwOWhcxgcKvaSw/YyXw81uAERJQufrI3zRcbGOkzSbOtl+eJCeAT9L6vPpHQzQ1uslJcnEsDexGJtpVxl6+zfxPHSw+TDRSIS81GW09flp6fHEC8suh4mYrIz2BSQOCMUZFpJOvYSteYzKaC6ey4Ydg7gcJtYsKuGZjY109mvpqgeuq2HxjDzeHS1gS6LA7VdVMIiJLfJyitwWZksTBzFR0tPvjTHsDZGfYWfP8Tb2j6bjWro9HM9x8PD8e1B2/A7BYMaDDeeaz+Pb87LG+pl+DU+d1McD6eb97cyszkAZHWkrC5M51NA/4brbD3eyxvs80SFNtC+zuJ78ijlMWSVhjh3CNWsWP39ZG1hLcpKoWVaNoDOMK34LRCuXc+j0AL965QSiKPD5O6ZTmZs0aokAJ88Psut4F/ddU0NMVnh2UxP3XVPNp2+sJaYojPij/PCZw/HUS0xW+MNbZ3j07hm8d6gDp83IvNrsCfdu0ksIaMZGiqJSXZBM91CQzn4/NrOOpTPy4sXpS8Efik0IAIqqSb/PqkxL2H5BD+qCf/h4WM16jHqJruEgVpOe6RXpdPb72HpYoxinJU1MEU2rTMf0PgPG+HswSgJG86WHYUGAPk+Ys+1uREGgLM9Jqt3wvhlaHxYuB43L+EARVUT2No1RASNRGbMzhYtb18QZN2PXpXHFdAuluU4a24a5/opSAqEoRoOEJIpsOzxWCE3BHQ8YFxBrP8mM+pXsPAVHGvv54p3TmVmTSWe/D51OIs1pJjXJyDt7NVFDi0nH7bOTkDePUXWl5Cw8xhRWzLZhNkq0dXviAQPg96+d4rO3TWNKSQojvghpyWYa24fxBiJsPdzJSaeZBdevJNwyzlVO0tFvr+D7Tx3UvqsocO/V1RxtGoinypo7PZxbUItS/whhVSIcSibdYkat/ajG/RckNh1LdKrbd6qXW5aWc+B0H75AlBTHROXSFKuIMjD2tGNtRzHVX4cdkZA+D2vUxJ0rqzh1fpD8TDvvtQjUzPknUjwNiLEgntTJvH1CZPYUI4/cMZ1jzf38/KVj/MvH52DRS6OdyVBflclvN5zUPMQXl2Iy6th9spdITCYv3TaBDBCTlXiaye0Lk+o0Y9RLcWteQdDST+NHREkQyE2xkJti4f3A5TDitBsT6g9Wk47UP6PwmpZkZM3CYtZv0+RYRAE+ct0kfvz8Ec51etDrRO69uppblpSztD4fURRw2fTcf20NT75xmkhMoTgniduXVfCXhYz/Hj3uEF/91Z548L1YXfcfhctB4zI+EAiCgEFSiAkiVQXOuH+BThIQ9AasVXPj1qVS5ULWd2Wy6eAYQ2fl3AI8/jDBcIxASMSo10T+XtnWjCwrWB2OCdcUjRY8YW19kZViQRcL8vvXT8XHHaNe4hv31vHAdZMIRzS706z0GP6ieug5DVlVDJVcRUA1k+wQMOolinOcbNhxLi7pEZNVeoeDvLX7PE67idZuDyaDjmsXFLNqXhFOm5GGsJGS676M3LQH9CYMpTP5yZuD5KbbsFv0NHWM8M7eNmZNymL7kTGaZSCs4JadhCIyYiTCsaaBODng3qurJ3xfg06kpjiFj4/6XRv0EqIokJ/lQADOtg1TmxJAPjUm027ILEI+soFQw14ESU/e/Pt4ep+A1WJg9/FurltYwrdebsLlyESvE+kdGuDjayfzzr5WmjpGmFaRxqdvriMYjmHRS0iS5pz4/BatKTEYht+9dooHb6zlmXfOEIzIfPOjszEbdQkaSQadGC8uA6zf2sQX1k2noW2YQDjGzOpMclx/W5HXKQZ4ZGUqP98yTOdAgEyXmY9fmUKqFCQ0QbtXgyQIrJpdwIzKDPzhGE6bgec3N8Yb+aIxhV+uP8F3P54oLz5/UiZTilMIRWSSbUb+zALor4Ikibyzry2hUB6KyOw50cOa+UUf7MX+QnyoQeMnP/kJb76p5bIXLVrEF77wBXbt2sV3v/tdwuEwK1eu5DOf+QwAp0+f5itf+Qp+v58ZM2bwjW98A51OR1dXF5///OcZHBykqKiI73//+1it1g/zti/jL4QUGSHSvI/Q2V2IGeUsm34NhxoH6BsOal3Ogx3IAQ/JC29FVWS67dVsemqs8C0IkJNq5+WtTfHaw+LpuVw1u4DibAeyohKwiLhK6wk3ja0SrIvu4r2tWt1jweQMXt3bnbB0D0dljjT0cbIjGPeAvmN5OUU1t2GZFMEjSxgMJn74+P74CmBKaSqfXzeDLQfbicYUZk3KxOuP0DsUpHcoiCQKfOnuqWw73Mmekz0UZzvITi3kY7/rIDe9kmhMIbS3g+/dmkusYTs6fz++ybN5p92AedwMcWZNBifPD7LjSJcWIJeVxwMGaAXZqgInp8f1f9ywuJRfrT8WN5V6+NY6AqEYz4zKfi+enotRN1YHEk1WbNXzGdz4OwBUOYqw47fcOONhfvy2lvbacqCdf7plKi+/10QoEuMj103i1PmheC/H9iNdDHvDfOqGKdo5ENhxZMyv4gION/aRmmyhvdfL1oOdPHhjLT994SjBcAyTQeLja6ewf5yKa1FWEv3uIAcb+hAAi1FHij0nLqX+l0EgpiqYgiM43v0+X6xaTNCei8nfhrD99ygpn4fksaAhiuANySiKit2sRydCtstMWpqds61DHDgzMe3XPxIc9eDQoCoqdpMO+1+i6yRodRdZUUm2Gv7blcnFarUAg54QB87209B2lukVaRRm2Cd0hYdjCv0jIQx6iRS78U/WSv5afGhBY9euXezYsYOXX34ZQRB44IEHeO211/j+97/Pk08+SVZWFh/72MfYunUrixYt4vOf/zzf/va3qaur48tf/jLPPfcct99+O9/4xje4/fbbWb16NT/96U/52c9+xuc///kP67Yv4y+ETpTx73kewduHOb+GqLsP4+Z/5Zu3fBJlpAcECcEsEWp9Ks79D8x+MOEcU8vT2XW8i8GREC6HiWA4hsNq4PHXT3N6tLidnWblkVWrSc0pQY1FIb2ENjWbu1cbGPKEyM2wc/DsxKKuPyrEPbLzMuykp1g53xvA44tQmpfEC++eiQcM0PSa5kzOYtijqbE+u7GRh2+Zyp0rqwhHZbJSLLz4bhPHmi6wnQTe3qtReC/Qdh9ZlUH09e+hRkJEAEPrMW5YcBfHdDnMr80mO9VGYbadnsEAuWk2nDbjhCJ+njXCyuohWquy6fGqFOcksb3BFw8YLoeJxvYRjpwdG+DePdhBQWYlufM+ixQLkZmRzPDb/4GUN5loWjn6gbPIrUewC2OBpa3HS166lX++ZwaKAme7PBOazk40DzLkDZOZZEJVNdrrxR7hGS4rp0YNg4wmHS9vPcuquYVIkuaM98rWZtatrKKhdZi0ZDNXTs+jtcfL1PJ0YrKC2aTnVNsQM0pT33fOXhCgcyjIS+810TMY4KqZuUyuWYl07FUuDO8qAqJpzOAqKivsPNrDs5vPEo0pLJmRy3Xzi7EYNNE/i1Ei02WhZyix2J1s/9tMjCIxhXcOtPPKtnPIisrM6gzuuKoC+yUEFEErfi+fmT/u70xDbVkKfUNBkm1G9p3qZdgTYlZlRtzyddgf5dWd58hOtRGJyoDKkul5E1hmfws+tKCRlpbGo48+isGg8Y5LSkpoaWmhoKCAvLw8AK655hreeustSktLCYVC1NXVAbB27Vp+/OMfc9NNN7F//35++tOfxrevW7fuctD4kCEImhuZiJDgD38pKL5BrJn5hOUQnsMb0afmkrLwVoY2/T/tZZWj9My4G2P1UtRTmtREquQnw2WJm/8UZNlpaevnO9clY3M3oVpTOIo+HjAAuvr9bD0bZn52FsPBKMm6bI41eeNidEa9xEevraKhzZ3wPWrL0nhz72EA7lhRwY4jXewYddS7bmFxQn/GBcRiCqvnFoKgifk9t6kRs0nHwTN93LK0POFFTneZ6R0KYDPrmV6VQTQmky32T+gpiR16FWFKIec6NaMdWVG4bXklz2xsxKATWbOoNOH4IosXXccJUvJdGBx6bCYpQSSwKNtBQ+vEINnYNsKm3hAdfT4eXG4nf+7Hef5YjBOnvFTnLeTmxVeSnZpOZUE3GS4LK2YXkGIzoqoqkgSXEmY16iV0Ok0kUVUUFtRpqrHjPcIrCpy8uk3rRreb9TR3eGjuSKSqSiJ85a4ZSJLAsXNDrN/WjH9UR0oSBT62dgrqaCH8/aDfG+Hrvx7zxvjdGw3ctKiWRc79yG6t4TNp/s1ELWnxU57v9fHEm2OMs4372slwWVk6TdO8Mkgin7xhCt994kD8+920pCxhlfHX4FyPl5fGdevvO9VLQZaDVTPz4kFSUaF3JEj3QAC7RU9Blp1P31TLC+82oRMFbl9RQXOHh/Vbm4nJCrnpNsrzk/GEogQjClajRNegphCwYfs5RBGuWVBCvztEXur7qwu9H3xoQaOsrCz+/y0tLbz55pusW7eOtLQxFkN6ejq9vb309fUlbE9LS6O3t5fh4WFsNhs6nS5h+1+ClJSJNpp/Cmlpl857/k/Eh3WvQ54gu4918+buFrJSraxdXEZVoeuSx6qyjDdmwHv2IOFOLUUS6W6mf9879M74FG8d6sdqEJijONjjqWXmrFr0RJHt+VwzX+bo2QHOdrixGHXcPzWG/K5m1WpIzeW8KXXC9U60+egYMnLwzCBm4zHWraiM587DUZltx3p56OZa3tzViskgsnx2Ae2DY/4JoiDEAwZoWkxTK9LZfbw74TquJFPcL8KVZKKmJIX0ZAsluU6Ksh0YDVK80OsNRFk5p5DWHi/bD3di0IvYKg0ktqQBgkBOmpWugZb4puTRdFUkpjDiDzN7ciZ7jo/2WDhT2dy/hFc2aCsJSRzgvmtqeHZTIx5/hK5+P9XFKRNsZktynRwdDWpeycl/vNsanzUfanbTNmjie5+s5bsPFiMKYnwVFgxHkWWVlCQzk0tSON485h9x09IyPL4Im/a1M60inTd3t7D2ilJkVRviZVllz/EectJstPV60evEuJDfuK+P2aQnLVV7H9v72uIBAzR69K6jncyfnIX9Ek2Kl8LxlrYJ3hgbdnew/JNfxuY5h2RPwZBRgGQcGzDXX+RsB7D1cCdXzy8GtPcqLc3Ojz6ziN6hAHargdx0W1zR9q/FK+MUfS9g17FublhchnWUQvzewXZ+8PSh+P668jQ+e/s05kzJRgCOnh3ghdFaEkDPoB+DTuT3b5zmZPMgc2uzKMx0sHBqLgWZdiwmPSlJJkLR2Ac6XnzohfCzZ8/ysY99jC984QtIkkRLS0t8n0bhE1AUBUEQJmy/8N/xuPjf/x0GB33vyz0rLc3+DxMA+0vxYd2rIMDGg508PZojb+3xcuhMH9/5+BxSxr3IpnAfwZNbiXafxVI1D0N6fjxoIOnoyF/Jvz0zNpvbcWqQR26fRme/H1lRyJcMPLf5GFkpVmZWZ2AhCAeejx8fc/cxeaqO98YkgwCoKHCxZ3SAD4ZjrN/azMKpOfEO76Nn+1k8I5dbl5cjKyqvbG3iyhn5fP6WSQTDUQQERAGmVqRTmOWgpdtDcXYSoXCMw439WEw6rp5fjCjAk2+cJqaoLKjNZuHUHP71iQNEYgr5mTZuW1bB70f7AVq7PXjK0+IKsACnvRkUGy0o4bEUh2nmWvw2JzcvKUenE9h7vJvUcd29ggB3X1XJmgUlyLJCSInyyr4D8f2yovLc5kZWzy3ijxsb8PjDLKzL4fT5wXjvRlWhi6JsB1fP0wql2WnWCWmWAXeIli4v4ug7oQDN3R6e23SWUETmo9fVsHZxGfXVmbh9YfLSbYQiMdZva2Z2TRatvR6SbIZ4N/QF3LGiktrSVNz+MMXZSVy7sIT1W8dm1kvq84nFFPr7vZqseXCijIYnECUUjhIKTnTKuxTES3RfW0x6ZL2NYHrt6EllLvicAGS5JtZDi7LsBP1hrGZ9/L3SA7mjhXnvSJC/9W3LTZs4ea0ocBIKhIlFongCUX71yomE/Uca+znbOkx+hg0B8F70XK6ckcdzm8/SPfr7W4x6kh1mfvDUQTJTLATDMQQEHr5tKgMD3ved9hNF4c9Otj/UoHHw4EEeeughvvzlL7N69Wr27dtHf/9YDra/v5/09HQyMzMTtg8MDJCeno7L5cLr9SLLMpIkxY+/jA8HwajC+m3NCdsiMYXOfj+pSWYEAfShIYZe+BdkvxuAcGcj1rrlpKz6OPJIPyTn8l9bEweqpfV5vLm7JT57NRkk7lxZxW82nKShbZh1CzPjPhkAaixCUfAkV06bxJZDWpCoLUvFZJASHOP63UGSxgWz6ZXpiMCL7zZh1EvMnpzNiC/C60c7Odfl4dE7Z/DxtVN492AH67c2U1GQTJLdyKxJmZTnJxOOyngDYXYe98XrHFsPd5KWbKa+OoOdx7qpLkyhsW2Ij6+djDcQJS/DzkvjHPsAfvHuIN+88RFMnQcRfX34smawszeJtjPN7DnRgyQKPHDdJAC+fv8sTEZdvGCZ7tC+z+lziSsI0Dq660qdFOXUk2Qz0N0foK48jYXTchEF4mq2M6rSCYZjqGjB6OLBQjeu2Nw5EOC7j48Fp1Otw2zYfo6qQhfZqVYCoRgOm4HMUZG+nDQrNy+poLVrhNZeLbWXmmRCAP7zuSPx8zxyx3Q+d/s0hr0hkmxGvP4oMVlh7xktONeVp/HazkQVgBWzCxBU9X0mpyA/w0Zaspn+cU2gd62sxKj702qz1YXJ5GXYaB+9d6tZz9Xziv7bNOz7xYXJ7gVIo1XoinwnlQXJnBltEnQlmbhqdgFv7W+noW2Y+qpMZk/K4p29iSsSty9MR78PQRDISrFQUZAcbzRMS7bQPdAWP7Ykx8GBUz2jisoebGY9LoeJ3kE/+e+Ttvx+8KEFje7ubh588EH+4z/+gzlz5gBQW1vL+fPnaW1tJTc3l9dee40bbriBnJwcjEYjBw8eZPr06bzyyissXLgQvV7PjBkzeOONN7jmmmtYv349Cxcu/LBu+f88REHAZNDFTXwA5tdmMSXZR3j/VohFUfOq4gHjAvxHN2HIfRD3zhfRFdYhiYsS9qc5LfFeCdCog9uPdDK9Ip39p3vZdHKERbNWEtjzYvwY6fTb3HXLPJaXCSiKiivZzo/fTGTtZKdaSXYYSXOaKcyyM3tSNj/84+H4/iNn+/nKvTOZHsmgvjoTg0HimVca44Hn1Pkh+oYD3LWqmmFPmGSHkVBI4s1diS/u4YZ+Jo/KSiTZjGSmWtl5tJvTLUPMrM7AOU4vCDTv5i3noLGtBJViWg560OlCrJitWTvJispvN5zkmx+d8yctQdOT9IiCNqi5HCa6BvxkucykmBXyzFH0Diu/e7WFMxcVpE0GiduWlCLLBsIxlSUz8tm0f+zZL56WS1qSlp8XRYED4xhbAEa9yB1XVbL9aCdbDnZwz+pq3jvYEWd2efwR/vXJ/Xz1gVkMuUOIooDJqONffj/GaqssTObQmT62HBzzslgyI499p3ri9aD66gw+v246z21uJBSWuW5hMVOKXX9R45rdqOMrd9dzpnWYQU+ImkIXOamWP3sOu0nHo+tm0DngJyYrZKdaSTLr/uaGuVBMpqXHx7muEQoyHRRm2hnyhuka8GMySOSl2fnc7VMZ9kZQVK3j/XtPHqBzNNAfbuhnYV0OVYWueC1PJwmowO9fP4UgCCysy+GmJeU0d7g1T5Q8J6IoxDMpgiBQmudMkNKxmvV8+qZaRBGUD6ix/EMLGr/5zW8Ih8N873vfi2+79dZb+d73vsenP/1pwuEwixYtYsWKFQB8//vf57HHHsPn81FTU8Ndd90FwNe+9jUeffRRfv7zn5OVlcUPf/jDD+uW/89CBgLhGFajjnUrKvnxuBnjfbOt+F781pgiaE4lYnI2wYoVBFQjSdE+pNNvE7Pn0Fb/GRwGhWuKXZw4rw1mOkkgGIlNuGZ+ssgd0wRik/RIVhtYC1AVA4bmbSi2VOzTVuJ99fuYR+W2w8DDqz/HQ88GNW683cg1C4p5e3crk0tTsVv0vLMvcbBXVThwupeyPCeyrOILRhJWKqCla/Q6kfPdHrJTrDhsiQEAoCDLweEGbdC0W/RsPdwZL0rvPtHN/ddO4mBDX1yg0GLSkeo088a4/Pm00tQExlE0phCOxIBL5+8tBomv3j2VI+c8dA/6WTA1hyl5ZtQjr9A/qvDrciye8Llkhyk+OBh1AivnFFCcm8TQKDOtPM+JeZRJo6qaVPh4ZKfa+M9nx/SeLKMEgPEIRWR6BgLUFWu1rkPNgwm1hanl6TyzMdHPffOBdm6/qiIeNPaf6mVhXQ6P3VWPqqroJfGvmu07TDpmVqRNmOH/OZj1IqVZYzn+vzVgKMCrO1p4azRNarfo+ezt0/mvl44hKwrBsMysmkyW1Ofxr6MF9o+umRwPGBew/Wgnn1hbS2PbMFmpVm5ZVsHm/a3ceGUZqqppqRVk2Tl4uofG9hG+fE89y2cV8Nao74YoCGzc15ZwTn8wyuBICDVnYp/TX4sPLWg89thjPPbYY5fc9+qrr07YVllZyQsvvDBhe05ODk8++eQHfn+XoaFnJMQf3jxNY7ubykIXd1xVydcemMmhMwPUlqUSO/82qhxFn5qLIErEknI4VnIPv3u7i5jsJ9nu4nNrvkwgYuAHb2vpp7uWOHjsrjo2HerFaJAozXUmXHNyoYM1rjO414/pUZmvuJeWpBmItZNRBR25ITe6QGKKRj7xNh+77m66h8PIsoJBJ9I94Kel28PkklT0l6AVWk16/MEYXQM+ZtVkTkjXCALYzHomFadgNkrkpJrjiqUqoJdEKgqcHGsaLUhLYgKLSVXhla3NPLJuOg2tw0iiQLrLgtcfwaATicQUyvOTqSlO4ffjZoAmg0Tynyn4Dssmfv36UTpGB5a9J3u4fn4e12Xm4px3A4IosdJkZN/pMbc/i0lHXVkqEVkhKitYDBLJFj2zKtIIRRWMejGhN0BVVerKUnl569jqcmAklNCUp6pMaNS7cP8X4HKYEjqxVVW95EB8cW3RG4igQlwa5VIQBIjIqtYk+mcG9w8qvfSnEJYV+twhREEg3WlCL2pUblWFIV84TrsGuO2qCg439rJiTiEdfT5sZh02iwG3L0wgFCMmKwkkgPGQFYXrryglJclI94APl8PMS+82IYoCS2bkEYupOGwmYISYrDA0EtTo4BEZV5J5lGabiGhMuWSa8q/F5Y7w/8PwhmX+/Q8H8QaiJDtMnGkZ4kd/PMR3Pzqdmtl20Kt4hp2MLHqEc34LigoVYQO/emdM3mPYG+bnb7Vz7dwxJ7YnNrfy75/M4tqZaUgoCAaFB1aX89SmcwTDMW6daSe6aSxgCAYzjdEsfvTSmGxGpsvMI/W3ot//VHybisiWg+0cP+cGtPTUVbPzeXX7eZo73Tx4Yy0nzw3GXw69TiTZbuLXr2oFxvNdHlbNLeL1cbn0pfX5xGSF327Q5MYfXp3NW7vaGBhtrDIbdXz1tnLWzC/CE5LJcFmwmnRx61bQaisdvT7Nb1xRCUdl7r26musXlyKgrWZ0kojVrMcfjGI163n4ljoMepGOwQAmg4TLbmC8LVMgGI0HjAvYsLuDeauM6He+CIJI/vyb+Ppd9Rw8H9DUhO1G+kdC/ODpQwx5wiydmcfq2YVYjdKfbJpLtRv49sfm0NHvR5YVbJZEHa23drdww+LSBGHE8nwn+akG9F0HEQSJQkcO18wvYsQXYcQfoaLARVaqNV6gBUhzmhOk7wVBy8l/4zd7CUVl1iwqYUZ5WoK2lD8ss/VoJ9sOd1KY5WDtFaVkJBn/7tpLnlCMHz93hHOjarf1Velcv6iUt/e10dXv44ppuayeW4TNokdRoTDTgaLAr8cVth1WA5+4YQrJNgP9IyEyXBayUqwJcvFzJmcRjsq8sOUshVkOFtblxE27ZEXlrT2tfHTNJM53aZOpEV+E2ZOy+NmLx4jJCvtO2Vk5t5DHXz8dP6dO0gykPshndjlo/B+ETlQRUOgZ8jOzJoOsVBvdA35y0mxUZUromncw2LAHfVI6timLSR4OUH9+PYIcodl1/4Tzdfb7GfSNzXCuv6KE5987z75TvYiiwLVz81lsOMZ3lqUg65PIoJeETHz5FTy5M7HnoGcoSKe1Gmn2I8gqZIvDRMxJnHxpjAraNeBn1qQsqopSsJr0mE0SX7lnJntO9GAySkwpTeVn41zbGlqH4z4U/e4QRr1Ea7eHdLPMD+8uRhIFDvUQDxigsbTePjpMTFbZcayHSSUp3La8gl+Pk9+eMzmLwZFAfDZeWZDMkCfEK6N6RgD5GXa+8ZE5jPhCJNtMCAJ84zd70et1+IMR5k3JYfWcAgyjhdPoJZRNFUVFsY1SkVUF9/ZnybqukDd29qGoKrctr+AHTx2MDxBv7W5FEgVuXFj8JwcNX0jmNxtOcnK0Me9zt09lSmkqNoueDJeVhtYhctKtfPHOGbT2eEhJMlOWZUF59rMMhbUCtGRNYtE1j/Ifb7oJRWT63X5uWVrO9iOdNLQOU5bvZNXcIs53jcRXewun5vD466foHA0sv3n1JMYbplBfrjX3qQI8/15TvNGwbzjI8eZB/uXjc0n6EwJ/fwkUtBy/Xpo4AxdEgX5PmJ7BAA6rgYGRYDxgAFQVpfDN3+6Ny8CfbXdz89JyjHqRYEShbzgYTxldgMcfYWgkFDdfaunxcMX0XNzeMB19PsrznfiDUQw6bQVntxgSOugv4FjTAEk2I4MjIewWPa/vPM+NV5aiqFqvS7rTzMeun8zm/e0k2w2smFNE5BLp4b8Fl4PG/yEIAhi9bfj2b0D2DJB+xcMMeyK8vWcsbbK4LpO1HEfpbCTS2YgxuxRl0++5MISZayeeNz3ZjNkkcdOSMvzBKLGYEnccUxSV9TtaKbpxNn29w/R5Y1w3LR/Jlozs00KHYrSNKtImotcPT7+t5dOtJh33rM5CUcYa64pzkugfDtI14NeKhirkplspyLITiyl4A+EE8TrQahyZLgvPbWqkJCeJ+1aX8+6hDt48NEBVgROn3URuuo3Zk7IQBYHDjX209weYVpFOsl0LOi6HiUfWTWdgOIjdoifJbsAXiJHsMCGKIhUFyfzL7xId8hZNy+WHTx+ka8BPfVU6Oel2ls8upKnDTZJVE9rrGgpQmKZRQi8UwMfXYOZMzkaSE79PLOgnHJURBK3WcPEAeLJ5gMXT8xj2aDUNl01/wf4cgNNtw/GAATAwEmbpzHzWb21i38leZlSm4w/GKM5yUJWXhCCoRLf/Hn94jLEk+0fQdR3jM7esIBqTMRkkfKEYRoNEfVUGTruRdKeZQDDKkcZ+0pMtoGrpqfF4c3cL08tSNXppIJag0wVaAO8e8JOUN9GC909BEAX6PGG6B/wY9RLZaRb8gRg9wwFkWSUt2Uym08SgL8LpjhFNtn0kxL//4UBcMXfZzHwWT89l59Eu1NHfJnSRIOOG7ee4YXEpz21qZMWcAkKXGKhlRSU6ek6jXuLJN0+TbDeR4bLw5u4WDHqJunKNHerxh6kqciU0uIJmXnWmZfS9UeF0yzCnR/9tNEjcuqyCZzc2UFOcgicQ5UfPHOJLd9e/7+f1fnA5aPwfgtHfRf+z3wRZ+4P2BiJxw54LePdID0uvno2t7TC65CzC3Yl0UiXoZfW8It7YdR5V1fLon76pjhffa+Lo2X7y0u1cs6BogtrooWYPDW0eegYDbDs1zE/ueojY/heIdDag9/eycnY1L20dSxtJooAkjktVhGKcPD9MYaadlh4vLoeJG68s4/+NmvSIAty4pJy2Hh/Pb25EJ4msuaKUuVOy2HVsrHnv6nlFOB1G7lxZSVm2hf3HO1i/W9vfPRhgSX0+Br2kCSUqKnOnZDGzKpOuQR8zqjLoGwrQ0ecjHJMxG3UMe8McbRrgjmXllOY4EASBSEzhzlVVNHW4CYZiTClN43TLWD9FYVYSOp3IE2+MpRFsZj0P3zo1/u9oTObq+UV09Pno6PNRXeQCVUVwJ9JUo+ZkwIOqaoF1PHLTrdy8tIJDjf3EYgp6nZe8NBsVeUmoioooCpzrTKwbZaZY+NEzh+PNi3tO9jDsC/PgDVNQFBW9qBIbShzMAWLDPSioRGQFgyJiNUhUZDuQJBGXy8rr25tpaB9hyigLbf3WJq5dUMKTb449g9QkM+JoQVuSNAHJiwdno0EEYVRAXVUJywpuX4RoTMFhNUxgQnUMBvjXJw7gG60h1FdnUJyTxLMbtb6aZLuRh2+dyr/94SD+YJQHrq3h5a3NJNtNVBa66HcH2LivjS/dU4/LYUIQBC7VKqbXiaSNMuH2HO9hSX0eL46jYuskgfxMO+tWVhKLKZTlOrnxyjJefLeJIU8Is1HHDYtLicZkblmq9RklO4w4bUbcPu09ukD9ritPIxiOkeIwJbj/1VdnsOVAO6GInEBeaO3xkpn0t3W0j8floPF/CLG+8xjS8rCUzgCgT7h0ITamjr4VcgxRl8iusUf6aWzTc9vyCmKySobLzBNvno4Xh9t6vfxq/QnWLi7lmY1jDW9pyZa421k4IvPPL/fyz3d9isHuAfyqkSLJwG3L9Gzc30ZqkpkVcwp5+qIGss5+Hw/eXIfXH8FpM/Cd3++Pp4QUVTPc+dRNtdy0RHvpNBXXYkIRmaGRIDOqMvGHY0SHZIa8EUoyTLxzeKw/KBiO4Q3GElhPO450kZ9hJzXJxLsHOlg8PZeyfCf//oeDBEJasLpzVRWdA35eeq8Zk0Hi+iuKKcpKIhKRcfvCuJJM9A6NzcxNRh1v7k4c/H3BKEOeEMUZWlOV02Zk2+EumjvcpLssvHugnZuWlmPyejWHQFFCnXEzflM6//KJXERRE/3blu2geTSVcteqKtp6fTy3sYGYrCKJArcsKyfdZcZp1mueFYWuOOsHIBiKTZA1b2gd1gqsBomYKmCZsoRw19mEYyiYyhd+uoMRX4S5k7O5ZUkpNqMOWVZwe0PIKjR3unn3YDuiKLBsZj4OqyFui6uTBK5bWBwvaNuMErctr0igjy6pzyMYkfnBs0fQSwJXzy/GoBcZcIcIR2WCEZlIzEDqKMEghsALW87GAwZorK36qox4YXjYG2bLwQ5Wzs7nhXebyUq1MW9KNtGYwpGz/WSnWnngukkEgrG4k+OFleH4SdGFojdovRW9QwHuv6aGzQfaSbIZmFmdidkokZ9hR1VUzCaJEV+Iz90+DUXVtKZ6Bv1sP9KJIAgMjYSYXJrKp26qpaXHE/fTOH1+iKffaSAaUyjMsvPIHdP53hP7UVWwmfTEZIWr5xdhM+uRRJFTLYOX9Kz/W3A5aPz/CJIaRefrQhnpRbAkoSbloRosGKMjCKJIzJGCuXQ6gsGEqDeS4dCTl26Ni+ABlOY4cI5odMmYpx9DegFIuvjqxHbiZW664ov81yunGfKE+Mh1kxLYRKA1BI5fJZTmJhGTlYRZYzgi4w+rbDntJ82pUpitZ05VMt5glBFfGAF1gj/0vNpsnnj9NE0dbuqrM7hqdgFPvz1G7Vw9r4jjTYNsP9KBJIksrc/HatYzrVxjD+klgW2HO/jE2lrOdrhJdlpJthnizCGLSc+pc4NcjL0nevjKvfVMLkpBUVW++qu98c8oKjz++mnWrajkxOhnC7OTkGUFi0mP2aijo9fLFdNy6OjzEgjFCEeixGITiwyCOq64PhwkEIqyeEYe0ZhCTXEKb+w8T9pVVyIYSggKJsKmVPRDURpa+5AkkarCZD692EbnsJmgLGLSSzy3sTGeZpEVlec2naUiP5l0UwxV1FGam8TKOQW8tacVVQXdJcSnTAYp3nCnqhDJqMEy9xaCBzcgSDrMs27kTCgdt1dLHe481oXRIHHnsvI4k+poUz9to3L5iqLy9p5Wam5P4dM31hKVVfIzbaQ5jHGNKFWF2dUZlOU6GfFHMBklZEWhtdtHVaELURDwBqKEIzLeQCT+92UwiKTYDQiqRvW+eCUF2qA+fhXT3OGmtrRk9G9Ax6AnxM5RuZnuAT+nW4Z49O56fvzcYQRB4KGb6rhuYTFDnjDD3hBFWUlYTXqe3TT2t7jnRDezJ2WSmWIhEpOJxGQcJj2yrBKNKVgMOqqLU/nl+uP4g1EyU6x84oZarGYDB0/3MmtSJlMr0vne4/vjK4k7V1YlrMxaur28taeFf7plKj2DAYpz7EytSOdXrxxnwK2lNWfVZFKU7fjf0adxGX9fCAIIrfsYePtX8W2p1/wTXkMqnWEdoiCT6UjCmF5AqP0M0XAAlwpfXlvKK/v6ON4eYlpJEouLVZJDaUTk2Ug2JwqQfO1nGW4+iSDHCBQvYtO+DmbWZOKwGtDrhEtSMjNcFm5dXoEoQGmuk+89vj9h/5oFhfhH3AyMBDnWNEB2mpUbryhBUVSyU21UuiLcdUUWz+/uJyYrrJpbSPeAPy5xvu1wJ1NKUplWkc6hhj6cdiOiKLB11LhJiSm8ubuFDJeZJ988g6yoiAJ86uY6VFQMOhF/WOWelWX82x+PUZCZRExRSHVObLYrynYgKCp6UWDIH6XfPUFZCkkSuHVZOaqqCTC+uKUp7kutk7QO8JuXlDPsDVOWKmKdlcLvNo81Kxr1EoXOsUByoT9ifJrBbNThjQj8YWeIynwzC6aa+MFTB+MaT5v2tfGF2yaTu/8LIMfoS/9hgoIvaGZI4UgU9zPfQLK6cC68hZuuKGXJjDxkRUWWZerK0zjSOLYCu2Z+MWa9VqAVRYFTfQpP7Ulm2aRPEZXhva1e1q0wx9lhADuOdHLj4hKsBh2KqibUTS5g2BMiK9WqWcLqRGRFpdcdwh+K4bIbkSSBF947y6Ez/WS4LNxzdTWb9rXSPagpDlhNOh65uZqUSB96KUxvxMbAsE6rsYlaemtyaWpCehIg1WmO99SApvF04LT2nIc8IZrahrnv6hpsFj1RWWHfyR4GhoNxt7+fv3yMz90+jVA4RixmwGbWk+o0xScSOknko2smYTLoWDQtl0hUJior/OGdRnYe037zB66dREv3CGsWlRKOyDises62DcflaY6c7Wfn0S6uWVAcVxwIX4JOe6Sxn+sWlmh/k6rA5v1t8YABGlV71qRMCtL+F3SEX8bfF4bIMINbnkQsqCWaVYshPMiQrQTRP4Ct7yTo9Ei2cqJD3YgGE4IgokZD2P3trLWdYHWpis7fhykwleHtz6JPyUHpOIMSi3C+7kGeOJyDThJZaoF9p/sBbVC56cpS1iwq4Y/vjM2yplemo6hK3OfBaTfy2dumsuNoJ/3uMMvqUigz9tCulnCmZRirWc/82my+/XtNziIr1cqC8FnmDJ2n7qbbUEUdfaqTLQc7uWOF5llh0IlsOdDOlfV5HGroozI/meMXyUiD5pCX7rLQPeBHUeHl95oozk5i6+FOdJLAl+6u51M31rLzeDfpTgt1ZakcauiNm/A4rAaWzcxHHR1kbCb9JaWzbSY9j79+GkkS+Pj1U+IBAzQjpzd3tXD3qirOtAwxszSTqQP7sa2oZNOZEOl2iWVlAinyAJAFQGaymVk1mfGUHsC1C4vZdriTmdXa7HHT/rYEUcBwVOZws5trcsoJtZ3CFWiZENCNBomUQCuydwjZO0T/898l7fZv4bJqKq9yYIiyHDs1xSlEowpGg4RVipCsCxHGiiAKbD/SQfdAgCfeG3sGZ1qHKM52xKVi1i4u5WTLMO09XiaXpFCR75xgT6vXSfFO8rwMO5+8YQryaF+ICvxmwylOjq7eeocCfP8PB3nwxtp48+mCSgcZTa8QPaO5t7uMFkILPw2yi7CiIEkCC+py6B8OcrbdjU4SWTGngNQkEx+7fjKCoNFWy/KcPL+pkRWzCwiGotxzTQ1nWoZ573AHWSlWFs/IxW42cOvyirhA47AnRFFOErGYgiQIhMMxHrqlTqvHiCI6SWDfqR62HGjHaTdy69KKeMAAsJp1uH1hNu0f65q/bmExOWm2uHPksFdbEV2A4RJ9SKvnFTHiDTPiCxNLs06QrAcYGJ44yflbcDlo/C+HKGoifIRiRBb/Ey8cj9F7OMS9V9WSFewlsOG7XKDLyOLNeI9tQfaMeUGkLLsX8+QltJ/rwZkl0iCnkjP3fizeZqJ6G6GMWn75Qme8L8FgSPzDFUSRA6d7434TBp1Ia4+XroGxAcXtDRPw+bjTvhvFKhMNpDPknItFp+eLd83AoBPZPs7DIRKVkVNLOGqcwabNw6jA7VeloJdEnhrtFxAFuGtVNekuCzcvLSfJZkDfMkxLd6Icd6rTzMEzidTFaZXpZKRYMek1ufHxtZd3D7bzz/fPYsQXQZYVctNs2IyJL+4dKyr51Ssn8Pgj6CSBW5dX8NZoc5eqqHFaZfwZCTB3SjZHmwbodwfZe9bDFZXzKXv1a1TnlKMERlCOhAhd9dm4D0RUVkl1muJBMjfNxms7z9PYNsyxpgGWzMgjFJ4484xE5bhQonHfE3z25q/yo+dP4A/FMBt1PHRNEYa9/xFnw6EqxPpbEGy5CAIYvJ3M1rVzTi2gy6dSlMb/1955BkhxXun6qarOaaa7pyfnxAwTSEPOCBAZZaGEZKRry5YlXznK611na23d9eraXu+u10EOsqwcjHICIYIQOcMAM0zOuXOouj+q6aEZ2cZeaxld1fOLGqq7T3dX1/m+E95DVtdOQvlrONIfJtNt+cBKN58/wog/gigKTK9Mx2zS0TMQoHcoQH3LINcsLqOhbTiR1J1Tm0U4Omr/sC9Ez2CAp98+TWvXCA/cPj3hMM4Ti/fAnGd+XpTIjncTx0rIj+3oM/RZPsOxVj9Ou4m8DBvLZ+QzoyoTnSQwId9J90CAnYc7CIajzK7OUh2j2cD+U93o9RIFWXZeiGuwNbQNUV3i5qdPHU443xSbgdtWVHKsoR+zUUd98wBX1OXx7sFWDtX3UpaXwqq5RbxzoI1oTMFq0lPfMkhVsZva0jQURcFq1id2N+d5eec5rlpYkpD7v5jmrhEWTc1l6351N11d4kaWFR5+XJXOOdU0QFWxe+zOymnW+jQ0VEzBboInthHsOINl4jxi+gI+OdOHPjCCkGVn+NXfJhwGggiCcoHDAFAYPvA6I7M/zXde8cYnrBVz3Gvi9IAbp91ItZRGVB5t5kt3WpJmOw95Qwx6Q0mxVkGAO9dWJY4nFzso9hjZG1mOw6xjICDgwcTTr53gbNswZbkpXL2olO6BAMfP9TMwHKTDMoloJEJJno5IVCYmK7RcMPtCVuDZrWdYMbswoTD7iTUTOXS6J3FTy0u3IYlCImzgSTWzem4R//70YUKRGKvnFrHzcLKelS+olnVOKvpgOfhhf4T/ev4IS+ryMRok0lJM7DvVnZAolxVVzgHUahe7xUBhpp09xzs5Ez/nbNsQxxpSuPPKr2HuOUFEMnPKn4rUrjArXk3a0efjpR3nEq87uyYrqet528E2PnlVzZiSzNk1WVh9SzF5BzDkTgSLnbuvraV3MIA7xUSW3Ip8kXaYrDfz/sluYorCrGw3uv0PM0GUqDRbiZ0eQufJ49k9fby4pwejXuLWlRU0dY6wcGoukYjMtgOtzKrJorokDV8gQmluKk9vOZ0IrdWWppGfYWfD8nIGR9ThVukuC+ELclwr5xTx06cPJZK24YhaDXWxgzIZJCbkO4nJCiY5eYEAEOlq5FxzF4+/pd44l83IZ+XsQkYCESRRlbL/8RMHEju04439fPLqGtp7vXQPBNj8bgO1pWlMq0hn38lu8jPsnG4ZTNqtDXnDdA34OVTfQ3PXCJPKPMiywpJpedSUpGG3GDh8uocVswp4YZva0DqpPA1/MJLYkd93w+SxtkdlpAuUe512IwWZdq5bUoYgQGGWg8NnerhlhVqBlZdh54eP7Uucf7plkDm12bR2e2nuHEEQYNHUPNwpJq0jXANM0UH6n3kQwWDEkJbH0I6nyVm6if6X/h0lEsR1YzZCcBhB0mMumYIcCaNEx9aOy4ERTrX5KMi0x+vg/Txxgcz3Owfa+NIt0/j5C0eJRGUCwQgbV1WydX+rWnoajvGJNVX87LnDDHnD6HUit1xZTmWulc9ePRFJEijJNNMXENl+rJX+oSDLZxZw5GwvZ+MhoNOtQ/zij8f4X1dVc/xcP9keG8GYyO9eOZmQyNh2oJVNa6t4ZPOxxA9+2BfmwurHow19fOnWaRxr7FfnOcgKOR4buek2BoaDbFxdyb89eSjh8GLyByuqXvzjEkWB4UAEfyiK2agnGpUTK9HcdNsYmZRdRzv4p00zef9YB/0jIapL0xjwJvdXnG0b4nBnJk+9KSErIRSli3tvyEz8//kRAA6rAZfDxP6TXdy8ogKTUceRM73qvHO3mftvmsLmdxsw6CWuXlhCXpqVqGcegiDgjcr83z/sTzgrgOJsO5+ruhKOqh35hvxqmowTGOgcIBSROZ3ipPz6b+B98YfEvIPo0vIYqL2FV59Vb8KhSAy9JHDfDVPYd7ILvV7ky7fV0dI1wm/iJcRfuGVaUi5mygQPh8708uZFukgPbKxLlIwqsjqbY9mMfJwOE529Pq6/ooxfbT6W+D5WzinEbNRhMevQ60RSMnNI7pkHKb+WA02j4Rin3cSTb9az+7jamFmY5UgK6QG8vaeFBZNz+F18F3v4TC8b4uN3y/JSE6XSF9Le48NpN9LcNcLgSJCmrpEktePVc4soylb1nvqGggSC0aRZLmajboyyQEWBk+LcVGrL0shPt1OQ5eCHj+2Pv3/V6LuvURcAAyMhinNSxlyrv3vlBN+4cyYnzvUjSaJaoSYIf7ckOGhO4yNLtK8N9/JNRHrbCPe341p8K8GG/UjWFIzZ09AJEezzbkCSo4Ta60FnwJRbgb/hEJGu0U5l86TlzM+EhWkhfM4y/uHR5L6MYV+Y7oEA1SVuJFHEG4jyu1dOUFeZwaQyDwdOdTMwHOD+m6bQ3uvHYTFgN+v5p1/uwR8Pn9y5diKPvHQioT30u1dOsGZeEZ5UcyKp3D8cxCTJ3LqygnSnmfrmwSQRPFV8sJuKQhfHG9XVdUlOCpWFTm5YWo5eJ9LW7aV3MMgTb5xCEgWW1OWTlmriqxvriERl+oaDSeGN9452sHR6ftJgG7NRR6brglnQwMGzffzs+aMEQlEyXRY+t2EKTZ0jxGRVaC/dZeZYQy898QTkvNps/vWxfYkbwp7jXdxwRTlnW4eSVs6iQCIhm+m2UJg5KiqX7bbw5XW5ZAZOYxg8QmBqNb3GCJ+7rlZNFKeaiYYiZDstVBfUqb0DigKCOv86EpUJhKJJDgOgoX2EkRXLyE5LQzTZabFV8/3fjE6pE0WBL99WR3vJp3CZZOxp6fzz4ycS34XJIJJiM/HQ7/YkJeAf2DjaQBa4KDzncVp45u3k6wrUjv7zTt+dYuTWFZVs3t5AV7+fCQVObl9Vyb3XT1YnI1r0pKWa+f5vR2Xc5ZCDzyy4Be+OJ1V9tPRC+svXMMlrY3ptHoKiVkq9F88LdfT4KM9zjrFDrxOSdMt0kpi4yQ75wtRVpo/Z0VUVuzgQz9HMrskaE1J6aUcj/7RpJqDKoNdflGs41tjHJ9ZW8eaeZpo7R6gtTWPupGzSU03Mn5SDThLYc7wrUTl1noa2IbLTrJgMqpJAYZadcx2j0z6cdiM6ScSVYsKgE1Xl6j8xUvZvRXMaHxEEAUyxQUKDvfT1ypidHnr/+DDRfnUF6Dv2LimzryJ1wY3EhvuIDfUimR10P/9wIkQ1bLSQcc0X6N/ye2K+QWw1izAXTqT7uX9FDozAzFuIRMc2AQ37w7wVT9hluCx8ZWMdDW1DDAyHWFyXR47Hyrd/sTtxE8nLsHHF9Hw2b29EEgWG/ZExYnXbDrQxtzabVxIKnWASYzz6ykky3RYml3u4mJgskxJXoS3JTeHaxWX85qUTNMbzGJluC0tn5nHDFWoV05nWAaZM8PCjJw7SOxTkrvVVCRFBUHMt7T1e7r6mhp1HOnDaTRRnO+jsGSI/dAZFjtFlKeVHTx5MrOg6+/08+upJKgtdCUXRG5eV8/U7Z9LV50eSRAZGQkkrSIA39zQzuyYrMTCqriKd6mI3G1dVYjPrKcpOwXmB7lOq6IfjvyHa30YMMJzdTemi2+keTKOzL8CAL0xGqgmjJCKggAIxReFUyxDPbDnDwHCIO9ZM/MBrqakvyndf0ZGdJjJlQm9S6EWWFV7Z2Uh+pp3fvtbIF2/Joyjbwdm2ITKcFlbPK+SN95uSVuuRqMy+k12JRspjDX3MqcmkMDuFaEx1rJPK0thxUazdbjFw34bJxGIKDquBB389Wl56qmmAn79wlBuuKMNklHBYDInP7jx7zwzzWmE5azZ8CzkcRHZk0NcaZsTv5dzZYfIy7ORn2CnJTeFs6xC9Q0HyM+1J1wDAqjlFiTwBwNp5ReRn2lk8LZfCLAcep4W184p5c08zkihw5awCUmzGxLhiRSGpGus8/mCEDcvK0enEpIQ2qE7lnusmsWp2IYFwDJtZR+9QiCFvOKFZdde6at471pn47eh1IrNrshjxhQlFYqTYjXz2+sm8uKORg/U9TCx0cfWiEsKRGFaTHlEQSEs14bLqL3WC7iWhOY2PCLpAN/1v/5poq1qSJy65LeEwzjO852VS5l7H4DuPkTLvenzHdyA50oiWLkSIBhHqtxBoOooxtwIiIbxHtqKEAwh6IwRGkE68xjUz7+J3W0e30SaDhHRBC+zkcg+PvXYyUV0EqtaUxzk687uly8u6+Wrdu6woSB9Q+28z6/FfcLO6YW4m3iH1OTv7/OR4bGPisCtmFaCTRKZOyMCdYkQnSdyxtoohbwibxYAiy6QxzBRjM7IgMW9REV/45f7EjeiRzce5c101v/zjUcJRGaNeoqLQxfPxDuAzLYO8s7+VL11XTt8LDwMK7bO+NCYE0NrtZU5NduL4iTfqqS1xU5ypym0PXCRdAurNuLrYzbAvTI7Hhj8URRQEFk/K+sBYszLYTrS/DcmVg5CahTDSzaFADk8+cZCKQhdd/X5cdiO3r6zAqJMAhbbeAP/6hwOJm0x7j5dZVZm8d6wzcaOsq0iPd+0Xk+kyc7plbB+DLxAlOz65TRAEblhShtmsx+uP4Eox8t7RzjGPcVj1zKrOYnZ1Fp5UE229fn769CHkeOf5XeuqSHdZsFsMyLJCU+cwhVkOjp7tJSqrTu/iVXVj+zB6vaRO91QUJpWmJZUCA4z4Y/znOwHqWwb40q25bN3XwIH4OXtPdDGhwMm6+cU8HJ+z8qvNx/jqHTPYf7ILfyhKTUkaBp1IXWUG6U4LBVkOrCYdZqNESW4qOlHAZJSwWnSsmlOIgtrPo5MEmrtG4p9XOGnXDKo8usthYvexTmwWA7UladRVpCfml5wvKHhxewOFWQ6ON/Zj0EssmpqbeI4/vnuWf9o0kxONfUiSyKTSNDJSjOAyY7aaCPhCKIrCxuXl3LikDKNOAEVdYGY649318c/274nmNMYxigC+YAyLUSLYfDzhMACUSHDs+bEoROM3LDlGKLOGndY8Nr/bi9Hg5OY59zJJ30HkzFYi3ecA8J3YiWXCTEb2v05suJdp4T2kXL2UN/Z1kJUisbCukIefPJJ4DXeKidfeS05AvrzzHKvnFo2ZYAfxVVhMHqOjdMvSIqzBLmo8GbgNEVzmMP+ydfR5X3uviS/eMo239rQQjsZYMDkHvSSSn2lTu5slgbZuHz956lCiiunWFRVkeN/GfOQNAMSUTD51xW28eSqM3WLgRGMfMTnGPddPon8oiNNhIiPVQM+An8547X9BhoVCe5ho/JfmcLqA5Oorl8OE5SLJjv7hELGY6iDz0m1jSl2XTM/j0VdPYtBLHDjVTSgS48op6RgCQyAZCBtSVeeRSIQqhFd9nU45FVkWSLHq8XV5mTJB4tDpHnI8NqqK02jr83O6ZRCDTsQS7/I+T1F2Cg6bkbqJmfQM+PE4LYQjURQUntlyhtLcFK5ZVJbUEQ6wdEYeTpuRu6+pJcVqIBJXAW5oG8KdYuL21RM5esEM8WkV6YQiMt/8+S5kRdUiW7egJJG4l2UFvU7i4KkeGjuGEUWBaxaV8N7RjoTUxqa1Y3dFFpOOYw39PLdVPaemxM3iaXlsiQ93kkSB3HQbL+9UV+YjvkjCYZznVNMAq+YUJo49qRYi0RiSJGIx6hj2hWlsDxAMR9FJEi9tb2DlnEIMHoLO0AAAMthJREFUepHmzhHK81LZdbiDigKnWlAhCFhNOryBCA/cXofXFyHLY2FGVSa/e+Ukp1sGKcxycOvKCgLBiCqHI0Cq3cAn11ezfkGASEwm02Vm9/EuBkZC9A/3oNeJfPLqGn75wqgQ5pQJ6WQ7TRSl5yd6RM4vMKxmPX5v8PylglESkhojz8usfBhoTmOccX4S10goylNvn+FAfTd3ra+moj15qI1iy0A025ADoxVF1srZBBoOAuA9voOjlZ/ldO8QaxeUoMgKrx7txr24Clf3rxOPEUx25OBook88vY1Z05ZQ3bkHFAVf1MHdV1fjD6lieBdWd5wnHImhv2A34XKYcKeYMOolDHq1Q/fzN03hdOsgXn+EyiIXO/Y3MytHIVMaQjE42dZhoKV7NG7c2u1FLwlcW2dXa+P1MYIKfOPnu+keCPDAxjp+tflYIkcQjSn8+qUTFG1cRmrcaSjRIOkZaVjO9dA3FOTqxWV09Qd5YdvoD7Mkx8EPPllHY3MXFqOO4nQTsVcfSvy/HAmyfGZ+YvKgXidy1cISIhfkRgRBrcp5e28LkZiMy27ka3fMYOv+VroH/CycmoOswLTKDGzxBripmTKmXf/F0EgfSjSKfdbVNFqr2Xmkk1A4xsIpufiUKEcb+mjt9jJvUjb1zQOJEE9nn5+TTQPce/3kRMnwp6+tvehagjMtg0mhl/mTc8h0W7l2cSn+YJRINMoXb5nKH99tIBiKsXpuIVlpNg6e7uF0ywCzq7MY8oUTndUGnURnn4+NqyrpHvAjABWFrsRKHlRF2veOdDC5zMOB+h5qS90cqO9OhBFlWeHpt89w28rKxGMOn+ljweQctl0gUnjVwlJee+9c4vjI2T6+eEshoUgUvSRSmpeK7oJcxPnRqhdj1EtsWFaOKAp0x3s29hzvRBAEBkdCfOa6WsIRGW8gwoQCJ2/ubqaiyMX7xzvZebida5eU8u/PHKay0EVMVjjV1M8Xb5nG4FCQkUAEu0VPYaade6+rxReKYjHqcZgkFAVKcyrU9xzvys9xjTaPzq/JoqrQxZAvTFqKiRSrnn/8xHS6B/zYzQY88bkdF4d2Lzea0xgn+CMxGtqHaWwfoqY0jeaOYVbNzGVWhQurWUSYuATqdyXOPztigOn3kN61C91gM2LJbKSYl0DDIUwF1cgpuYQUHZGY2mQnigKLpubSNRwhPcVDdKgHEGDatYTq47Xugkjqsjt5r8PAgG4efT6ZaToP7+xpZnc8JPHJq6rHrKJnV2dhtxooy0slL8NOeX4qjW1DrJpbRCQao7HTi6TT8dKORsIRNSFdW57J1iPNTMxycvRskHl1WdRVBtl3sgu7xcCNS0sp6N5GcLc6mCu89Et865nuhCSGJIljZEYAer0xUuP/HplyC9957EQi7NHQNsT6BSVJu56zbcMMhkRmTSoixWlnuH4ffSOjScuob4jWbhL9EqIo8PKOBq6Yoc4PMRokbl9VqZb2hqIYdBLTKjN4eks9vYNBHFYD//XcEe65fjIHTnbT0edj+gQ3Oa4eGrOv5P3mKB6bRJU+ne/8ak8i4ZxiM7LzSHti/nV1sZudR5LDkb5AhFAkyievqiYWU0i1Gsl0WxK7Jp0kJjrkz/PuwTaW1OXy3DtnsZn1zK9wYIwMsWFuBoogkGqXePNwO64UM5WFbgKhGJFIjPJ8Z2KiXGefn7wMO23dXnQ6kWyPbcz3cLJpgLXzizlQ38O8STmJyqoL8QbCieqpvSe6WDm7kLuvqaV/OEh5Xiq/f+0kfUPJO+oRf5jWbi+xmMI7B9q4Yno+6U5z3BkMUFPiTjQYglosceh0byJ3JgjwD3fMQK8TkRXIclt4aXsjxxpHFyzrFxRTnpuCJBViMeqYkO/kjjUTeebtM0iSwGeunURptkNNNIsC0fj1pTfqsMWTzucX+eedxQchAml2I2n2uAacDE6LHqfl0lV8Lwea0xgHROMDVpaV66kpixC1hNFn2/jWI/sS1T6zq9K5ZcGdxLb9EgC/YuKHz7dSlFVNlrOOlj1BvrGqEDsCwbZTmDNy6W8Lcvi02pchywpv722hoqCW0KSrEH2DDFnyePK9MJ9ccRu6CQuImZy80SFSlm+jZzBEpkOiZ8CfcBgAv3/tFPfdOJnXdjXR1uNlRlUm+Zl2QqEYs6rUOdyiKPLoaycTP5yNqyr56dOjA5a27mvFahC5I+0gwcPbmZZXic5xM8aZ+UyZ4MGol6hMDRJ8ZnSSY8dwLOEwQG2yc6eYxtxU3NZ4wlEQaQnaiERHkv5/6/4W5tSMJuAB9HqJiKJDkHTI7hKcS+9kaPvjKNEIuS4jobAv0VQIarfzrOosCrNScFgN9A8HOHlukCkT0hEFONbQx9QJGTz+ximC4SihiMwfXj9FRaGTjj4ftbkmjkbz+LdX1FW1y2FiMGZKqhY73xzncpjwpJoRBAG9JH6ALIjC42+oqr7XLinltpUV6CWJqKwgyx88QS8aVfiH2+vQSSIWQ4yvP9mREN/72idm0DsY5JVdoyGrDcvKWTBZ3ek0dQ0zp3YC//bU6PdZVeQe8xrqTBHV4YXDMYqyHUkhLVBzAxfmMURR4O09zdTH51NcPIrWoBPVMtfO0e80Go2hj8+g+P2rp/jup2ZTXdLPgfpuakrSmFLu4d2D7VhNOhw2I1fOKsDrDyc0y8xGHd+4cyYN7cOc6xiiuthNaY4Dk14iO82qOgYgvTqTunIPgihgjI+mleOf8ccNzWmMA0YCYW6uDDDw7iNEHGlQNp9HtsSSykN3HetmSW0lE+bfAEDEoqCTRBo7vDR2eKkpSiFyage+k9sAiFo8HGocq2Lb0DbE5gY9vUN2AqFeplWk81+vN8e1gfqZVpFORWEaKVY9lYVOntpyNunxvkCEZ7ecYc38Ig7V95LuNPPz548iKwqeVDPl+ak0d44kblY6SfjA0ZZbD3awbI4LQY4SbTtFR4+fHzx7MlGFMiHPzqcmX4108DkA7Nbkqq5Dp3u5a101P3nqoKo2G1dwzVI6CE+8AkGJYrQ6gOTVuVEvYTXrWTQ1l3Mdw2S4zDgtOsKygj8UISoYEUsXYCucDgo0DUSoKu5jWmUmgyMhMt0WynJTeGN3EyajHlmWqS52s/1QW8KBleWlMn9SDtcsKqV/JER5XirvHe3AZlZvgnqbg2d3NLF2XjEWkw6zUZfkMEBNbdx85QQGhkPxPgGF/3VVDT977gjZHisDw0HSnBZCoSir5hYhoOoMLZ9RwFNvn6ZvKMi9N0xK2nmA2nBoMIjsPqaG4woz7ayYXcDjr6shrmhUHjP854VtDXzlNlUZOcttTQxGOs/plkGWzcznjXgIz+UwsTCe0M1wWfGFoqyZW0RL1whDXjWcOKs6M6miqCwvlQy3Rf0MWwZ5+q16vvaJmdjMBvae7CI7zcrNyyt48q3kMO2CKbnUlKTRPxJkQr6TjBQT5gkeakvd6EURk0HCk2pi+axC5FiMbLeV7DQrX7l1GtGYTI7HhtOqJ93hYe38Ynp64g5JAdMFoS9ZVhJTBT/s0bLjHc1pjANypD5iYT+O2iWEu5vQCxEm55tp6EheJQ8GZAbefRIAg83FP274Er94vZn2Xh/XT08h/Oa7SA4PclYlOkGmItuUqPA4T4bLwrHGPgKhKOlOM5PKPPxq82iM/3hjP59cmkUw3Ey4a5AFVWNlCSYWuRga9vP23hbyMuwsn5nPq7vOEYnKnG0dYmpFOk3x1WA0pmA0SKyaW0SqzUAspqDXiZxo7EHynUIGhLJ5/Obd3qSyxVMtI7TXlJIXP861BJk/MZV3jw8C6sCe6ROn85lra+keCJBiNZKdZuFol8SzpyZgMojcUeUkxWZI3KgANiybwOmWAY439lFT4mb5FA+7T3Ty6nsteFLNbFxZyZA3xONv1hOOyKxbUExHn4+D9T1YzXo+sbqS9l4/R8/2kWLVMeiLkmozYjHpE05j5exCfviH/YnV+8vAPddNYtgb5Ial5XjcdtbNL+HJt04nwmRLZ+Rz3w2qyqmCgsNi4P8+fiARgjsU7zD+ws1T2H6og3mTsqkodPHE66cSoZWZVZkMjATxB6P0Dwdp7/Gyak4Rh073JE3Q++6v3k+s8F0OE/de0J38QaJ4gVAUBLhhaTmZLsuY4Ui7j3Vy85UV3LFmIoFglEAoym9fOs7SmQWJJPayGXncc90k2np86nx0uxFvIMxNyyegAG3dXk43D9IzoDo4g15CVhSuXVzCyjkF6CUJm0nHugUlvLyjEYNOYvW8IgozLBSlW5Mq7ZwX7VAWT8khEIohSQKG+LCuCbmjIaCPuQ/4q9GcxuVClIkGQyBHEYGhY+8SbIyPJj2ylaUV82kum8ze06MlkW6XA70rC0SJaNBPll1WE5suE3lpEYYWfYZt/S52HOujPMfGsuk5HGny0REvhS3JTSHDbWHRNFX+oTDLwb/8fj9Wsz6+IvXxlVUews9/izS/mrQsmbycWxfX8egWNTZekGmntiSNdw6qxy1dI2xals/VJWlE208hOnMg00F7j6o+KwpqFc/vXznBubgj0UkCX7l5MvJr/w5A1Oqha2BsNZgPE/E6EIwj7Vzr6GTBmimMRCRy3Sb+uO0M24+OVsucFxc8L/h2tm2YtfOKGfKFGfGFKchyMOgNUZiqUDXTTHcgwq9eb2TNvGKWTs9HkgTa+3xJoZefPXeET19TS1G2A5fDTKbbxplTjXy2ph/7YD3BnHTaFCuLp+UmhiqFIrExEwOffKueL99SR/egH3eKic3bG5Oqyd58v5nqYjcNbUPIikKW2zomZ/P6+804bEZ2HG5nx2F1Rvq0ioyE09h9rJOJxS7C8c7/tFQL//ybPUwscrFgSg6pNiO/2nwsKSTUPxyko9eL2SgSCMmkWI1j+hjK81IJhqK8tL2BnHQbVy8s5eDpnqTdpMkgJS0+Fk/L5UyLmhuymfVMmZBOmsMUH64lYLcYeOy1kzR3eRPnfH3TDBQEhv0hPCnmxEAlp0WfeK2qvFRqb5um9qVE5aSKoT+FIiuY9OJfPE/j0tCcxv8QeiGC6OsFUSJoSEXXd47Qe88i+waIXbFx1GHEiZx8l7WL57L39BBmo46184qJRBWOTfoCMUUhJ81Cb38QiynKieZh0p2ZvHCyl/eOq7Hoth4v+8/08w8bp3LgzAACah281awnHJYJRWIowE3Ly/EGIjR3jrBuVjaepmex1q1EkWMIoo5IXxt12UFi8RVhz4AqKnf3qkKWFsUwpHpwdbyHf/ezCdt1WaXMK7+F8ny1ya6t25twGBCPw799lv9dPB25fjv65vdZWHsDr++9SGgtPY2eWfchIGPPcCNse4L0+q2kA8HZd7LjWHLYa9gXRn+BhLcvGOGpt05jt+ixmPRs3d+Kzazne4v88P4fKLA4qJ6xiQZ/hJ6efgRRT1dfsnotwNt7W8jxWHnqrdP84NOzqLF20xrNYaucRY5OpFTuJiVNXbl+QEsKAMFQFG8wzMH6HkwGiZMXdRif/86e2XIGWVbnff8l2nt9LLygIRDgbOsQRr0u8Rmcb7g73tjPytmFHxgqjMkKNrMRUYjQO+jjcxum8Nhrp2jr8VJTksb1V5QBCp+5bhKiCJIAX719Ou8f70IviVSXukFRB0cN+ULMrsli/YJiojGFIW+YVLuBVLMBAYXUC+z98q3TaO/1x8NEVhwm1Ul47OpOQfkTDiEW+TtqYmj81WhO438AY2QA75ZfETx3BAQR1w1fZ+jsYcS66xEBWR57owIwSAo3Li0nEpPZsr+FVHsJ//W8WpOe5bZy5/oqfvLUIWQFphQ7eO94co36kDdM/8BoSe6wL0RTx0gilqyTRPafVEsrXSkm/MN67CU19L/1O5SYenMx5U3EVkiS9PmiqTnot/07KR2ncS68mYG9f0x63WjHGVyFffz4jWF0ksBVC0vHvLfO/gCB5avQuUvQpaazyJpHdoaTYFidkOZymIkoAu+0GDHqJRS7SMnKe+j0wkgIUtNzkMRTY3IB54su1d4A9Q4+EldgBUi1GRBGzqEAsn8Y067/Yu6qe6g9+gSCu4C3rSvG2JpiNzK9OpPJ5em4dQHeGMzg8XdGq5KKs+zcv0Z9ZQWBHI8NnSQm2bZqThE/f/4o7b0+Ovp8TCxys/9UssqpyaBLJFb9wQgepzlRPQVqueyBix5z8cI5y21lT3w+eyisjgS95coKeocC5KRbWTYjP2kMqSQKFGQ6WDu/GEVWcKWYMRt13LqyAlEQiMRkTEaJYCiGPxDBYTOS7jTT1DWS0FZChtJsBw/ePZuorGA1Sghxw1wWPR6PfTRXcAEWvURpln30vWi7gI8EmtP4kBEECJ3Yhj6/CteiW0AQGA5E0TszCb/2MHLIj3DNF9G7son0j3Zi6zJL2XYmyiv76xPPk+McXaV19PnUCV81Wew63IFBVEdmRi8q8ROUGG++30QkKjPkDbNoai4nzvXT1e/n5uUTyMu0U1XiprPPR0a6i+G9jyUcBkCw5TiOmisSx0aDxLLCCJGtqtaOEoskpvpdSI7LyPSJGVhNeioKx+r9LJicQ9TkosMxmRyXldb2Ebbsa6GpcwRdfJTn5HIPkaiMLxBFsGTzRlOEP2xRJ8yV5gqsn1/IM1tHdbSKsx0MjoyGfCJROSkRLAhw65xU2JUspy13NxDpa0Mny+QVWrGYdBcM1BGYXpkBMhxv7EOvc3OuL/n9NnSM0Bk0smFZOYIC/cMBPrFmIntOdNE/HGR6ZQbBcJTSuPjd0bN9bFpbRXuvl84+P4Kg5kFONo3uPl7a0cimtdX0DwepbxlgcplaVfbOBT0XqXZj0oyFikIntWXqDO5gOIokieRm2Nl7vJO2Hh99gwGWzyxg46pKXnuvCXeKiasWlmAz6Zhfo4olKrICotpYFwjHcFgMWHQCihUKPJZERVZNoYthfwRBBIdJj6Io6M+7bO3m//81mtP4EFAQUHw9KP5hBKMZR8VM/Cffo+MP30bUG0mZdwPRMzuQg+ouoOfZf8V9y7cIHN1GpPUY+sLJ6CYsILBjAFEUcNqNfGJ5IaI3eSfR0uVlcV0ueR47w4EoV83O4unto45nQo6NbLeFmROzMBgknDYDMYWE2maa08yxxn5ejZefljpl0vqSpcIBiITUhKWiUFdoxFD/Np2zP483KlJs12Mum07w9OhkPtFsR3Ll8Nmr03C5rBw928O9N0zm0VdOMOQLs3BKDgun5tLR56N3MECmy8quI+1JyfPn3znLhAInV9TlowCSTuTF3R2J1eiZ1iGKslP4/E2TOXymH4/TTE2JOjiorjIDQRCwmnWJ8aXhSIwJOVay9vwUOXqB3LakU2XjASXsp6erh6sXlRIMRYnJCik2I8cbe2nv8XOqeYBX31Orni4clgPgj6CWvopw+5oqfrX5GBOL3OR4bLy+u4loTGFZvLcD4Lcvn+BzG6bQ1j2CIAjkpdt56NFRMb5oTJXfOHSqC0kv8cb7TXzuxqncfOUE9p7opiDDzsJpOYTCMW5fXYkY3zE0tg3x7JbT6HQS+45389kbJzE4EiI/y6Eqw9qMxGSZxdNyMegk7GYDTqshMWQKABkcRh2Oi3oOYhcuSBQFh/n8/2te4uOE5jT+zogi6L2t9AZ1dA8JpLl0GJoOMfTe8wDEQn76X/0ZrituJ9iiJk6Febfzs20jNLYXUZoxkfp9AWqGR7h6dibrMtvRRXqx6gy8M5DJhnisWwQm5lmJ9reRZvCiiGlMmJpFscfAiVYf+WlGKorS+PbvjyXUV40Gif+9YUoibCLLCodOjzqi3Wf9TCmqQzkz2kQI4Dd5EuGp+Z+q4rHuMhqOBkhLNfOrbV18c8PVGKyZSE17kd2F9OYsYMBvZqCzB53UR4rdzB9eO8ms6ixsFj2dfT4O1Hcz4g1TW+ZhwBvkWEMfRdkOpkxIZ9gXJhqNUd88mJCMmFSaxqevreXHTxwkxWZQK6ZsRs60DtHW48UfjDCpPI3BgRAd/T4kQSTbY6Wm1M3zW88wMBKiPN+BNHER8rZzqoijIJI691q8R95RvxvfEBMdI3z3te7ETTsSjXHH6iq27ButGHrtvXOsW1CSUMe1W/Q4bGpJcFSG7DS12e3CIUJLp+cnhaMy3Ra8/jDPbDmD027kxmUT+NyGKTz5Zj2hcIz1C4qZkJ9CYWYNI/4I7hQTDrNEdbGb8vxUDJKoKgq7dWS6LUiC2j+Q77ZQUeAkGI7hdpgwSgLL63IIRRWMOgFFVnMGlfmpiSl02k1f469Bcxp/BwQBTPIIYjRITG/hWI/Az19pIC/TTpEnwpLud8Y8JjzUg+RIIzbcS699Au8eVHMVrd1qKKWzv4nFk7NI8WQTE430WbN4842DCW1/l8PEpPQc7O/8HwCkojp+e3wxwUCIuZUp1LcHeHzXSRZMzuG5rWqvRSgco6FtKDHcpveixrhjTUM0VC2gPBogfO4gotGCMPNmDvSYWVKXh04SaAsYKSnKIsUZiMsuFNKv2NEXXYmSswBFMhJDoLvfx388oyb3M90WbrlyAjJqKWVBhp0Mt4Uhb5iBEVV59NaVFZxtHeKP2xpwOYzctLyCUCTG/Mk5xGIyHqcFs1HHDUvLaOv2UlnkxusP88iL6o3bYTUwf0oO//7M4UTZaKrNyL03Tsaol3A5zAiCxEM7DVw/8/PYFC8BnR3JGCY2MNq8aDv6HP90+wMcahwmGlMoy3Pys+cuKlKIyeSl28jx2MhJt1FbmkYkFkMUQBRFBkeCfP6mqTz22in6R4IsmZbHwqk5iKIaMqspSWPZzDwO1HezfkEJkWgMk17CZtVxz3WTkAS1eEASRdIdetIdo/02mfGBOmlpap4gFlMwS8n9Ay6rAayj9ioyGEQhMY8L/nynsobGn0NzGv9NREEhNNjFqU4voZhEZbGRU11RZtdm0zPgp7bEjRBIh56WpMcp1jTQqzeDi7t8QQ0LmwOdWAZOAyJH+3RJw2D6h4NsPzXMCmcW0YEOvJmT2fmSmgDdf3YwcZ7RkPwV+4NRaordHDzdQ5bLTH6GLVH2CPDSET+56+5CqRkgKuiImZ1UKiGmpIdQFJlegwGrOYon1cywT9XzP9k0mDS5b3K5h40rK/jWJ2epA4y8QfQ6HQMjIcLhCLVlLhrahukeUOdbiIJAe6+fd+JNY90DAX785AE2rqpk87tqzuLOdRP5xQtHaY1P73tzTwvXLCplQkEqp5oGWTQtl20HWpP6DAa9Ic62DiKIIruPtTG7OotzXX7+z8vnCw/8VOfbuWfNA4SbjxAzpnBOysUcEmiN37SLshyJaXLnmV2TxbA/TFleCqIgYDPrSbEY+OKt0xBQw2KCIHD7momMpscFZlZlMX+yGlJq7hzhiim5+EKqeJ7dqPYlDPkjKApMLfPwp5ID2sZA43KiOY3/JlH/AHqDngq3TCwwjBQ1ku+QyYy2Yg6fxDBYjH7qGgz2VHQOF4IgEuxuod9aRPaMtcRG+pCcujGSGF+7Og/jwacZbDmOYDAzabaVqjwbx1pGb/D1XVHWFFchpGYRlUQkURij63+hhJsgqI15xxt6WTI9n2xbjPvm6Xmn2cOh5iBTC83MywnjHfHx8r4Regb8fOlqA8Lu3yMPdyOKEvrFD9DZ5+P5d86iKPClW6fx5Fv1Sa95sL6HK+ry+OFj+xEFWDqjgLrKdDa/28DASIirQyXodRK/f+0ksqxw1cIStu67yKkqEAjFEk1bFqMh4TDOs3l7A/deP5lTTftxWPQcOztWi6pnIKDOm0DdIeRn2JMaHn0RgQY5i9daw1jNOq6Yno/VKOF2mJEkgSFfiC/fNo2XdpyjuXOEGVUZzKnJ5vDZHjLcVmLxTmG33YBRLyEKYDfpCURiGOMNaiaDhNUgEYnG6BsK4k4xMbnYjSQIOOJqueerplLM54sdNM+gMT7RnMZfiSiC5OtC6WkERUHvyWP46Lsw0IbOkUbo9C4m2l34DryGDATrtxNLL8RSVsfgu08BYJwwB53BhDygNtDZmrfzxWvm8NTObk419bNmdi757a8RaFGl0JVwgPA7j3Dt3PsTTsNokLhuUSHPHtXR0BNhfqqVz16Tyo+evlDBNYW8DBtleakY9RJzarOwm3TMqskmEo3hpoORLT9mRfUyll0xCV3LXiJbt1Byw9e4a0oUwZiCTgjTnLeEbY0y4SgsCMYS4S5QQyIXr8SBRPWRrMDru9VKnc4+H9GYovZpvH48caMcGAnhTjEnJZYB9JKYWFWLH9D/EIuXgwKc6xxh+sQMzrYlz4coz3cmdiu/eOEIX944nV2HOzjZ1E9loYu5tdmkO03ctrISnaTqO1kMIgsmZ6MoCikWA5IEG1dWEI7KmI0SBhGmV2Yw4otgNkg4LHpERh2AoiiYdCImW3JncrbTTLbTjIbGRxnNafyVGHxt9D31IDpHGogiMd8IaSvuYmhfJ4Gmo9gqZoOYPKUr0n0OqXYxwsSlCNEg4bPv486vZGD7k4lyVUdhE6VZ66gscjElCwKb94557Wz9CJIooCgKn7qqmp88MyoNfrplkBWzJP5x42S1uibdgtOdSle/nxuXlROTFXr6/Qz5Izz+Rj2D3hDfuy6LwJrv8viuPk6+10t10VSuX70EeagZ/2s/B8BYNoO98jzePKgKH9ZW5iTZ1NLtpaLQyclzo8qwJoOE/aKms9ZuL2mpZjr7/MiyklQa/N6RDm5ZUcGvXxyd/12c7WDIN9pVnWIzJnIx55lVnYXdYmDF7EIynGZS7UbWzi/m7b0tGPUSy2cVYDXrEs4oFp9DPacmgxlVmYiCQqrNgEkSMVnF0X4ChaQmNEUGs07EfL68VUmuLtLQ+DihXfV/AUFQMPq7iA12oEvLJdxygtQFNxJsPQWxGM6FN9P97A8TQ5GG3t+MdeJcDEs+RZdPxCoEMPh72B4o4YUTRswGkRtmzcfiO4vO6iQ63INkTaGnYDlPPtcIQO/UNNan5RLpTh6OY3em8q1PlgAC/UPBpBsowOvvt7BoWh7VZRmk2IycbRsi022L9z6IFGXZ+c/njtDeo+ZG2mQ3v9x8IiFnse9UD229Pr56Q0XiOUOn32fm3DrOt+/ZLcmr5yffrOdrd8zk7b3N7DvVTX66nQ3Ly/l5fGTleTJcFt4/riaclXjIJhjfoYQiMbbsa+Grd8zgbOsgBr1Eeb6TEX+YU80DRCIyI/4I1y0p42RTP+09PmpK1H6E3sEArV0jBEIRlhcUcK5jiCXT8ojKMjpRIDfNyldum0YgFCM7zYrbbgBFlVaPxbTOYg2NvxbNafwFjLERRL2EIbMQ9AZkVxa9z/0wMXfbXFgzZoqe78QuKFvON58+S6rNwHVL5vGLP44min/wRy/f2TgN2+IawuEoRqud19/r4dYVFURj6hhSc85Goi/8ACXeU2AsqeOEz8lDT+8mJiv8r6uqx9gqiQL7TnTzVLwU9JpFpew/1cDhM2rpZ6rNyGeureXB36g9FeGokqR/BOpwn26fRPoFfzPLowl4nSRw3ZIynn/nLNGYTKrNSDQeJvrU1TV09vkY8oaSavon5Dsx6MREGOut95v57PWT+Y9nD+OLD7HZsGwCAgpVxW5iMYWmjmEmFrr41PoqZAVEQeAXLxwhLdVMhsvC9kNtTCrzUJ6XQpa7EqNBh8MkkTW3iEF/FEkU4rIUCqVZjtE3k+g50ByGhsbfguY0/gwGguiECP1+6Bzw4kmzY2o4wIW1i8JFoSgAQW8gxaymoCsKXby5p23MOQeaAvQNh2jqHGHdfAcpVgMvbm8k22OltduLb2YBFfO+hDnUS1Q0EbFn8d3HRse9tvf4xsheL5uRz66jo/pNz249wy0rKhJOY9AboqF9iByPlbYeX0JmI8l2AS6OugyLTkDtVj7XMYzbYeSGpWVEY4pabmySmDExk9ZuL+4UM1lpNubUZmE2qlVEfUMBKotc3L56ItGYjE4SVYG6O2fgC0SxmnS47UZ8oRgdfT7MJh3TJ3jQiQIXXqJf3zSTl3aeoyE+4GnWxAz0gqCWmDLafJaqNZ1paHxoaE7jIvR6EVOkT83g6gw09Yl89/cnGfSGuOfaWqbKF809MJrRe/KJ9DQn/uaYtpIuv3pD9gWi2C6K7wPEFNh7shtfIELfUAir2cD8yTk0dgyxeFou0ZjMGa+Fx18PUJCpIystWeDuxR2N3Lm2Cl8wQmP7MDMmZrDnRNeYCqPIReW8gyMhTHGv0N0fYNHU3KRxoMtn5OP2NRIFBL0R6q5Hl1HIrGp1R2HQS+Rn2BnyhRmON53FojLhSAyvP4xOEglFokwu87DvVDdmo47FU3OxmPRYjBLDvhglOQ6ynRZEATy20R4Eu1HCnu3gT+G2Gbh9RTkxGXTjcAymhsbHAc1pXIDZpCANtRHqPkekuxlDZhG5aapk9oIpOZzrHGJ+1XS8R7YmdhveY++SUreS6Eg/cmAYyZ6G5M7lx8+rchzHGnrZtLaa4439iZuczaynLC+VF7apVT3pTjN/3N7A2Va18ufo2T6qS9xcUadOkxgYCVFb5kmyVZYVGtqGmDwhje0H28ldVMqTb55OOkcUhaTZ3aAOq8/NsDOrOovcDBuF2Q4qC130D6uloAVZDl7afZoJM76ALyLS67Vi7wjQ1e9DJ4kcrO9mYqELf0jdIRj1InqdyH8+d4RUm5GB4RDXLSmlrjyNooxi3G5bQqxuerknae7B34IiExd51ByGhsbl4CPhNDZv3sx//Md/EI1Guf3227nllls+lNeRAoMMbn+SwNkDib9ZaxfzmfVrePKdZkTBSlPIQc7yOwmeO4KixDDlTSQa9CFmlIB/mIjJSX3ARc/QBWqkgsInr6qmo1e98RoNEm09Pox6iVAkRkxREg7jPEfP9nHlzAJAHYIzocDJ7qPmxIwFh9XA1Ip0nt1yhuauEX770nHuXF/FL144SmefH6tZz6a1VfiDERxWtYfghqXlZLnNhCIxLEYdZqNEJKqw/VAbqQ4jJxr7yM2wsXxeBR19PlJNOuqc6qjRsrxUFFkhPdWMXhRwXrB7EgT4+idmMDASwm7Wk2o1JMZhXowWMdLQ+Ggz7p1GV1cXDz/8MM8++ywGg4ENGzYwc+ZMSkvHym3/d4kO9iQ5DADf4S0EzFM51TTAqaYBdh/T8c1lOhwDHSCK9L/1Wyzl03mlp5jnt/uQZS/prl6+cecsDp7uoTQ3hTf3NPP+sS7MRlX6OhSJsXhaLg6bgZ6BwJg51+exWw3cuFSdSfHkG/Xctb6axvZhVcBPFPCkmlhY66Eo08KkEhfZqQb+941TGPCGsJr0OG0GZBSqi90IAggIGHUCGakmZEWdj5xq0fHpq2vwBiPYTHrMerU3wnFRmCg79U/3FygKWA0SVrclfqx5Bg2N/18Z905j586dzJo1i9TUVACuvPJKXn31VT772c/+3V9LjoQ+8O8So3mBQChKe8yD6Xw5rChhnjif9j3BxMp6TrUqIfLkm/WqymxNNtCljs2MM7k8nRSbkRF/hOJsBxUFTk42jfY6TJ3goaljiCferEevE7lp+QQynGaiMXUFn+W24DTGqCu2MbnIgVmSMRgk9EYJm1WPThAwxkdbXhwS8jhG523LMvFGNDW3oN3vNTQ0/hzj3ml0d3fj8YzG89PT0zl8+PCfecTfjuLIRJeaQXSwK/E3KaOEw13JeQGdLRXH9NWgyOjSi2iJuqgskLCa9GSn2XBY9DwVl9Zo7/NTWeCkdWIGe453IYkCa+YVkZdhY8QfwhKvMLpzXRWHzvRyrKGP2lIPk0rdmAwSRdmpmI0SaXYjiqwwIceB3WHCOxwE9Jjsow4gBkgKWKTk0ZaaI9DQ0Ph7Me6dhizLCMKogpKiKEnHfwm323bJ5/oEG44VnyF05A1C7Wcx5VfiL7mCV385OrUuxWYgrOg4pq9GFAX2HImycp4b+gaxmHS4HEYKMu186ppaegcC2G0G0p1mNq2t4ppFpUiSgNNmwGQ0UJIVJiYruFMt5GY4qCh0j7EpL/ODbTV7xlZkjVc8HvtfPmmcoNn64fBRsfWjYidcPlvHvdPIzMxk795RSY2enh7S09P/zCOS6evzXlKljcdjJ9VppbndBZXXk1IbpjtixGQwcO8Nk9l7vJt0l5nplRnYLTrae43IssyahVY8DgNGox5FAYNewCyJpJr1FGfaCYejiZV+xnmJawUiwTAZqZYL3tfYcZh/zta/5vzLiWbrh4Nm69+fj4qd8OHaKorCn11sj3unMWfOHH7yk5/Q39+P2Wzm9ddf5zvf+c6H8lp+v0xelgPfsJdgCNIdeowWE5keKzMrPYgCBAJqV3P5BYniaFTBbhht8jvvJEKhsWNQNTQ0ND7KjHunkZGRwf3338/GjRuJRCJcd9111NbWfmivFwgoiHor5ytKw2FQswUaGhoaGuPeaQCsXbuWtWvXXm4zNDQ0ND72fMCUAg0NDQ0NjQ9GcxoaGhoaGpeM5jQ0NDQ0NC6Zj0RO47+DKF56T8dfc+7lRrP1w0Gz9cPho2LrR8VO+PBs/UvPKyiaUJCGhoaGxiWihac0NDQ0NC4ZzWloaGhoaFwymtPQ0NDQ0LhkNKehoaGhoXHJaE5DQ0NDQ+OS0ZyGhoaGhsYlozkNDQ0NDY1LRnMaGhoaGhqXjOY0NDQ0NDQuGc1pAJs3b2bVqlUsX76c3//+95fbnDF4vV7WrFlDa2srADt37mTt2rUsX76chx9++DJbN8q//du/sXr1alavXs1DDz0EjF9bf/SjH7Fq1SpWr17NI488AoxfW8/zgx/8gAceeAAYv7bedtttrF69mvXr17N+/XoOHTo0bm19++23ueaaa1i5ciXf/e53gfH5uT711FOJz3P9+vVMmzaNb3/725fPVuVjTmdnp7J48WJlYGBA8fl8ytq1a5XTp09fbrMSHDx4UFmzZo1SVVWltLS0KIFAQFm4cKHS3NysRCIRZdOmTcrWrVsvt5nKjh07lBtvvFEJhUJKOBxWNm7cqGzevHlc2rp7925lw4YNSiQSUQKBgLJ48WLlxIkT49LW8+zcuVOZOXOm8pWvfGXcXgOyLCvz5s1TIpFI4m/j1dbm5mZl3rx5SkdHhxIOh5WbbrpJ2bp167i09ULq6+uVZcuWKe3t7ZfN1o/9TmPnzp3MmjWL1NRULBYLV155Ja+++urlNivBk08+yTe+8Y3EXPTDhw9TUFBAXl4eOp2OtWvXjgt7PR4PDzzwAAaDAb1eT0lJCefOnRuXts6YMYPf/va36HQ6+vr6iMViDA8Pj0tbAQYHB3n44Ye5++67gfF7DTQ0NACwadMm1q1bx6OPPjpubX3jjTdYtWoVmZmZ6PV6Hn74Ycxm87i09UK++c1vcv/999PS0nLZbP3YO43u7m48Hk/iOD09na6urstoUTLf+973qKurSxyPV3vLysqYPHkyAOfOneOVV15BEIRxaSuAXq/nxz/+MatXr2b27Nnj9nMF+PrXv87999+Pw6HOpR+vtg4PDzN79mx++tOf8utf/5rHH3+c9vb2cWlrU1MTsViMu+++m/Xr1/PYY4+N28/1PDt37iQYDLJy5crLauvH3mnIsowgjEoBK4qSdDzeGO/2nj59mk2bNvHlL3+ZvLy8cW3rfffdx65du+jo6ODcuXPj0tannnqKrKwsZs+enfjbeL0GpkyZwkMPPYTdbsflcnHdddfx4x//eFzaGovF2LVrFw8++CBPPPEEhw8fpqWlZVzaep7HH3+cT3ziE8DlvQb+v5+n8ZfIzMxk7969ieOenp5EKGg8kpmZSU9PT+J4PNm7b98+7rvvPv7hH/6B1atX8/77749LW8+ePUs4HKayshKz2czy5ct59dVXkSQpcc54sfXll1+mp6eH9evXMzQ0hN/vp62tbVzaunfvXiKRSMLBKYpCTk7OuLwG0tLSmD17Ni6XC4ClS5eO22sAIBwOs2fPHr7//e8Dl/c+8LHfacyZM4ddu3bR399PIBDg9ddfZ8GCBZfbrD/JpEmTaGxsTGyvX3zxxXFhb0dHB/fccw//8i//wurVq4Hxa2trayv/+I//SDgcJhwO89Zbb7Fhw4ZxaesjjzzCiy++yAsvvMB9993HkiVL+MUvfjEubR0ZGeGhhx4iFArh9Xp57rnn+PznPz8ubV28eDHbt29neHiYWCzGu+++y4oVK8alrQCnTp2isLAQi8UCXN7f1sd+p5GRkcH999/Pxo0biUQiXHfdddTW1l5us/4kRqOR73//+9x7772EQiEWLlzIihUrLrdZ/PKXvyQUCiVWQgAbNmwYl7YuXLiQw4cPc9VVVyFJEsuXL2f16tW4XK5xZ+sHMV6vgcWLF3Po0CGuuuoqZFnm5ptvZsqUKePS1kmTJnHXXXdx8803E4lEmDt3LjfddBPFxcXjzlaAlpYWMjMzE8eX8xrQJvdpaGhoaFwyH/vwlIaGhobGpaM5DQ0NDQ2NS0ZzGhoaGhoal4zmNDQ0NDQ0LhnNaWhoaGhoXDKa09DQ+BvZtGkT/f39l9sMDY3/UTSnoaHxN7Jjx47LbYKGxv84H/vmPg2Nv4WvfvWrANx+++3853/+Jw8++CAdHR1EIhFWr17N3XffTWtrK3fccQcLFy7k0KFDDA8P86UvfYlly5bxk5/8hIGBAb7+9a8DJB3fdtttpKSk0NDQwE033cRVV13F9773Perr6xMyHV/+8pfR6bSfr8b/PNpOQ0Pjb+Cf//mfAfjNb37DV7/6Va699lqeffZZnn76aXbu3MnLL78MqJ288+bN4+mnn+YLX/gCDz744CU9v8Ph4OWXX+a2227jwQcfpKqqimeffZbnn3+egYGBxOAoDY3/abSliobGf4NAIMCePXsYGhriRz/6EQB+v5+TJ09SW1uLXq9n4cKFAEycOJHBwcFLet4L5fC3bt3KkSNHePrppwEIBoN/3zehofFXoDkNDY3/BoIgoCgKjz/+OGazGYD+/n6MRiMDAwPo9XpEUUyce/HjzhOJRJKe97wwHagy2D/60Y8oKSkB1LkV40myW+PjhRae0tD4G5EkCZ1Ox+TJkxPhouHhYW666SbeeuutP/tYp9PJsWPHUBQFr9fLli1b/uS58+bN49e//jWKohAOh/n0pz/No48++nd9Lxoal4rmNDQ0/kZWrFjBbbfdxre//W0OHTrE2rVruf7661mzZg3r1q37s49dt24dLpeL5cuXc/fddzNjxow/ee7XvvY1/H4/a9euZe3atZSXl3PXXXf9vd+OhsYloancamhoaGhcMtpOQ0NDQ0PjktGchoaGhobGJaM5DQ0NDQ2NS0ZzGhoaGhoal4zmNDQ0NDQ0LhnNaWhoaGhoXDKa09DQ0NDQuGQ0p6GhoaGhccn8PxpIohBoq4cAAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(data=data_no_mv, x=\"tenure\", y=\"TotalCharges\", hue=\"Churn\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Es besteht eine rein logische, lineare Beziehung zwischen der Dauer der BetriebszugehÃ¶rigkeit und den in Rechnung gestellten Gesamtkosten. Je lÃ¤nger eine Person Kunde ist, desto mehr monatliche BetrÃ¤ge musste sie bereits zahlen."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.4.2. Kategorische Merkmale"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Abwanderung (Ziel)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ZunÃ¤chst wird die Verteilung der Zielvariablen Churn untersucht."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Abwanderung im Vergleich zur Nicht-Abwanderung')"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# produce pie chart for churn\n",
+    "# generate procentage relationship\n",
+    "churn_rate = data_no_mv.Churn.value_counts() / len(data_no_mv.Churn)\n",
+    "\n",
+    "# Plot\n",
+    "labels = 'Keine Abwanderung', 'Abwanderung'\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.pie(churn_rate, labels=labels, autopct='%.f%%')  \n",
+    "ax.set_title('Abwanderung im Vergleich zur Nicht-Abwanderung')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Die Abwanderungen machen etwa 27 % des gesamten Datensatzes aus, wÃ¤hrend die Nicht-Abwanderungen etwa 73 % ausmachen.\n",
+    "- Dies ist ein unausgewogener Datensatz und eine andere Metrik muss in der Bewertungsphase verwendet werden.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Geschlecht"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEJCAYAAABohnsfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAgFElEQVR4nO3de1iUdf7/8efAIB6wVXFGkF9fNl1XdnELVwptCzb7ekrJxFoVFN3NMstazcU1JAnLQy5qWuqvLTush5Tlp1CG5KFdfxm6Erur2fI1S/FKIRgUlUFBmJnvH9UYebpRhkF9Pa7LS+7P3Pfnfg/X5+I19+c+jMnlcrkQERExwMfbBYiIyLVDoSEiIoYpNERExDCFhoiIGKbQEBERwxQaIiJimEJDREQMM3u7AE+rqKjC6dStKCIiRvj4mGjfvs1FX7/uQ8PpdCk0REQaiaanRETEMIWGiIgYdt1PT4mIXAmXy0VFhY2zZ6uB62+K29fXTEBAO1q1uvj5iwtRaIiIXIDdfhKTyUSnTv8Hk+n6mpRxuVzU1p7lxAkbQIOC4/r6TYiINJIzZ+y0bdvuugsMAJPJRIsW/rRrZ8FuP9Ggba+/34aISCNwOh34+l7fkzF+fi1wOOoatI1CQ0TkIkwmk7dL8KgreX/Xd4yKXMfa3tSSlv5+3i6jWaiuqaXyVLW3ywDA4XDw17++w5YtH+BwOKirq+XOO+9m/PjH+NOf5nDLLV2Jjx/j7TKvmEJD5BrV0t+P+GmrvV1Gs7BmfgKVNI/QSE+fR2XlKRYvXk5AQABnzpxh1qwUXnzxeXx8fL1d3lVTaIiINJKSkmK2bNlEdnYubdoEANCqVSv+8Idn+PTTPXz88Ufs27eXxx77HcePH6NLl66kps6mVatW3HVXJBs3bqVdu3YA7uWDB79g8eIFtGrVijNnTvP447/nrbdeo3PnEA4e/JK6ujqSkpK59daIJnmPOqchItJI9u8v5JZburgD4zuBgR359a/vBcBmK+Oll5bxzjvrKSsrY/v2Dy/b76FDX/Lcc7N5++21+Pn58Z//fMbIkaN588013Hff/fz5z8s88n4uRKEhItJITCafyz7rLjr617Rs2RJfX1+6dOlKRUXFZfu1WjsRFBTsXu7UKYhu3boD0L17GKdOnby6whtAoSEi0kjCw3tw+PAhTp+uqtdus5WRlPR7ampq6l3GazKZcLnOhcx3P9fW1tbbvlWrVvWW/f396y1/vw9P8+g5jVdeeYVNmzYBEBMTw7Rp03jmmWcoKChw/xImTZpEv379KCwsZMaMGVRVVREZGUlaWhpms5ni4mKSkpI4duwYt9xyC+np6bRp07Db3q+GrlA5pzldoSLSHHXsaKFfv0HMmTOLZ555ljZtAqiqsrNgwTxuuulH+Phc/HN6u3bt+Z//+Q99+vyKLVtym7DqhvFYaOTl5bFjxw42bNiAyWRi/PjxbNmyhX379rFq1SqsVmu99ZOSknjhhReIiIggOTmZjIwM4uPjSUtLIz4+nsGDB7N06VKWLVtGUlKSp8o+j65QOac5XaEi0lxNnfpH3n57BY899jt8fc3U1p7l7rt/zcMPT2D+/NkX3W7y5D+wcOF82rYNIDIyisDAjk1YtXEml4eOaw4cOEBVVRUREREAzJo1i9DQUJYsWULPnj0pLS2lX79+TJo0iZKSEsaOHcvWrVsB+OSTT1iyZAkrVqwgKiqK3bt3YzabKSkpYfTo0Wzbts1wHceO2a/q+zQslrYKjW+tmZ+AzVbp7TLkWxqb53hibH799WGCgkIbtc/m6Ifv08fHRGBgwEXX99iRRrdu3dw/FxUVsWnTJlavXs3u3btJTU2lbdu2TJgwgczMTLp164bFYnGvb7FYKC0tpaKigoCAAMxmc712ERHxDo/fp3HgwAEmTJjAtGnT6NKlC0uXLnW/NmbMGLKysujatWu929ldLpf7BNEPb3Nv6G3vl0pMaTiLpa23SxC5oMYem2VlPpjN1/+1Qj4+Pg363Xk0NAoKCnjqqadITk5m8ODB7N+/n6KiIgYMGAB8Ew5ms5mgoCBsNpt7u/LycqxWKx06dKCyshKHw4Gvry82m+28cyGX0xjTU3KOpqeaD43N+hp7bDqdTurqnI3aZ3PkdDrr/e4uNz3lsRgtKSnhiSeeID09ncGDBwPfhMScOXM4efIktbW1rFu3jn79+hESEoK/vz8FBQUAZGdnEx0djZ+fH5GRkeTk5ACQlZVFdHS0p0oWEZHL8NiRxooVK6ipqWHevHnutpEjR/Loo48yatQo6urq6N+/P0OGDAEgPT2dlJQU7HY74eHhJCYmApCamsr06dNZvnw5wcHBLFy40FMli4jIZXgsNFJSUkhJSbngawkJCee1hYWFkZmZeV57SEgIK1eubPT6RESk4a7/szwiItJo9JRbEREDPPV0CKNPWigpKeahh+5n0aJXuP323u72Bx+M5eWXXyU4uHOj13YhCg0REQM89XSIhjxpwWw28+KLs/nLX9bSunXTPU7p+zQ9JSJyjejY0cLtt0fx8ssvnffaX/7yBqNHP0Ri4ghefnkRDofDIzUoNEREriGTJk1m9+6d5Ofvcrft2pXHjh3/n9dfX8kbb6zm6NGvyMr6fx7Zv0JDROQa0qZNAH/8Ywovvjjb/Qj2goLd/Pd/D6Bly5aYzWYGD76fgoJ8j+xfoSEico25447e9aapfvjUC5cLHI46j+xboSEicg36bprq2LFyevWKZOvWD6ipqaauro6cnHf55S8jPbJfXT0lImJAdU0ta+aff2NyY/R7Jb6bpnr66UnceefdVFZW8vDDiTgcddxxR2+GDx/RyJV+Q6EhImJA5alqr34JWXBwZzIz36vXdscdvdmx4xMAxo0bz7hx4z1eh6anRETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGG65FZExID2P2qBuYV/o/dbd7aGipNnL7nOwoUvcvz4MV54Yb67bffuXfzpT3N5++01TfrEW4WGiIgB5hb+FMxv/Psgek17Hbh0aDz22JOMHTuSHTu2c9ddMZw5c4b09Lk888yzTf6IdIWGiEgz17p1a6ZNm8HcubPo1esOXn/9/3LXXdG0atWKiRMfpqammh/9qB1JScl07hzC2rWr2LTpfXx8TPzsZ+FMmzaj0WpRaIiIXANuvz2KqKg+zJmTxuHDh1i2bAVPPPEIL764iKCgIP7xj528+OJsFi58mVWr3iIrKxcfHx/mzXsem60Mi8XaKHUoNERErhGTJk1m+PAhzJmTTlnZ1xQXH2H69Kfdr1dVVeHr60uPHrcyfnwid98dw8iRCY0WGKDQEBG5ZrRpE0BAQFuCgztjt9vp3DmEt95aA4DD4aCi4jgAc+cu4LPPPmXXrjymTn2KmTOfp2fPXo1Sgy65FRG5BoWG/phTp06xZ8+/AHj//Xd57rkZVFRUMHr0Q3Tp8hPGj3+M22+P4ssvDzTafnWkISJyDWrRogXPPz+PxYvTOXv2LK1btyElJY327dtz//3DeOSRRPz9W/Jf/xXK4MFDG22/Cg0REQPqztZ8e3ls4/fbEN9/PHqPHrfy2mt/OW+dESMSGDGi8b/7AxQaIiKGfHMD3qXvp7gR6JyGiIgYptAQERHDFBoiIhfhcrm8XYJHuVxOwNSgbRQaIiIXYDa3oKrq1HUZHC6Xi7q6Wk6cKKdFi5YN2tajJ8JfeeUVNm3aBEBMTAzTpk0jLy+PuXPnUlNTw6BBg5gyZQoAhYWFzJgxg6qqKiIjI0lLS8NsNlNcXExSUhLHjh3jlltuIT09nTZtmvYBXSJy42nf3kJFhQ27/YS3S/EIHx9fWrUKICDgRw3azmOhkZeXx44dO9iwYQMmk4nx48ezceNG0tPTWblyJcHBwUyYMIHt27cTExNDUlISL7zwAhERESQnJ5ORkUF8fDxpaWnEx8czePBgli5dyrJly0hKSvJU2SIiAPj6munYMdjbZTQ7HpueslgsTJ8+nRYtWuDn50fXrl0pKioiNDSUm2++GbPZTGxsLLm5uRw9epTq6moiIiIAiIuLIzc3l9raWvLz8xkwYEC9dhER8Q6PhUa3bt3cIVBUVMSmTZswmUxYLBb3OlarldLSUsrKyuq1WywWSktLqaioICAgALPZXK9dRES8w+M39x04cIAJEyYwbdo0fH19KSoqcr/mcrkwmUw4nU5MJtN57d/9/30/XL6cwMCAq6pf6rNY2nq7BJEL0thsGh4NjYKCAp566imSk5MZPHgwu3fvxmazuV+32WxYrVaCgoLqtZeXl2O1WunQoQOVlZU4HA58fX3d6zfEsWN2nM4rv/pBA7E+m63S2yXItzQ269PYbBw+PqZLftj22PRUSUkJTzzxBOnp6QwePBiA2267jUOHDnH48GEcDgcbN24kOjqakJAQ/P39KSgoACA7O5vo6Gj8/PyIjIwkJycHgKysLKKjoz1VsoiIXIbHjjRWrFhBTU0N8+bNc7eNHDmSefPm8eSTT1JTU0NMTAwDBw4EID09nZSUFOx2O+Hh4SQmJgKQmprK9OnTWb58OcHBwSxcuNBTJYuIyGWYXNfjnSvf0xjTU/HTVjdiRdeuNfMTNAXQjGhsnqOx2Xi8Nj0lIiLXH4WGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMcyjoWG32xkyZAhHjhwB4JlnnqF///4MHTqUoUOHsmXLFgAKCwuJi4tjwIABzJgxg7q6OgCKi4tJSEhg4MCBTJw4kaqqKk+WKyIil2EoNEpLS89r++KLLy65zZ49exg1ahRFRUXutn379rFq1Sqys7PJzs6mX79+ACQlJTFz5kw++OADXC4XGRkZAKSlpREfH09ubi49evRg2bJlRt+XiIh4wCVD48SJE5w4cYJHHnmEkydPupfLy8uZNGnSJTvOyMggNTUVq9UKwJkzZyguLiY5OZnY2FiWLFmC0+nk6NGjVFdXExERAUBcXBy5ubnU1taSn5/PgAED6rWLiIj3mC/14tSpU/n4448BiIqKOreR2ez+Y34xs2fPrrdcXl5O7969SU1NpW3btkyYMIHMzEy6deuGxWJxr2exWCgtLaWiooKAgADMZnO9dhER8Z5LhsaKFSuAb85FzJ0796p2dPPNN7N06VL38pgxY8jKyqJr166YTCZ3u8vlwmQyuf//vh8uGxEYGHDlRct5LJa23i5B5II0NpvGJUPjO3PnzuXo0aOcPHkSl8vlbg8PDze8o/3791NUVOQ+QnG5XJjNZoKCgrDZbO71ysvLsVqtdOjQgcrKShwOB76+vthsNvdUV0McO2bH6XRdfsWL0ECsz2ar9HYJ8i2Nzfo0NhuHj4/pkh+2DYXGkiVLWLFiBYGBge42k8nEtm3bDBficrmYM2cOvXv3pnXr1qxbt45hw4YREhKCv78/BQUF9OrVi+zsbKKjo/Hz8yMyMpKcnBxiY2PJysoiOjra8P5ERKTxGQqNrKwsNm/eTKdOna54R2FhYTz66KOMGjWKuro6+vfvz5AhQwBIT08nJSUFu91OeHg4iYmJAKSmpjJ9+nSWL19OcHAwCxcuvOL9i4jI1TMUGsHBwVccGB9++KH754SEBBISEs5bJywsjMzMzPPaQ0JCWLly5RXtV0REGp+h0OjTpw/z58/n3nvvpWXLlu72hpzTEBGRa5+h0Fi/fj1AvfskGnpOQ0RErn2GQuP7U0wiInLjMhQab7755gXbf/vb3zZqMSIi0rwZCo3PP//c/fPZs2fJz8+nT58+HitKRESaJ8M3931faWkpM2bM8EhBIiLSfF3Ro9E7derE0aNHG7sWERFp5hp8TsPlcrFv3756d4eLiMiNocHnNOCbm/2mTZvmkYJERKT5atA5jaNHj1JXV0doaKhHixIRkebJUGgcPnyYxx9/nLKyMpxOJ+3bt+fVV1+la9eunq5PRESaEUMnwmfNmsX48ePJz8+noKCAiRMnkpaW5unaRESkmTEUGseOHWPYsGHu5eHDh1NRUeGxokREpHkyFBoOh4MTJ064l48fP+6pekREpBkzdE5j9OjRjBgxgkGDBmEymcjJyWHs2LGerk1ERJoZQ0caMTExANTW1vLll19SWlpKv379PFqYiIg0P4aONKZPn05CQgKJiYnU1NTwzjvvkJyczGuvvebp+kREpBkxdKRRUVHh/gpWf39/xo0bh81m82hhIiLS/Bg+EV5aWupeLi8vx+VyeawoERFpngxNT40bN44HHniAu+++G5PJRF5enh4jIiJyAzIUGg8++CA9evRg165d+Pr68vDDD/PTn/7U07WJiEgzYyg0AMLCwggLC/NkLSIi0sxd0fdpiIjIjUmhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMcyjoWG32xkyZAhHjhwBIC8vj9jYWPr378+iRYvc6xUWFhIXF8eAAQOYMWMGdXV1ABQXF5OQkMDAgQOZOHEiVVVVnixXREQuw2OhsWfPHkaNGkVRUREA1dXVJCcns2zZMnJycti3bx/bt28HICkpiZkzZ/LBBx/gcrnIyMgAIC0tjfj4eHJzc+nRowfLli3zVLkiImKAx0IjIyOD1NRUrFYrAHv37iU0NJSbb74Zs9lMbGwsubm5HD16lOrqaiIiIgCIi4sjNzeX2tpa8vPzGTBgQL12ERHxHsMPLGyo2bNn11suKyvDYrG4l61WK6Wlpee1WywWSktLqaioICAgALPZXK+9oQIDA67wHciFWCxtvV2CyAVpbDYNj4XGDzmdTkwmk3vZ5XJhMpku2v7d/9/3w2Ujjh2z43Re+RdGaSDWZ7NVersE+ZbGZn0am43Dx8d0yQ/bTXb1VFBQUL2viLXZbFit1vPay8vLsVqtdOjQgcrKShwOR731RUTEe5osNG677TYOHTrE4cOHcTgcbNy4kejoaEJCQvD396egoACA7OxsoqOj8fPzIzIykpycHACysrKIjo5uqnJFROQCmmx6yt/fn3nz5vHkk09SU1NDTEwMAwcOBCA9PZ2UlBTsdjvh4eEkJiYCkJqayvTp01m+fDnBwcEsXLiwqcoVEZEL8HhofPjhh+6f+/Tpw7vvvnveOmFhYWRmZp7XHhISwsqVKz1anxjnrKvVPPq36s7WUHHyrLfLEGlyTXakIdc+H7MfBfPHe7uMZqHXtNcBhUZzoQ8053j6A41CQ0SuefpAc46nP9Do2VMiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMczsjZ2OGTOG48ePYzZ/s/tZs2ZRVVXF3LlzqampYdCgQUyZMgWAwsJCZsyYQVVVFZGRkaSlpbm3ExGRptXkf31dLhdFRUX87W9/c//xr66uZuDAgaxcuZLg4GAmTJjA9u3biYmJISkpiRdeeIGIiAiSk5PJyMggPj6+qcsWERG8MD118OBBAH73u99x//33s2rVKvbu3UtoaCg333wzZrOZ2NhYcnNzOXr0KNXV1URERAAQFxdHbm5uU5csIiLfavLQOHXqFH369GHp0qW89dZbrF27luLiYiwWi3sdq9VKaWkpZWVl9dotFgulpaVNXbKIiHyryaenevbsSc+ePd3LDz74IEuWLKFXr17uNpfLhclkwul0YjKZzmtviMDAgKsvWuQCLJa23i5B5II8OTabPDQ++eQTamtr6dOnD/BNEISEhGCz2dzr2Gw2rFYrQUFB9drLy8uxWq0N2t+xY3acTtcV16s/DHIxNlulV/evsSkXczVj08fHdMkP200+PVVZWcn8+fOpqanBbrezYcMGnn76aQ4dOsThw4dxOBxs3LiR6OhoQkJC8Pf3p6CgAIDs7Gyio6ObumQREflWkx9p3HPPPezZs4cHHngAp9NJfHw8PXv2ZN68eTz55JPU1NQQExPDwIEDAUhPTyclJQW73U54eDiJiYlNXbKIiHzLKzc8TJ48mcmTJ9dr69OnD+++++5564aFhZGZmdlElYmIyKXojnARETFMoSEiIoYpNERExDCFhoiIGKbQEBERwxQaIiJimEJDREQMU2iIiIhhCg0RETFMoSEiIoYpNERExDCFhoiIGKbQEBERwxQaIiJimEJDREQMU2iIiIhhCg0RETFMoSEiIoYpNERExDCFhoiIGKbQEBERwxQaIiJimEJDREQMU2iIiIhhCg0RETFMoSEiIoYpNERExDCFhoiIGKbQEBERwxQaIiJimEJDREQMuyZC47333uO+++6jf//+rF692tvliIjcsMzeLuBySktLWbRoEevXr6dFixaMHDmSqKgofvKTn3i7NBGRG06zD428vDx69+5Nu3btABgwYAC5ublMmjTJ0PY+PqarrqFj+zZX3cf1osVNgd4uodlojLF1tTQ2z9HYPOdqxubltjW5XC7XFffeBF599VVOnz7NlClTAPjrX//K3r17ef75571cmYjIjafZn9NwOp2YTOeSz+Vy1VsWEZGm0+xDIygoCJvN5l622WxYrVYvViQicuNq9qFx5513snPnTo4fP86ZM2fYvHkz0dHR3i5LROSG1OxPhHfq1IkpU6aQmJhIbW0tDz74ILfeequ3yxIRuSE1+xPhIiLSfDT76SkREWk+FBoiImKYQkNERAxTaIiIiGEKjRvAkSNH6NGjB0OHDq33r6SkxCP76tu3b6P3KzeeI0eO0L17d2bOnFmvvbCwkO7du7N+/fqLbtu3b1+OHDni6RJvSM3+kltpHFarlezsbG+XIdIg7dq146OPPsLhcODr6wtATk4OHTp08HJlNy6Fxg2svLycmTNn8vXXX2MymZg6dSp33nknL7/8MsXFxRQVFXH8+HEmTpzIzp072bNnD2FhYSxatAiHw8Fzzz3HgQMHKC8vp3v37ixcuNBQ/yJGtWnThrCwMPLz8+nduzcAH3/8sXscrVq1iuzsbM6cOYOfnx8LFiygS5cu7u0dDgfz589n9+7dOBwO4uLiGDdunDfeynVDoXGDKCsrY+jQoe7l2NhYPvvsM4YPH869995LWVkZ8fHxZGVlAfD555+zbt06/vnPfzJ27Fjee+89fvzjH3Pfffexf/9+Kisr8fPzY926dTidTsaOHcv27dsJDw9372P27NkX7D8gIKCp375cwwYNGsQHH3xA79692bt3L927d8flcmG32/nwww9ZuXIlLVu2ZPHixaxevZpnn33WvW1GRgYAGzZs4OzZszz88MP06NGDyMhIb72da55C4wZxoempqKgoDh48yJIlSwCoq6vjq6++AuBXv/oVZrOZzp07Y7FY3N9f0qlTJ06ePElUVBTt2rVj9erVHDx4kKKiIk6fPl2v/7y8vAv2/7Of/czTb1euI3379uWll17C6XSyadMmBg0aRE5ODgEBASxYsID333+foqIiPvroo/PG1s6dOyksLGTXrl0AnD59mv379ys0roJC4wbmdDp5++233d9VUlZWRmBgIFu3bsXPz8+9ntl8/jDZtm0bS5YsITExkbi4OCoqKvjhwwUu1r9IQ3w3RVVQUMCuXbuYOnUqOTk5lJSUMGLECEaPHk10dDQdO3aksLCw3rYOh4OkpCT69+8PwPHjx2nTRt9BcjV09dQNrHfv3qxZswaAL774gtjYWM6cOWNo2507dzJo0CCGDx/OTTfdxD/+8Q8cDkej9S/yfYMGDWLBggX06NHD/SGmdevWhIaGMm7cOH7xi1+wdevWC47BjIwMamtrqaqqIj4+nn//+99eeAfXDx1p3MBSUlKYOXMmsbGxAMyfP9/w+YaHHnqIP/zhD7z//vv4+fnxy1/+8rxLHK+mf5Hvu+eee5gxYwa///3v3W1+fn44nU7uu+8+XC4Xt99+OwcOHKi33ciRIzl8+DDDhg2jrq6OuLg4oqKimrr864oeWCgiIoZpekpERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGSDORm5vLmDFjvF2GyCUpNERExDDd3CfSQH/+85/JzMykTZs2REZGsm3bNnJzc0lPTyc/Px+Hw8HPf/5zUlJSCAgIoG/fvgwbNoydO3dSUlLC0KFDmTx5MgCLFy/mvffeo127doSGhrr3cfbs2Uv2d+utt7J//36efvpp+vXr56XfhNyIdKQh0gAfffQR69evJzMzk/Xr11NVVQV8EyS+vr6sX7+ed999F6vVSnp6unu706dPs2bNGtauXcsbb7zBV199xdatW9m8eTNZWVmsXbsWu93uXv9y/XXr1o1NmzYpMKTJ6UhDpAG2b9/OwIEDuemmmwBISEhg165d/P3vf6eyspK8vDwAamtr6z2c8d577wW+eUpwYGAgJ0+eZOfOnfTr18/9aJXhw4ezcuVKgMv2p6e0ircoNEQawGw213ua73ffJud0OklOTiYmJgaAqqoqampq3Ov5+/u7fzaZTO4+LtSXkf5at27dmG9LxDBNT4k0QExMDJs3b6ayshKAzMxMAO666y5Wr17N2bNncTqdPPvss+d9k+EPRUdHk5uby6lTp3A6nfW+7+RK+hNpCgoNkQbo06cPv/nNbxgxYgRxcXFUVlbSqlUrHn/8cUJCQhg2bJj7qavTp0+/ZF8xMTEMHz6c4cOH89BDD9G2bVv3a1fSn0hT0FNuRRrg008/5V//+heJiYkAvPnmm+zZs4eXXnrJu4WJNBGFhkgD2O12kpOTOXjwICaTieDgYJ5//nk6derk7dJEmoRCQ0REDNM5DRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImLY/wLqjLpwL4cdqQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"gender\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die Abwanderungsrate zwischen MÃ¤nnern und Frauen ist ungefÃ¤hr gleich hoch."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Senioren"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"SeniorCitizen\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bei Kunden, die als Senioren eingestuft werden, ist die Wahrscheinlichkeit hÃ¶her, dass sie abwandern."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Partner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"Partner\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kunden, die keinen Partner haben, sind eher bereit, abzuwandern."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### AngehÃ¶rige"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"Dependents\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kunden, die Verwandte haben, sind eher bereit, abzuwandern."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mehrere AnschlÃ¼sse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"MultipleLines\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bei Kunden, die mehrere AnschlÃ¼sse haben, ist die Wahrscheinlichkeit einer Abwanderung  geringer."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Internet Service"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"InternetService\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wenn ein Kunde einen Glasfaseranschluss hat, ist es wahrscheinlicher, dass er ausfÃ¤llt als ein Kunde mit DSL."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Online-Sicherheit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"OnlineSecurity\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kunden, die den Internet-Sicherheitsdienst nicht nutzen, werden eher abwandern."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Online Backup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"OnlineBackup\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Personen, die keine Online-Datensicherung nutzen, sind eher bereit, umzuziehen."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GerÃ¤teschutz\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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a8fm4ceOIjo4mPDwci8VSZdpfD5vRtKnvbyu4jvPza+jqEuQ30Parv67VbVerofH9998zfvx4xowZw7hx44CzF7t37drFiBEjADAMA6vVSpMmTSgqKqKyshJ3d3dycnIcp7qq4+TJYux247Lj1dcdICenyNUl1AnafvVbfdx+V+u2c3OzXPKP7Vq75ba4uJiHH36YadOmOQIDwNvbmyVLlnD8+HEMw2DDhg2Ehobi4eFBUFAQ27ZtAyA1NZWQkJDaKldERC6g1kIjJSWF3NxcXn75ZcettcuWLaNJkybEx8czceJEBgwYgGEYPPTQQwDExsaSnJzMwIED2b9/P9OnT6+tckVE5AKcfnpq+/btAIwdO5axY8decJz+/fs77pI6V2BgIOvWrXNmefWavcJW7w7rK8rLyD9V7uoyROQK1fqFcKk5blYP0hePd3UZ1dJ15hpAoSFSX6kbERERMU2hISIipik0RETENIWGiIiYptAQERHTFBoiImKaQkNERExTaIiIiGkKDRERMU2hISIippkKjQu9/Oi7776r8WJERKRuu2RoFBQUUFBQwCOPPMKpU6ccw7m5uUyePLm2ahQRkTrikh0WPvHEE+zcuRPA8ZY9AKvVesFeaUVE5Op2ydBYu3YtALNnz+bZZ5+tlYJERKTuMtU1+rPPPktGRganTp3CMP776tQOHTo4rTAREal7TIXG8uXLWbt2LU2bNnW0WSwWPvroI6cVJiIidY+p0EhNTeX999+nWbNmzq5HRETqMFO33DZv3lyBISIi5kIjODiYxYsXk56ezldffeX473KKi4sZPHgwJ06cAGDXrl2EhYXRr18/li5d6hjv0KFDDBs2jP79+zNnzhwqKioAyMzMJCIiggEDBjBx4kRKSkquZB1FRKSGmAqNTZs2kZaWRlRUFFOmTGHKlClMnTr1ktMcOHCA0aNHc/ToUQBKS0uJjo4mKSmJbdu2cfDgQT755BMAoqKiePrpp3nvvfcwDIPk5GQA4uLiCA8PJy0tjY4dO5KUlPQbVlVERH4rU6Gxffv28/673EXw5ORkYmNj8ff3B+CLL77gpptuomXLllitVsLCwkhLSyMjI4PS0lI6d+4MwLBhw0hLS8Nms7Fv3z7H8yC/tIuIiOuYuhD+8ssvX7D9oYceuug08+fPrzKcnZ2Nn5+fY9jf35+srKzz2v38/MjKyiI/Px9fX1+sVmuV9upq2tS32tOIc/n5NXR1CXWGvov661rddqZC4z//+Y/j5/Lycvbt20dwcHC1FmS327FYLI5hwzCwWCwXbf/l/+f69bAZJ08WY7cblx3vWt0BXCEnp6jG51lft58zvov6qD5uv6t127m5WS75x7bph/vOlZWVxZw5c6pVSEBAADk5OY7hnJwc/P39z2vPzc3F39+fJk2aUFRURGVlJe7u7o7xRUTEda6oa/RmzZqRkZFRrWn++Mc/cuTIEY4dO0ZlZSVvv/02ISEhBAYG4uXlRXp6OgBbtmwhJCQEDw8PgoKC2LZtG3D2WZGQkJArKVdERGpIta9pGIbBwYMHqzwdboaXlxcLFy5kypQplJWV0atXLwYMGABAQkICMTExFBcX06FDByIjIwGIjY1l1qxZrFy5kubNm5OYmFitZYqISM2q9jUNOPuw38yZM00tYPv27Y6fg4OD2bp163njtG/fnpSUlPPaAwMDWbdunanliIiI81XrmkZGRgYVFRXcdNNNTi1KRETqJlOhcezYMf7yl7+QnZ2N3W6ncePGrFq1ijZt2ji7PhERqUNMXQiPj49n/Pjx7Nu3j/T0dCZOnEhcXJyzaxMRkTrGVGicPHmSoUOHOoaHDx9Ofn6+04oSEZG6yVRoVFZWUlBQ4BjOy8tzVj0iIlKHmbqm8cADD3D//fdzzz33YLFY2LZtGw8++KCzaxMRkTrG1JFGr169ALDZbHz//fdkZWURGhrq1MJERKTuMXWkMWvWLCIiIoiMjKSsrIzXXnuN6OhoVq9e7ez6RESkDjF1pJGfn+94StvLy4uxY8dW6S9KRESuDaYvhJ/bLXlubi6GcfmeY0VE5Opi6vTU2LFjue+++7jrrruwWCzs2rXLdDciIiJy9TAVGiNGjKBjx47s2bMHd3d3Hn74Ydq1a+fs2kREpI4xFRpwtlPB9u3bO7MWERGp467ofRoiInJtUmiIiIhpCg0RETFNoSEiIqYpNERExDTTd0/VlDfeeIP169c7hk+cOMGQIUM4c+YM6enp+Pj4ADB58mRCQ0M5dOgQc+bMoaSkhKCgIOLi4rBaa71sERHBBaExcuRIRo4cCcC3337LpEmTmDx5Mg8++CDr16/H39+/yvhRUVHMmzePzp07Ex0dTXJyMuHh4bVdtoiI4OLTU3PnzuXxxx/Hx8eHzMxMoqOjCQsLY/ny5djtdjIyMigtLaVz584ADBs2jLS0NFeWLCJyTXNZaOzatYvS0lLuuececnNz6d69OwsWLCA5OZn9+/eTkpJCdnY2fn5+jmn8/Pyq9IElIiK1y2UXB15//XUeeughAFq2bMmKFSscn40ZM4bU1FTatGmDxWJxtBuGUWXYjKZNfWumYKkxfn4NXV1CnaHvov66VredS0KjvLycffv2sXDhQgAOHz7M0aNH6d+/P3A2HKxWKwEBAVW6YM/NzT3vmsflnDxZjN1++R55r9UdwBVycopqfJ71dfs547uoj+rj9rtat52bm+WSf2y75PTU4cOHufnmm7nuuuuAsyGxYMECTp06hc1mY+PGjYSGhhIYGIiXlxfp6ekAbNmyhZCQEFeULCIiuOhI4/jx4wQEBDiG27dvz6OPPsro0aOpqKigX79+DB48GICEhARiYmIoLi6mQ4cOjpdBiYhI7XNJaAwcOJCBAwdWaYuIiCAiIuK8cdu3b09KSkptlSYiIpegJ8JFRMQ0hYaIiJim0BAREdMUGiIiYppCQ0RETFNoiIiIaQoNERExTaEhIiKmKTRERMQ0hYaIiJim0BAREdMUGiIiYppCQ0RETFNoiIiIaQoNERExTaEhIiKmKTRERMQ0l7y5T0TAXmHDz6+hq8swraK8jPxT5a4uQ1xMoSHiIm5WD9IXj3d1GaZ1nbkGUGhc61wSGmPGjCEvLw+r9ezi4+PjKSkp4dlnn6WsrIx77rmHxx9/HIBDhw4xZ84cSkpKCAoKIi4uzjGdiIjUrlr/7WsYBkePHuVf//qX45d/aWkpAwYMYN26dTRv3pzHHnuMTz75hF69ehEVFcW8efPo3Lkz0dHRJCcnEx4eXttli4gILrgQ/sMPPwAwbtw47r33XtavX88XX3zBTTfdRMuWLbFarYSFhZGWlkZGRgalpaV07twZgGHDhpGWllbbJYuIyM9q/UijsLCQ4OBgnnrqKWw2G5GRkYwfPx4/Pz/HOP7+/mRlZZGdnV2l3c/Pj6ysrGotr2lT3xqrXWpGfbr4K1Vp2/3Xtfpd1HpodOnShS5dujiGR4wYwfLly+nataujzTAMLBYLdrsdi8VyXnt1nDxZjN1uXHa8a3UHcIWcnKIan6e2X+1wxraD+rn9nPVduJqbm+WSf2zX+ump/fv3s3v3bsewYRgEBgaSk5PjaMvJycHf35+AgIAq7bm5ufj7+9dqvSIi8l+1HhpFRUUsXryYsrIyiouL2bx5MzNmzODIkSMcO3aMyspK3n77bUJCQggMDMTLy4v09HQAtmzZQkhISG2XLCIiP6v101N9+vThwIED3HfffdjtdsLDw+nSpQsLFy5kypQplJWV0atXLwYMGABAQkICMTExFBcX06FDByIjI2u7ZBER+ZlLHniYPn0606dPr9IWHBzM1q1bzxu3ffv2pKSk1FJlIiJyKep7SkRETFNoiIiIaQoNERExTaEhIiKmKTRERMQ0hYaIiJim0BAREdMUGiIiYppCQ0RETFNoiIiIaQoNERExTaEhIiKmKTRERMQ0hYaIiJim0BAREdMUGiIiYppCQ0RETFNoiIiIaS553esLL7zAu+++C0CvXr2YOXMms2fPJj09HR8fHwAmT55MaGgohw4dYs6cOZSUlBAUFERcXBxWq0vKFhG55tX6b99du3axY8cONm/ejMViYfz48XzwwQccPHiQ9evX4+/vX2X8qKgo5s2bR+fOnYmOjiY5OZnw8PDaLltERHDB6Sk/Pz9mzZqFp6cnHh4etGnThszMTDIzM4mOjiYsLIzly5djt9vJyMigtLSUzp07AzBs2DDS0tJqu2QREflZrR9ptG3b1vHz0aNHeffdd9mwYQN79+4lNjaWhg0b8thjj5GSkkLbtm3x8/NzjO/n50dWVlZtlywiIj9z2cWBb7/9lscee4yZM2fSunVrVqxY4fhszJgxpKam0qZNGywWi6PdMIwqw2Y0bepbYzVLzfDza+jqEuQKadv917X6XbgkNNLT05k6dSrR0dEMGjSIw4cPc/ToUfr37w+cDQer1UpAQAA5OTmO6XJzc8+75nE5J08WY7cblx3vWt0BXCEnp6jG56ntVzucse2gfm4/Z30XrubmZrnkH9u1fk3jxx9/ZNKkSSQkJDBo0CDgbEgsWLCAU6dOYbPZ2LhxI6GhoQQGBuLl5UV6ejoAW7ZsISQkpLZLFhGRn9X6kcbatWspKytj4cKFjrZRo0bx6KOPMnr0aCoqKujXrx+DBw8GICEhgZiYGIqLi+nQoQORkZG1XbKIiPys1kMjJiaGmJiYC34WERFxXlv79u1JSUlxdlkiImKCnggXERHTFBoiImKaQkNERExTaIiIiGkKDRERMU2hISIipik0RETENIWGiIiYptAQERHTFBoiImKa3psqIlJN9gpbveuZt6K8jPxT5b95PgoNEZFqcrN6kL54vKvLqJauM9cAvz00dHpKRERMU2iIiIhpCg0RETFNoSEiIqYpNERExDSFhoiImKbQEBER0+pFaLz11lsMHDiQfv36sWHDBleXIyJyzarzD/dlZWWxdOlSNm3ahKenJ6NGjaJbt27ccsstri5NROSaU+dDY9euXXTv3p1GjRoB0L9/f9LS0pg8ebKp6d3cLKaXdUPjBldSokt5Xt/U1SVUW3W2SXVo+zmfs7Yd1L/tV9+2HZjbfpcbx2IYhlFTBTnDqlWrOH36NI8//jgAb7zxBl988QXPPPOMiysTEbn21PlrGna7HYvlv8lnGEaVYRERqT11PjQCAgLIyclxDOfk5ODv7+/CikRErl11PjTuvPNOdu/eTV5eHmfOnOH9998nJCTE1WWJiFyT6vyF8GbNmvH4448TGRmJzWZjxIgRdOrUydVliYhck+r8hXAREak76vzpKRERqTsUGiIiYppCQ0RETFNoiIiIaQqNeuDEiRPceuut7Ny5s0r73XffzYkTJ1xUlVxKfHw8U6dOrdK2Y8cO+vbtS3FxsYuqqntqYt/OysrikUceueQ4X3zxBUuWLLniOs0oKipi0qRJTl3GLz766COWLVtWK8v6NYVGPeHh4cFTTz2lXzj1xBNPPMHBgwf56KOPADh9+jRz585lwYIF+Pr6uri6uuW37tvNmjVj9erVlxznu+++4+TJk1c0f7NOnTrFoUOHnLqMX/Tt25dp06bVyrJ+rc4/pyFn+fv7c+edd7Jo0aLz+t168cUX2bp1K+7u7vTo0YOoqCjc3d1dVKkANGjQgHnz5hEdHU1wcDDLly/n7rvvxsfHh9GjR1NaWkrjxo2Ji4ujZcuWvPzyy2zevBk3Nzc6depEfHy8q1eh1lxq34bL798nTpwgMjKS7du3M2vWLHx9ffnqq6/Iyspi0qRJhIaGsnz5ck6fPs3KlSt59NFHWbx4MXv37qWyspJhw4YxduxYPvvsM5YsWYLdbqdt27bceOONZGVlcezYMTIyMhg5ciQTJ06ksrLygtPPmzeP7OxsJk2axIoVKxz1FRcXM2PGDHJzcwGYNGkSffv25dixY8ydO5eCggK8vb156qmnuP3225k1axYFBQUcO3aMJ554gpSUFF588UUA1q1bx7Fjx7j99tvZu3cvCxcuZNeuXSxcuBDDMGjRogXPPfccPj4+F6yxRhhS5x0/ftzo06ePUVRUZPTu3dvYsWOHYRiG0adPH2PDhg3GyJEjjdOnTxs2m82YMGGCsX79ehdXLL+YM2eOMXXqVGPw4MFGYWGhERYWZmRkZBiGYRiffvqp8eCDDxoVFRVGt27djPLycqOystKYNWuW8dNPP7m48tpxqX37+PHjxscff3zZ/fuXeRiGYTz55JPGpEmTDLvdbnzzzTfGHXfcYRiGYbz55pvGk08+aRiGYbz66qvGggULDMMwjLKyMuOBBx4w9u3bZ+zZs8fo2rWrUVhYaBiGYSxfvtwYMWKEUVZWZuTm5hqdO3c2Tp06ddHpz63jXJs2bTLmzp1rGIZhfP3118bChQsNwzCM+++/3/jqq68MwzCMb7/91ujXr59jHX6ptby83OjRo4dRUFDgmObAgQOO9SkrKzOCg4ONr7/+2jAMw0hISDD++c9/XrTGmqAjjXrE19eXZ555hqeeeoqtW7cCsGfPHgYNGoSPjw8Aw4cPJzU1lYiICFeWKj+bNWsWvXv3ZsWKFfz4448cP36ciRMnOj4vLi7G3d2dLl26MGLECPr27ctDDz1Es2bNXFh17bvQvg1Xtn/36NEDi8VCu3btKCgoOO/z3bt3c+jQIfbs2QOcPXV4+PBhbrnlFlq1akXDhg0d43br1g1PT0+aNm1Ko0aNKCoquuj0AQEBF6ynS5cuJCYmkpWVRe/evZk0aRIlJSUcPHiQ2bNnO8Y7ffo0+fn5AI5eLzw8PAgNDeX999+nR48eFBQU0KlTJ7777jsADh8+TLNmzbjtttuAs6dFAaZOnXrBGoOCgi76vZml0Khnevbs6TiUh7O9AP9aRUVFbZclF+Hr68v1119PYGAgxcXF3HjjjWzZsgWAyspKxymLpKQkPv/8cz799FPGjx9PQkICd9xxhytLr3W/3rfhyvZvLy8vgIv2hl1ZWUlUVBT9+vUDIC8vjwYNGvD555/j7e19wXn9Mj/DMC46/bkdq57r5ptv5t133+Xf//43//rXv/j73//OG2+8gaenp2NfAPjpp58c7w06t44hQ4awbNkyTp06RVhYWJV5e3h4VFnPoqIiSkpKLlpjTdCF8Hpo1qxZ7Nixg+zsbLp3784777xDaWkpFRUVvPnmm3Tv3t3VJcoFtG7dmlOnTrF//34A3nzzTf7617+Sl5fHwIEDadeuHdOmTaNHjx4cPnzYxdW6xrn7NlBj+7e7u7sjbLp3705ycjI2m42SkhLCw8P5/PPPTc/rYtNbrdYLBtr69et5/vnnueeee4iNjSUvLw/DMLj55psdobFz586LHj117tyZ7OxstmzZwr333lvls1atWnHy5EnHkceaNWt47bXXfvM6XoqONOqhXw7lH374YXr37k1hYSHDhw+noqKCnj178sADD7i6RLkAT09Pli1bxvz58ykrK8PX15dFixbRpEkT7r//fkaMGIGPjw+tWrVi+PDhri7XJc7dtwH69OnDoUOHfvP+3alTJ1544QUSEhKYNm0ax44dY+jQoVRUVDBs2DC6devGZ599Zmpeo0aNuuD0NpuNFi1aMGbMGNatW+cY/7777mPGjBmEhYXh7u5OVFQU119/PUuWLGHu3LmsWbMGDw8Pli5detGjo3vuuYcdO3bQsmXLKu1eXl4sWbKEmTNnYrPZ+P3vf8/ixYvx9PS8YI01QR0WioiIaTo9JSIipik0RETENIWGiIiYptAQERHTFBoiImKabrmVq8KJEycIDQ2lXbt2wNmHwho0aEBkZCQDBw68onk+8sgjPPnkk9xyyy3VnnbTpk3Mnz+fG2+80fFQmI+PD08++SRdunSp1rxeeOEF2rdvz5///Odq1wHwxhtvUF5eTkREBK+99hpFRUU8+uijVzQvEYWGXDW8vb2rPGGbkZHB2LFjcXd3p3///tWe3+V6Tr2coKAgVq1a5Rjevn07U6ZM4eOPP8ZqNf9P77PPPrui4PpFeno6bdu2BWD06NFXPB8RUGjIVSwwMJCpU6eydu1a+vTpQ0JCAvv27aOyspLbb7+dmJgYPv/8cxYtWsRbb70FQGFhIX379uXDDz9k6NChLFu2jD/84Q+kpKTw8ssv4+bmRuPGjVm0aBHNmzdn+/btrFy5EpvNhre39yWPJIKDg8nJyaGwsJDFixdTUFDA8ePH6d27NxMmTCAuLo5vvvkGi8XCXXfdxYwZM9i4cSMHDx5k8eLFuLu706tXrwuuh6+vL0eOHOHpp58mLy8PNzc3Jk6ciIeHB9u3b2fnzp14e3uTl5dHfn4+Tz/9NN9++y3x8fEUFBRgsVgYN24c9913H5999hlLly6lZcuWfPvtt1RUVBAXF0fXrl1rc/NJHaVrGnJVa9++Pf/5z3946aWXcHd3Z9OmTWzduhV/f38SEhLo0aMHJSUlfPnllwC8/fbb9OrVi9/97neOeXzzzTckJCSwZs0a3nrrLe6++25WrlzJ0aNHWbp0KS+99BKpqak888wzTJkyhdOnT59Xh2EYbNy4kXbt2tGkSRMASktLeeedd4iKimLevHk0atSIt956izfffJPDhw/z97//nYiICDp27MjMmTMJDQ296HoAzJgxgwEDBvDOO+/w0ksvkZiYSHBwMHfffTdjx46t0k1FRUUFEydOZMyYMbz11lusXr2axMRE/vd//xc4+9KicePGkZqayrBhw1i6dKnTtpHULzrSkKuaxWLB29ubjz/+mKKiInbt2gWAzWajadOmWCwWhg8fzubNm/nDH/7Apk2bmDlzZpV57N69m549e9K8eXMAx3sJNmzYQHZ2dpX3FFgsFv7v//4PgP379zNkyBAsFgvl5eW0bt2a5cuXO8Y99y/3Tz/9lNdeew2LxYKnpyejRo3ilVdeOe/aw8XWo6CggG+++YaRI0cC0Lx5cz788MOLfi9Hjx6lrKzM0aFds2bN6NevH//+97/p1q0bLVq0cPScevvtt7N582ZzX7hc9RQaclX78ssvadeuHcXFxURHR9OrVy8ASkpKKCsrA2DEiBEMHTqUkSNHUlRUdF7vsu7u7lX6BCotLSUjIwO73U5wcDB/+9vfHJ/9+OOP+Pv78/XXX593TePXrrvuOsfPdru9yjLsdvsFO7+z2+0XXI9frpGcO48ffviBFi1aXHDZlZWV5/VzZBiGY5nn9rL6y4V8EdDpKbmKHTlyhKSkJMaNG0fPnj3ZsGED5eXl2O12nnrqKRITE4Gzf2V36tSJp59+mhEjRpw3n27durF7925Hz6uvv/46S5YsITg4mJ07d/L9998D8Mknn3DvvfdSWlpa7Vp79uzJ+vXrMQyD8vJykpOTufPOO4GqPbRebD18fX3p0KEDqampwNnwGj16NEVFRVWm/0Xr1q2xWq28//77wNn3bL/33nuOZYpcjI405KpRWlrKkCFDAHBzc8PLy4sZM2bQu3dvunfvzqJFixg6dCiVlZXcdtttzJo1yzHtyJEjmTZtGitXrjxvvrfeeitRUVGMHz8eAD8/PxYsWECzZs2Ij49nxowZGIaB1Wpl5cqVV/TegpiYGObNm0dYWBg2m4277rqLCRMmAHD33XeTmJiIzWbjL3/5y0XX47nnniMuLo5169ZhsViYP38+fn5+hISEsHDhwirL8/DwICkpiXnz5vH8889TWVnJpEmT6N69u+neXuXapF5uRUTENJ2eEhER0xQaIiJimkJDRERMU2iIiIhpCg0RETFNoSEiIqYpNERExDSFhoiImPb/AVaAPrx8zLhVAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"DeviceProtection\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kunden, die keinen zusÃ¤tzlichen GerÃ¤teschutz erworben haben, werden mit grÃ¶ÃŸerer Wahrscheinlichkeit migrieren."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Technischer Support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"TechSupport\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kunden, die keinen technischen Support in Anspruch nehmen, werden eher abwandern."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Streaming-TV/ Streaming-Filme"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for col in [\"StreamingTV\", \"StreamingMovies\"]:\n",
+    "    sns.countplot(x=col, hue='Churn', data=data_no_mv)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die Hinzunahme von Film- und TV-Streaming-Angeboten hat kaum Auswirkungen auf die Abwanderungsrate."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Papierlose Abrechnung"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"PaperlessBilling\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kunden, die ohne Rechnung bezahlen, werden eher abwandern."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Zahlungsmethode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"PaymentMethod\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kunden, die mit elektronischen Schecks bezahlen, wandern deutlich hÃ¤ufiger ab als Kunden, die eine andere Zahlungsmethode verwenden."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vertrag"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x=\"Contract\", hue=\"Churn\", data=data_no_mv)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bei Kunden mit kurzfristigen VertrÃ¤gen ist die Wahrscheinlichkeit grÃ¶ÃŸer, dass sie das Unternehmen verlassen, als bei Kunden mit lÃ¤ngerfristigen VertrÃ¤gen."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "DataPreparation"
+   },
+   "source": [
+    "# 3. Aufbereitung der Daten"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "DataPreparation"
+   },
+   "source": [
+    "## 3.1. Reduzieren der Kunden-ID"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Removing the Customer ID, it does not add value to the model\n",
+    "data_prep = data_no_mv.drop(\"customerID\", axis = 1)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "DataPreparation"
+   },
+   "source": [
+    "## 3.2. Umkodierung der kategorialen Variablen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert binary variables to 1 and 0 with Yes and No\n",
+    "bin_var = [\"Partner\",\"Dependents\",\"PhoneService\",\"PaperlessBilling\",\"Churn\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def binaer_umwandeln(x):\n",
+    "    return x.map({'Yes':1,'No':0})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>SeniorCitizen</th>\n",
+       "      <th>Partner</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>PhoneService</th>\n",
+       "      <th>MultipleLines</th>\n",
+       "      <th>InternetService</th>\n",
+       "      <th>OnlineSecurity</th>\n",
+       "      <th>OnlineBackup</th>\n",
+       "      <th>DeviceProtection</th>\n",
+       "      <th>TechSupport</th>\n",
+       "      <th>StreamingTV</th>\n",
+       "      <th>StreamingMovies</th>\n",
+       "      <th>Contract</th>\n",
+       "      <th>PaperlessBilling</th>\n",
+       "      <th>PaymentMethod</th>\n",
+       "      <th>MonthlyCharges</th>\n",
+       "      <th>TotalCharges</th>\n",
+       "      <th>Churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No phone service</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Electronic check</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Mailed check</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>108.15</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No phone service</td>\n",
+       "      <td>DSL</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Bank transfer (automatic)</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Fiber optic</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Electronic check</td>\n",
+       "      <td>70.70</td>\n",
+       "      <td>151.65</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
+       "0  Female              0        1           0       1             0   \n",
+       "1    Male              0        0           0      34             1   \n",
+       "2    Male              0        0           0       2             1   \n",
+       "3    Male              0        0           0      45             0   \n",
+       "4  Female              0        0           0       2             1   \n",
+       "\n",
+       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
+       "0  No phone service             DSL             No          Yes   \n",
+       "1                No             DSL            Yes           No   \n",
+       "2                No             DSL            Yes          Yes   \n",
+       "3  No phone service             DSL            Yes           No   \n",
+       "4                No     Fiber optic             No           No   \n",
+       "\n",
+       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
+       "0               No          No          No              No  Month-to-month   \n",
+       "1              Yes          No          No              No        One year   \n",
+       "2               No          No          No              No  Month-to-month   \n",
+       "3              Yes         Yes          No              No        One year   \n",
+       "4               No          No          No              No  Month-to-month   \n",
+       "\n",
+       "   PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  \\\n",
+       "0                 1           Electronic check           29.85         29.85   \n",
+       "1                 0               Mailed check           56.95       1889.50   \n",
+       "2                 1               Mailed check           53.85        108.15   \n",
+       "3                 0  Bank transfer (automatic)           42.30       1840.75   \n",
+       "4                 1           Electronic check           70.70        151.65   \n",
+       "\n",
+       "   Churn  \n",
+       "0      0  \n",
+       "1      0  \n",
+       "2      1  \n",
+       "3      0  \n",
+       "4      1  "
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_prep[bin_var]=data_prep[bin_var].apply(binaer_umwandeln)\n",
+    "data_prep.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SeniorCitizen</th>\n",
+       "      <th>Partner</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>PhoneService</th>\n",
+       "      <th>PaperlessBilling</th>\n",
+       "      <th>MonthlyCharges</th>\n",
+       "      <th>TotalCharges</th>\n",
+       "      <th>Churn</th>\n",
+       "      <th>gender_Male</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TechSupport_Yes</th>\n",
+       "      <th>StreamingTV_No internet service</th>\n",
+       "      <th>StreamingTV_Yes</th>\n",
+       "      <th>StreamingMovies_No internet service</th>\n",
+       "      <th>StreamingMovies_Yes</th>\n",
+       "      <th>Contract_One year</th>\n",
+       "      <th>Contract_Two year</th>\n",
+       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
+       "      <th>PaymentMethod_Electronic check</th>\n",
+       "      <th>PaymentMethod_Mailed check</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>108.15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>70.70</td>\n",
+       "      <td>151.65</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   SeniorCitizen  Partner  Dependents  tenure  PhoneService  PaperlessBilling  \\\n",
+       "0              0        1           0       1             0                 1   \n",
+       "1              0        0           0      34             1                 0   \n",
+       "2              0        0           0       2             1                 1   \n",
+       "3              0        0           0      45             0                 0   \n",
+       "4              0        0           0       2             1                 1   \n",
+       "\n",
+       "   MonthlyCharges  TotalCharges  Churn  gender_Male  ...  TechSupport_Yes  \\\n",
+       "0           29.85         29.85      0            0  ...                0   \n",
+       "1           56.95       1889.50      0            1  ...                0   \n",
+       "2           53.85        108.15      1            1  ...                0   \n",
+       "3           42.30       1840.75      0            1  ...                1   \n",
+       "4           70.70        151.65      1            0  ...                0   \n",
+       "\n",
+       "   StreamingTV_No internet service  StreamingTV_Yes  \\\n",
+       "0                                0                0   \n",
+       "1                                0                0   \n",
+       "2                                0                0   \n",
+       "3                                0                0   \n",
+       "4                                0                0   \n",
+       "\n",
+       "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
+       "0                                    0                    0   \n",
+       "1                                    0                    0   \n",
+       "2                                    0                    0   \n",
+       "3                                    0                    0   \n",
+       "4                                    0                    0   \n",
+       "\n",
+       "   Contract_One year  Contract_Two year  \\\n",
+       "0                  0                  0   \n",
+       "1                  1                  0   \n",
+       "2                  0                  0   \n",
+       "3                  1                  0   \n",
+       "4                  0                  0   \n",
+       "\n",
+       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
+       "0                                      0                               1   \n",
+       "1                                      0                               0   \n",
+       "2                                      0                               0   \n",
+       "3                                      0                               0   \n",
+       "4                                      0                               1   \n",
+       "\n",
+       "   PaymentMethod_Mailed check  \n",
+       "0                           0  \n",
+       "1                           1  \n",
+       "2                           1  \n",
+       "3                           0  \n",
+       "4                           0  \n",
+       "\n",
+       "[5 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create dummies\n",
+    "data_enc = pd.get_dummies(data_prep, drop_first=True)\n",
+    "data_enc.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dropping of dummies that also contain No phone service and No Internet service\n",
+    "dup_variables = [\"OnlineSecurity_No internet service\",\"OnlineBackup_No internet service\", \"TechSupport_No internet service\",\"StreamingTV_No internet service\",\"StreamingMovies_No internet service\", \"DeviceProtection_No internet service\",\"MultipleLines_No phone service\"]\n",
+    "data_enc.drop(dup_variables, axis=1, inplace=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "DataPreparation"
+   },
+   "source": [
+    "## 3.3. Test auf MultikollinearitÃ¤t"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "DataPreparation"
+   },
+   "source": [
+    "Um ein korrektes Funktionieren der spÃ¤teren Regression zu gewÃ¤hrleisten, darf keine MultikollinearitÃ¤t zwischen den Variablen bestehen. Das Vorhandensein einer solchen wird mit Hilfe der Bibliothek Statsmodel Ã¼berprÃ¼ft."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                  feature         VIF\n",
+      "0                           SeniorCitizen    1.376564\n",
+      "1                                 Partner    2.824725\n",
+      "2                              Dependents    1.969391\n",
+      "3                                  tenure   20.482153\n",
+      "4                            PhoneService   47.244378\n",
+      "5                        PaperlessBilling    2.956951\n",
+      "6                          MonthlyCharges  212.353073\n",
+      "7                            TotalCharges   21.374002\n",
+      "8                             gender_Male    2.021331\n",
+      "9                       MultipleLines_Yes    2.861614\n",
+      "10            InternetService_Fiber optic   17.695260\n",
+      "11                     InternetService_No    8.234451\n",
+      "12                     OnlineSecurity_Yes    2.682712\n",
+      "13                       OnlineBackup_Yes    2.909898\n",
+      "14                   DeviceProtection_Yes    2.992570\n",
+      "15                        TechSupport_Yes    2.758343\n",
+      "16                        StreamingTV_Yes    4.928957\n",
+      "17                    StreamingMovies_Yes    5.090603\n",
+      "18                      Contract_One year    2.056188\n",
+      "19                      Contract_Two year    3.487502\n",
+      "20  PaymentMethod_Credit card (automatic)    1.984196\n",
+      "21         PaymentMethod_Electronic check    2.955994\n",
+      "22             PaymentMethod_Mailed check    2.383290\n"
+     ]
+    }
+   ],
+   "source": [
+    "# independent variables\n",
+    "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
+    "  \n",
+    "# VIF dataframe \n",
+    "vif_data = pd.DataFrame() \n",
+    "vif_data[\"feature\"] = vif_test.columns \n",
+    "  \n",
+    "# VIF for each Feature \n",
+    "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
+    "                          for i in range(len(vif_test.columns))] \n",
+    "  \n",
+    "print(vif_data)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\"MonthlyCharges\" hat den hÃ¶chsten VIF und wird aus dem Datensatz entfernt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_enc.drop(\"MonthlyCharges\", axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                  feature        VIF\n",
+      "0                           SeniorCitizen   1.366018\n",
+      "1                                 Partner   2.817414\n",
+      "2                              Dependents   1.961947\n",
+      "3                                  tenure  17.073930\n",
+      "4                            PhoneService   9.277446\n",
+      "5                        PaperlessBilling   2.796488\n",
+      "6                            TotalCharges  18.028499\n",
+      "7                             gender_Male   1.942509\n",
+      "8                       MultipleLines_Yes   2.514269\n",
+      "9             InternetService_Fiber optic   4.186492\n",
+      "10                     InternetService_No   3.473225\n",
+      "11                     OnlineSecurity_Yes   1.986701\n",
+      "12                       OnlineBackup_Yes   2.182678\n",
+      "13                   DeviceProtection_Yes   2.299462\n",
+      "14                        TechSupport_Yes   2.099655\n",
+      "15                        StreamingTV_Yes   2.749724\n",
+      "16                    StreamingMovies_Yes   2.771330\n",
+      "17                      Contract_One year   2.056169\n",
+      "18                      Contract_Two year   3.468149\n",
+      "19  PaymentMethod_Credit card (automatic)   1.820729\n",
+      "20         PaymentMethod_Electronic check   2.535918\n",
+      "21             PaymentMethod_Mailed check   1.982063\n"
+     ]
+    }
+   ],
+   "source": [
+    "# the independent variables set \n",
+    "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
+    "  \n",
+    "# VIF dataframe \n",
+    "vif_data = pd.DataFrame() \n",
+    "vif_data[\"feature\"] = vif_test.columns \n",
+    "  \n",
+    "# VIF for each Feature \n",
+    "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
+    "                          for i in range(len(vif_test.columns))] \n",
+    "  \n",
+    "print(vif_data)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\"TotalCharges\" hat den hÃ¶chsten VIF und wird aus dem Datensatz entfernt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_enc.drop(\"TotalCharges\", axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                  feature       VIF\n",
+      "0                           SeniorCitizen  1.363244\n",
+      "1                                 Partner  2.816895\n",
+      "2                              Dependents  1.956413\n",
+      "3                                  tenure  7.530356\n",
+      "4                            PhoneService  9.260839\n",
+      "5                        PaperlessBilling  2.757816\n",
+      "6                             gender_Male  1.931277\n",
+      "7                       MultipleLines_Yes  2.426699\n",
+      "8             InternetService_Fiber optic  3.581328\n",
+      "9                      InternetService_No  3.321342\n",
+      "10                     OnlineSecurity_Yes  1.947904\n",
+      "11                       OnlineBackup_Yes  2.093763\n",
+      "12                   DeviceProtection_Yes  2.241375\n",
+      "13                        TechSupport_Yes  2.060410\n",
+      "14                        StreamingTV_Yes  2.636855\n",
+      "15                    StreamingMovies_Yes  2.661529\n",
+      "16                      Contract_One year  2.055971\n",
+      "17                      Contract_Two year  3.456061\n",
+      "18  PaymentMethod_Credit card (automatic)  1.794059\n",
+      "19         PaymentMethod_Electronic check  2.401970\n",
+      "20             PaymentMethod_Mailed check  1.967082\n"
+     ]
+    }
+   ],
+   "source": [
+    "# the independent variables set \n",
+    "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
+    "  \n",
+    "# VIF dataframe \n",
+    "vif_data = pd.DataFrame() \n",
+    "vif_data[\"feature\"] = vif_test.columns \n",
+    "  \n",
+    "# calculating VIF for each feature \n",
+    "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
+    "                          for i in range(len(vif_test.columns))] \n",
+    "  \n",
+    "print(vif_data)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keine der Variablen hat jetzt einen VIF von mehr als 10."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4. Merkmalsskalierung"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separate target variable and predictors\n",
+    "y = data_enc[\"Churn\"]\n",
+    "X = data_enc.drop(labels = [\"Churn\"], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SeniorCitizen</th>\n",
+       "      <th>Partner</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>PhoneService</th>\n",
+       "      <th>PaperlessBilling</th>\n",
+       "      <th>gender_Male</th>\n",
+       "      <th>MultipleLines_Yes</th>\n",
+       "      <th>InternetService_Fiber optic</th>\n",
+       "      <th>InternetService_No</th>\n",
+       "      <th>...</th>\n",
+       "      <th>OnlineBackup_Yes</th>\n",
+       "      <th>DeviceProtection_Yes</th>\n",
+       "      <th>TechSupport_Yes</th>\n",
+       "      <th>StreamingTV_Yes</th>\n",
+       "      <th>StreamingMovies_Yes</th>\n",
+       "      <th>Contract_One year</th>\n",
+       "      <th>Contract_Two year</th>\n",
+       "      <th>PaymentMethod_Credit card (automatic)</th>\n",
+       "      <th>PaymentMethod_Electronic check</th>\n",
+       "      <th>PaymentMethod_Mailed check</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "      <td>0</td>\n",
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+       "    </tr>\n",
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+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.064303</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1.239504</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.512486</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-1.239504</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows Ã— 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
+       "0              0        1           0 -1.280248             0   \n",
+       "1              0        0           0  0.064303             1   \n",
+       "2              0        0           0 -1.239504             1   \n",
+       "3              0        0           0  0.512486             0   \n",
+       "4              0        0           0 -1.239504             1   \n",
+       "\n",
+       "   PaperlessBilling  gender_Male  MultipleLines_Yes  \\\n",
+       "0                 1            0                  0   \n",
+       "1                 0            1                  0   \n",
+       "2                 1            1                  0   \n",
+       "3                 0            1                  0   \n",
+       "4                 1            0                  0   \n",
+       "\n",
+       "   InternetService_Fiber optic  InternetService_No  ...  OnlineBackup_Yes  \\\n",
+       "0                            0                   0  ...                 1   \n",
+       "1                            0                   0  ...                 0   \n",
+       "2                            0                   0  ...                 1   \n",
+       "3                            0                   0  ...                 0   \n",
+       "4                            1                   0  ...                 0   \n",
+       "\n",
+       "   DeviceProtection_Yes  TechSupport_Yes  StreamingTV_Yes  \\\n",
+       "0                     0                0                0   \n",
+       "1                     1                0                0   \n",
+       "2                     0                0                0   \n",
+       "3                     1                1                0   \n",
+       "4                     0                0                0   \n",
+       "\n",
+       "   StreamingMovies_Yes  Contract_One year  Contract_Two year  \\\n",
+       "0                    0                  0                  0   \n",
+       "1                    0                  1                  0   \n",
+       "2                    0                  0                  0   \n",
+       "3                    0                  1                  0   \n",
+       "4                    0                  0                  0   \n",
+       "\n",
+       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
+       "0                                      0                               1   \n",
+       "1                                      0                               0   \n",
+       "2                                      0                               0   \n",
+       "3                                      0                               0   \n",
+       "4                                      0                               1   \n",
+       "\n",
+       "   PaymentMethod_Mailed check  \n",
+       "0                           0  \n",
+       "1                           1  \n",
+       "2                           1  \n",
+       "3                           0  \n",
+       "4                           0  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Scaling the variables\n",
+    "num_features = ['tenure']\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "X[num_features] = scaler.fit_transform(X[num_features])\n",
+    "X.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.5. Undersampling "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iht = InstanceHardnessThreshold(random_state=0,estimator=LogisticRegression (solver='lbfgs', multi_class='auto'))\n",
+    "                               \n",
+    "X_resampled, y_resampled = iht.fit_resample(X, y)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.6. Erstellen von Test- & Trainingsdaten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split dataset in train and test datasets\n",
+    "# The default value of 80% to 20% is used.\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, random_state=110)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "Modeling"
+   },
+   "source": [
+    "# 4. Modellierung und Auswertung"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.1. Logistische Regression"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "Modeling"
+   },
+   "source": [
+    "Zur LÃ¶sung des Problems wird die logistische Regression verwendet. HierfÃ¼r werden die beiden Bibliotheken Statsmodels und Scikit-Learn verwendet. Die komplette Auswertung des Modells findet erst im Unterkapitel zu Scikit-Learn statt."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "Modeling"
+   },
+   "source": [
+    "## Statistische Modelle"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "Modeling"
+   },
+   "source": [
+    "### Training und Vorhersage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.082006\n",
+      "         Iterations 11\n",
+      "                           Logit Regression Results                           \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  Churn   No. Observations:                 2803\n",
+      "Model:                          Logit   Df Residuals:                     2781\n",
+      "Method:                           MLE   Df Model:                           21\n",
+      "Date:                Thu, 21 Oct 2021   Pseudo R-squ.:                  0.8817\n",
+      "Time:                        15:00:28   Log-Likelihood:                -229.86\n",
+      "converged:                       True   LL-Null:                       -1942.4\n",
+      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
+      "=========================================================================================================\n",
+      "                                            coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "---------------------------------------------------------------------------------------------------------\n",
+      "const                                     5.1912      0.828      6.266      0.000       3.567       6.815\n",
+      "SeniorCitizen                             0.4609      0.457      1.008      0.313      -0.435       1.357\n",
+      "Partner                                  -0.4112      0.302     -1.362      0.173      -1.003       0.181\n",
+      "Dependents                               -0.5746      0.294     -1.952      0.051      -1.151       0.002\n",
+      "tenure                                   -2.9281      0.309     -9.468      0.000      -3.534      -2.322\n",
+      "PhoneService                             -1.2307      0.544     -2.261      0.024      -2.298      -0.164\n",
+      "PaperlessBilling                          1.2621      0.288      4.385      0.000       0.698       1.826\n",
+      "gender_Male                              -0.1334      0.255     -0.524      0.600      -0.633       0.366\n",
+      "MultipleLines_Yes                         1.0865      0.336      3.231      0.001       0.427       1.746\n",
+      "InternetService_Fiber optic               3.1681      0.400      7.916      0.000       2.384       3.952\n",
+      "InternetService_No                       -2.8314      0.567     -4.992      0.000      -3.943      -1.720\n",
+      "OnlineSecurity_Yes                       -1.7901      0.321     -5.581      0.000      -2.419      -1.161\n",
+      "OnlineBackup_Yes                         -0.3203      0.309     -1.036      0.300      -0.926       0.286\n",
+      "DeviceProtection_Yes                      0.4336      0.331      1.312      0.190      -0.214       1.082\n",
+      "TechSupport_Yes                          -0.8710      0.329     -2.648      0.008      -1.516      -0.226\n",
+      "StreamingTV_Yes                           1.1971      0.351      3.414      0.001       0.510       1.884\n",
+      "StreamingMovies_Yes                       1.4263      0.374      3.815      0.000       0.693       2.159\n",
+      "Contract_One year                        -3.5720      0.488     -7.317      0.000      -4.529      -2.615\n",
+      "Contract_Two year                        -6.5206      0.584    -11.164      0.000      -7.665      -5.376\n",
+      "PaymentMethod_Credit card (automatic)    -0.0720      0.313     -0.230      0.818      -0.686       0.542\n",
+      "PaymentMethod_Electronic check            1.2794      0.406      3.154      0.002       0.484       2.075\n",
+      "PaymentMethod_Mailed check               -0.3240      0.398     -0.813      0.416      -1.105       0.457\n",
+      "=========================================================================================================\n",
+      "\n",
+      "Possibly complete quasi-separation: A fraction 0.37 of observations can be\n",
+      "perfectly predicted. This might indicate that there is complete\n",
+      "quasi-separation. In this case some parameters will not be identified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# add constant\n",
+    "X_const = sm.add_constant(X_train)\n",
+    "# create model\n",
+    "log_reg = sm.Logit(y_train, X_const).fit() \n",
+    "print(log_reg.summary())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "Modeling"
+   },
+   "source": [
+    "Das trainierte Modell zeigt statistisch nicht-signifikante Variablen an. Dies ist gegeben, wenn der Wert P>|z| grÃ¶ÃŸer als 0,05 ist und es sich nicht um die Konstante handelt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Removing the statistically non-significant features (P>|z|> 0.05)\n",
+    "insignificant_features = [\"Partner\", \"gender_Male\", \"OnlineBackup_Yes\", \"DeviceProtection_Yes\", \"PaymentMethod_Credit card (automatic)\",\"PaymentMethod_Mailed check\"]\n",
+    "X_train.drop(insignificant_features, axis=1, inplace=True)\n",
+    "X_test.drop(insignificant_features, axis=1, inplace=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": false,
+    "paragraph": "Modeling"
+   },
+   "source": [
+    "Nun kann ein zweites Modell erstellt werden:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.083077\n",
+      "         Iterations 11\n",
+      "                           Logit Regression Results                           \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  Churn   No. Observations:                 2803\n",
+      "Model:                          Logit   Df Residuals:                     2787\n",
+      "Method:                           MLE   Df Model:                           15\n",
+      "Date:                Thu, 21 Oct 2021   Pseudo R-squ.:                  0.8801\n",
+      "Time:                        15:00:28   Log-Likelihood:                -232.87\n",
+      "converged:                       True   LL-Null:                       -1942.4\n",
+      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
+      "==================================================================================================\n",
+      "                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "--------------------------------------------------------------------------------------------------\n",
+      "const                              4.7119      0.718      6.566      0.000       3.305       6.118\n",
+      "SeniorCitizen                      0.3954      0.458      0.864      0.387      -0.501       1.292\n",
+      "Dependents                        -0.7328      0.262     -2.797      0.005      -1.246      -0.219\n",
+      "tenure                            -2.9242      0.297     -9.845      0.000      -3.506      -2.342\n",
+      "PhoneService                      -1.2073      0.540     -2.235      0.025      -2.266      -0.149\n",
+      "PaperlessBilling                   1.2161      0.285      4.273      0.000       0.658       1.774\n",
+      "MultipleLines_Yes                  1.0989      0.331      3.320      0.001       0.450       1.748\n",
+      "InternetService_Fiber optic        3.1159      0.391      7.966      0.000       2.349       3.883\n",
+      "InternetService_No                -2.8462      0.529     -5.381      0.000      -3.883      -1.809\n",
+      "OnlineSecurity_Yes                -1.7441      0.313     -5.576      0.000      -2.357      -1.131\n",
+      "TechSupport_Yes                   -0.8357      0.325     -2.569      0.010      -1.473      -0.198\n",
+      "StreamingTV_Yes                    1.2193      0.348      3.508      0.000       0.538       1.901\n",
+      "StreamingMovies_Yes                1.4394      0.368      3.908      0.000       0.717       2.161\n",
+      "Contract_One year                 -3.4572      0.471     -7.337      0.000      -4.381      -2.534\n",
+      "Contract_Two year                 -6.3299      0.557    -11.372      0.000      -7.421      -5.239\n",
+      "PaymentMethod_Electronic check     1.3103      0.362      3.623      0.000       0.601       2.019\n",
+      "==================================================================================================\n",
+      "\n",
+      "Possibly complete quasi-separation: A fraction 0.36 of observations can be\n",
+      "perfectly predicted. This might indicate that there is complete\n",
+      "quasi-separation. In this case some parameters will not be identified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# new model\n",
+    "X_const = sm.add_constant(X_train)\n",
+    "log_reg2 = sm.Logit(y_train, X_const).fit() \n",
+    "print(log_reg2.summary())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Keine statistisch nicht signifikanten Variablen mehr. Das endgÃ¼ltige Modell wurde modelliert:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.083077\n",
+      "         Iterations 11\n",
+      "                           Logit Regression Results                           \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  Churn   No. Observations:                 2803\n",
+      "Model:                          Logit   Df Residuals:                     2787\n",
+      "Method:                           MLE   Df Model:                           15\n",
+      "Date:                Thu, 21 Oct 2021   Pseudo R-squ.:                  0.8801\n",
+      "Time:                        15:00:28   Log-Likelihood:                -232.87\n",
+      "converged:                       True   LL-Null:                       -1942.4\n",
+      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
+      "==================================================================================================\n",
+      "                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "--------------------------------------------------------------------------------------------------\n",
+      "const                              4.7119      0.718      6.566      0.000       3.305       6.118\n",
+      "SeniorCitizen                      0.3954      0.458      0.864      0.387      -0.501       1.292\n",
+      "Dependents                        -0.7328      0.262     -2.797      0.005      -1.246      -0.219\n",
+      "tenure                            -2.9242      0.297     -9.845      0.000      -3.506      -2.342\n",
+      "PhoneService                      -1.2073      0.540     -2.235      0.025      -2.266      -0.149\n",
+      "PaperlessBilling                   1.2161      0.285      4.273      0.000       0.658       1.774\n",
+      "MultipleLines_Yes                  1.0989      0.331      3.320      0.001       0.450       1.748\n",
+      "InternetService_Fiber optic        3.1159      0.391      7.966      0.000       2.349       3.883\n",
+      "InternetService_No                -2.8462      0.529     -5.381      0.000      -3.883      -1.809\n",
+      "OnlineSecurity_Yes                -1.7441      0.313     -5.576      0.000      -2.357      -1.131\n",
+      "TechSupport_Yes                   -0.8357      0.325     -2.569      0.010      -1.473      -0.198\n",
+      "StreamingTV_Yes                    1.2193      0.348      3.508      0.000       0.538       1.901\n",
+      "StreamingMovies_Yes                1.4394      0.368      3.908      0.000       0.717       2.161\n",
+      "Contract_One year                 -3.4572      0.471     -7.337      0.000      -4.381      -2.534\n",
+      "Contract_Two year                 -6.3299      0.557    -11.372      0.000      -7.421      -5.239\n",
+      "PaymentMethod_Electronic check     1.3103      0.362      3.623      0.000       0.601       2.019\n",
+      "==================================================================================================\n",
+      "\n",
+      "Possibly complete quasi-separation: A fraction 0.36 of observations can be\n",
+      "perfectly predicted. This might indicate that there is complete\n",
+      "quasi-separation. In this case some parameters will not be identified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# final model\n",
+    "X_const = sm.add_constant(X_train)\n",
+    "log_reg_final = sm.Logit(y_train, X_const).fit() \n",
+    "print(log_reg_final.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# prediction\n",
+    "y_hat = log_reg_final.predict(sm.add_constant(X_test)) \n",
+    "# Statsmodel only gives the probabilities, therefore rounding is required.   \n",
+    "prediction = list(map(round, y_hat))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.1. Auswertung"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zur Evaluation sollen mehrere Metriken verwendet werden, die komfortabler mittels Scikit-Learn erzeugt werden kÃ¶nnen. Deshalb wird das identische Modell wie mit Statsmodels nochmals in Scikit-Learn erzeugt."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scikit-Learn"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training und Vorhersage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# C is needed to build the exact same model as with Statsmodels; source: https://www.kdnuggets.com/2016/06/regularization-logistic-regression.html\n",
+    "logistic_model = LogisticRegression(random_state=0, C=1e8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# prediction with testdata\n",
+    "result = logistic_model.fit(X_train,y_train)\n",
+    "prediction_test = logistic_model.predict(X_test)\n",
+    "prediction_train = logistic_model.predict(X_train)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "Evaluation"
+   },
+   "source": [
+    "### Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy with testdata: 0.9882352941176471\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Accuracy Score \n",
+    "acc = metrics.accuracy_score(y_test, prediction_test)\n",
+    "print('Accuracy with testdata: {}'.format(acc))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "Evaluation"
+   },
+   "source": [
+    "Die Genauigkeit deutet auf ein Ã¼berdurchschnittliches Modell hin. Allerdings handelt es sich um einen unausgewogenen Datensatz. Daher mÃ¼ssen weitere Metriken analysiert werden.\n",
+    "\t\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "traindata:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.96      1.00      0.98      1374\n",
+      "           1       1.00      0.96      0.98      1429\n",
+      "\n",
+      "    accuracy                           0.98      2803\n",
+      "   macro avg       0.98      0.98      0.98      2803\n",
+      "weighted avg       0.98      0.98      0.98      2803\n",
+      "\n",
+      "testdata:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.98      1.00      0.99       495\n",
+      "           1       1.00      0.98      0.99       440\n",
+      "\n",
+      "    accuracy                           0.99       935\n",
+      "   macro avg       0.99      0.99      0.99       935\n",
+      "weighted avg       0.99      0.99      0.99       935\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# classification report\n",
+    "\n",
+    "print(\"traindata:\")\n",
+    "print(classification_report(y_train,prediction_train))\n",
+    "print(\"testdata:\")\n",
+    "print(classification_report(y_test,prediction_test))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "HÃ¶here Genauigkeit fÃ¼r das Training als fÃ¼r den Testdatensatz. Insgesamt sind die Werte fÃ¼r den Test- und den Trainingsdatensatz sehr Ã¤hnlich. Daher sollte nicht von einem Overfitting oder Underfitting ausgegangen werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 39.5, 'Predicted label')"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Confusion matrix testdata\n",
+    "cm = confusion_matrix(y_test,prediction_test)\n",
+    "df_cm = pd.DataFrame(cm, index=['No Churn','Churn'], columns=['No Churn', 'Churn'],)\n",
+    "fig = plt.figure(figsize=[10,7])\n",
+    "heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
+    "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=14)\n",
+    "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=14)\n",
+    "plt.ylabel('True label')\n",
+    "plt.xlabel('Predicted label')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True Negatives: 493\n",
+      "False Positives: 2\n",
+      "False Negatives: 9\n",
+      "True Positives: 431\n",
+      "Recall: 0.9795454545454545\n",
+      "Precision: 0.9953810623556582\n"
+     ]
+    }
+   ],
+   "source": [
+    "# metrics from confusion matrix\n",
+    "tn, fp, fn, tp = cm.ravel()\n",
+    "recall = tp/(fn+tp)\n",
+    "precision = tp/(tp+fp)\n",
+    "print(\"True Negatives: \" + str(tn))\n",
+    "print(\"False Positives: \" + str(fp))\n",
+    "print(\"False Negatives: \" + str(fn))\n",
+    "print(\"True Positives: \" + str(tp))\n",
+    "print(\"Recall: \" + str(recall))\n",
+    "print(\"Precision: \" + str(precision))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "PrÃ¤zision und Recall vermitteln ein viel realistischeres Bild des Modells. Es erreicht eine PrÃ¤zision von rund 68 % und eine Wiederauffindbarkeit von 52 %. Der Recall ist fÃ¼r den Anwendungsfall eindeutig wichtiger und muss daher auf Kosten der PrÃ¤zision verbessert werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ROC-Kurve, AUC\n",
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "ax.set_title('ROC Kurve')\n",
+    "plot = metrics.plot_roc_curve(logistic_model, X_test, y_test, ax=ax);\n",
+    "ax.plot([0,1], [0,1], '--');"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Der AUC der ROC-Kurve ergibt einen guten Wert von 0,84. Daraus lÃ¤sst sich schlieÃŸen, dass durch die Optimierung des Schwellenwertes Optimierungspotenzial besteht."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.3. Interpretation"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ZunÃ¤chst sollen jedoch die Ergebnisse fÃ¼r das Unternehmen veranschaulicht werden und es soll geklÃ¤rt werden, welche Kunden zur Abwanderung fÃ¼hren und welche gegen eine Abwanderung sprechen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "InternetService_Fiber optic       3.115901\n",
+       "StreamingMovies_Yes               1.439381\n",
+       "PaymentMethod_Electronic check    1.310265\n",
+       "StreamingTV_Yes                   1.219198\n",
+       "PaperlessBilling                  1.216093\n",
+       "MultipleLines_Yes                 1.098867\n",
+       "SeniorCitizen                     0.395488\n",
+       "Dependents                       -0.732812\n",
+       "TechSupport_Yes                  -0.835712\n",
+       "PhoneService                     -1.207319\n",
+       "OnlineSecurity_Yes               -1.744166\n",
+       "InternetService_No               -2.846463\n",
+       "tenure                           -2.924275\n",
+       "Contract_One year                -3.457173\n",
+       "Contract_Two year                -6.329852\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read out regression coefficients and thus find out importance of individual attributes\n",
+    "weights = pd.Series(logistic_model.coef_[0],\n",
+    " index=X_train.columns.values)\n",
+    "weights.sort_values(ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Graphical representation of key features that lead to churn.\n",
+    "weights = pd.Series(logistic_model.coef_[0],\n",
+    "                 index=X_train.columns.values)\n",
+    "print (weights.sort_values(ascending = False)[:7].plot(kind='bar'))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die drei Hauptmerkmale, die zur Abwanderung fÃ¼hren, sind:\n",
+    "\n",
+    "- Der Glasfaserdienst (InternetService_Glasfaser),\n",
+    "- Die Online-Zahlungen (PaperlessBilling) und\n",
+    "- Das Abonnement des zusÃ¤tzlichen Filmstreamingdienstes (StreamingMovies_Yes).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Most important features that keep customers from churning\n",
+    "print(weights.sort_values(ascending = False)[-8:].plot(kind='bar'))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die drei wichtigsten Merkmale, die Kunden von der Abwanderung abhalten, sind:\n",
+    "\n",
+    "- Die VertrÃ¤ge, die fÃ¼r zwei Jahre gekÃ¼ndigt werden kÃ¶nnen (Contract_Two year),\n",
+    "- Die Zeit, die man Kunde eines Unternehmens ist (Tenure) und\n",
+    "- kein Abonnement fÃ¼r den Internetdienst (InternetService_No).\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.4. Modell-Optimierung"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Die Recall-Rate ist als Zielmetrik zu niedrig und muss daher erhÃ¶ht werden. Daher werden die Metriken bei verschiedenen Schwellenwerten der logistischen Regression analysiert."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "******** For a Threshold about 0.05 ******\n",
+      "Accuracy: 0.8588235294117647\n",
+      "True Negatives: 367\n",
+      "False Positives: 128\n",
+      "False Negatives: 4\n",
+      "True Positives: 436\n",
+      "Recall: 0.990909090909091\n",
+      "Precision: 0.7730496453900709\n",
+      "\n",
+      "******** For a Threshold about 0.1 ******\n",
+      "Accuracy: 0.9144385026737968\n",
+      "True Negatives: 420\n",
+      "False Positives: 75\n",
+      "False Negatives: 5\n",
+      "True Positives: 435\n",
+      "Recall: 0.9886363636363636\n",
+      "Precision: 0.8529411764705882\n",
+      "\n",
+      "******** For a Threshold about 0.15 ******\n",
+      "Accuracy: 0.9422459893048128\n",
+      "True Negatives: 446\n",
+      "False Positives: 49\n",
+      "False Negatives: 5\n",
+      "True Positives: 435\n",
+      "Recall: 0.9886363636363636\n",
+      "Precision: 0.8987603305785123\n",
+      "\n",
+      "******** For a Threshold about 0.2 ******\n",
+      "Accuracy: 0.9657754010695188\n",
+      "True Negatives: 468\n",
+      "False Positives: 27\n",
+      "False Negatives: 5\n",
+      "True Positives: 435\n",
+      "Recall: 0.9886363636363636\n",
+      "Precision: 0.9415584415584416\n",
+      "\n",
+      "******** For a Threshold about 0.25 ******\n",
+      "Accuracy: 0.9786096256684492\n",
+      "True Negatives: 481\n",
+      "False Positives: 14\n",
+      "False Negatives: 6\n",
+      "True Positives: 434\n",
+      "Recall: 0.9863636363636363\n",
+      "Precision: 0.96875\n",
+      "\n",
+      "******** For a Threshold about 0.3 ******\n",
+      "Accuracy: 0.9818181818181818\n",
+      "True Negatives: 486\n",
+      "False Positives: 9\n",
+      "False Negatives: 8\n",
+      "True Positives: 432\n",
+      "Recall: 0.9818181818181818\n",
+      "Precision: 0.9795918367346939\n",
+      "\n",
+      "******** For a Threshold about 0.35 ******\n",
+      "Accuracy: 0.986096256684492\n",
+      "True Negatives: 490\n",
+      "False Positives: 5\n",
+      "False Negatives: 8\n",
+      "True Positives: 432\n",
+      "Recall: 0.9818181818181818\n",
+      "Precision: 0.988558352402746\n",
+      "\n",
+      "******** For a Threshold about 0.4 ******\n",
+      "Accuracy: 0.9871657754010695\n",
+      "True Negatives: 491\n",
+      "False Positives: 4\n",
+      "False Negatives: 8\n",
+      "True Positives: 432\n",
+      "Recall: 0.9818181818181818\n",
+      "Precision: 0.9908256880733946\n",
+      "\n",
+      "******** For a Threshold about 0.45 ******\n",
+      "Accuracy: 0.9893048128342246\n",
+      "True Negatives: 493\n",
+      "False Positives: 2\n",
+      "False Negatives: 8\n",
+      "True Positives: 432\n",
+      "Recall: 0.9818181818181818\n",
+      "Precision: 0.9953917050691244\n",
+      "\n",
+      "******** For a Threshold about 0.5 ******\n",
+      "Accuracy: 0.9882352941176471\n",
+      "True Negatives: 493\n",
+      "False Positives: 2\n",
+      "False Negatives: 9\n",
+      "True Positives: 431\n",
+      "Recall: 0.9795454545454545\n",
+      "Precision: 0.9953810623556582\n",
+      "\n",
+      "******** For a Threshold about 0.55 ******\n",
+      "Accuracy: 0.9882352941176471\n",
+      "True Negatives: 493\n",
+      "False Positives: 2\n",
+      "False Negatives: 9\n",
+      "True Positives: 431\n",
+      "Recall: 0.9795454545454545\n",
+      "Precision: 0.9953810623556582\n",
+      "\n",
+      "******** For a Threshold about 0.6 ******\n",
+      "Accuracy: 0.9893048128342246\n",
+      "True Negatives: 494\n",
+      "False Positives: 1\n",
+      "False Negatives: 9\n",
+      "True Positives: 431\n",
+      "Recall: 0.9795454545454545\n",
+      "Precision: 0.9976851851851852\n",
+      "\n",
+      "******** For a Threshold about 0.65 ******\n",
+      "Accuracy: 0.9893048128342246\n",
+      "True Negatives: 494\n",
+      "False Positives: 1\n",
+      "False Negatives: 9\n",
+      "True Positives: 431\n",
+      "Recall: 0.9795454545454545\n",
+      "Precision: 0.9976851851851852\n",
+      "\n",
+      "******** For a Threshold about 0.7 ******\n",
+      "Accuracy: 0.9903743315508021\n",
+      "True Negatives: 495\n",
+      "False Positives: 0\n",
+      "False Negatives: 9\n",
+      "True Positives: 431\n",
+      "Recall: 0.9795454545454545\n",
+      "Precision: 1.0\n",
+      "\n",
+      "******** For a Threshold about 0.75 ******\n",
+      "Accuracy: 0.9903743315508021\n",
+      "True Negatives: 495\n",
+      "False Positives: 0\n",
+      "False Negatives: 9\n",
+      "True Positives: 431\n",
+      "Recall: 0.9795454545454545\n",
+      "Precision: 1.0\n",
+      "\n",
+      "******** For a Threshold about 0.8 ******\n",
+      "Accuracy: 0.9893048128342246\n",
+      "True Negatives: 495\n",
+      "False Positives: 0\n",
+      "False Negatives: 10\n",
+      "True Positives: 430\n",
+      "Recall: 0.9772727272727273\n",
+      "Precision: 1.0\n",
+      "\n",
+      "******** For a Threshold about 0.85 ******\n",
+      "Accuracy: 0.9882352941176471\n",
+      "True Negatives: 495\n",
+      "False Positives: 0\n",
+      "False Negatives: 11\n",
+      "True Positives: 429\n",
+      "Recall: 0.975\n",
+      "Precision: 1.0\n",
+      "\n",
+      "******** For a Threshold about 0.9 ******\n",
+      "Accuracy: 0.9871657754010695\n",
+      "True Negatives: 495\n",
+      "False Positives: 0\n",
+      "False Negatives: 12\n",
+      "True Positives: 428\n",
+      "Recall: 0.9727272727272728\n",
+      "Precision: 1.0\n",
+      "\n",
+      "******** For a Threshold about 0.95 ******\n",
+      "Accuracy: 0.9807486631016042\n",
+      "True Negatives: 495\n",
+      "False Positives: 0\n",
+      "False Negatives: 18\n",
+      "True Positives: 422\n",
+      "Recall: 0.9590909090909091\n",
+      "Precision: 1.0\n",
+      "\n",
+      "******** For a Threshold about 0.99 ******\n",
+      "Accuracy: 0.9497326203208556\n",
+      "True Negatives: 495\n",
+      "False Positives: 0\n",
+      "False Negatives: 47\n",
+      "True Positives: 393\n",
+      "Recall: 0.8931818181818182\n",
+      "Precision: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Testing the metrics at different thresholds\n",
+    "threshold_list = [0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,.7,.75,.8,.85,.9,.95,.99]\n",
+    "pred_proba_df = y_hat\n",
+    "for i in threshold_list:\n",
+    "    print ('\\n******** For a Threshold about {} ******'.format(i))\n",
+    "    # Round up if value is above threshold\n",
+    "    y_test_pred = pred_proba_df.apply(lambda x: 1 if x>i else 0)\n",
+    "    # read metrics\n",
+    "    test_accuracy = metrics.accuracy_score(y_test, y_test_pred)\n",
+    "    print(\"Accuracy: {}\".format(test_accuracy))\n",
+    "    # Confusion matrix\n",
+    "    c = confusion_matrix(y_test, y_test_pred)\n",
+    "    tn, fp, fn, tp = c.ravel()\n",
+    "    recall = tp/(fn+tp)\n",
+    "    precision = tp/(tp+fp)\n",
+    "    # print metrics\n",
+    "    print(\"True Negatives: \" + str(tn))\n",
+    "    print(\"False Positives: \" + str(fp))\n",
+    "    print(\"False Negatives: \" + str(fn))\n",
+    "    print(\"True Positives: \" + str(tp))\n",
+    "    print(\"Recall: \" + str(recall))\n",
+    "    print(\"Precision: \" + str(precision))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ein Schwellenwert von 0,3 bietet ein besseres Ergebnis fÃ¼r die Anwendung. Er erhÃ¶ht die Wiederauffindbarkeit auf ein zufriedenstellendes Niveau von 73,21 %, was zu Lasten der PrÃ¤zision geht. Die PrÃ¤zision ist jedoch vernachlÃ¤ssigbar."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Daraus ergeben sich die folgenden Werte:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.98      0.98      0.98       495\n",
+      "           1       0.98      0.98      0.98       440\n",
+      "\n",
+      "    accuracy                           0.98       935\n",
+      "   macro avg       0.98      0.98      0.98       935\n",
+      "weighted avg       0.98      0.98      0.98       935\n",
+      "\n",
+      "Confusion matrix for the new threshold:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " \n",
+      "Metrics for the new threshold:\n",
+      "Accuracy: 0.9818181818181818\n",
+      "True Negatives: 486\n",
+      "False Positives: 9\n",
+      "False Negatives: 8\n",
+      "True Positives: 432\n",
+      "Recall: 0.9818181818181818\n",
+      "Precision: 0.9795918367346939\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Threshold about 0,3\n",
+    "y_test_pred = pred_proba_df.apply(lambda x: 1 if x>0.30 else 0)\n",
+    "test_accuracy = metrics.accuracy_score(y_test, y_test_pred)\n",
+    "c = confusion_matrix(y_test, y_test_pred)\n",
+    "# read values from confusion matrix\n",
+    "tn, fp, fn, tp = c.ravel()\n",
+    "recall = tp/(fn+tp)\n",
+    "precision = tp/(tp+fp)\n",
+    "print(classification_report(y_test,y_test_pred))\n",
+    "# create confusion matrix\n",
+    "print(\"Confusion matrix for the new threshold:\")\n",
+    "df_cm = pd.DataFrame(c, index=['No Churn','Churn'], columns=['No Churn', 'Churn'],)\n",
+    "fig = plt.figure(figsize=[10,7])\n",
+    "heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
+    "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=14)\n",
+    "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=14)\n",
+    "plt.ylabel('True label')\n",
+    "plt.xlabel('Predicted label')\n",
+    "plt.show()\n",
+    "print(\" \")\n",
+    "# print metrics\n",
+    "print(\"Metrics for the new threshold:\")\n",
+    "print(\"Accuracy: {}\".format(test_accuracy))\n",
+    "print(\"True Negatives: \" + str(tn))\n",
+    "print(\"False Positives: \" + str(fp))\n",
+    "print(\"False Negatives: \" + str(fn))\n",
+    "print(\"True Positives: \" + str(tp))\n",
+    "print(\"Recall: \" + str(recall))\n",
+    "print(\"Precision: \" + str(precision))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "Evaluation"
+   },
+   "source": [
+    "ErwartungsgemÃ¤ÃŸ steigt die Rate der fÃ¤lschlicherweise als abgewandert eingestuften Kunden. Im Gegenzug steigt aber auch die Anzahl der Kunden, die korrekt als Abwanderer vorhergesagt werden (True Positives). Wie in der Hausarbeit ausgefÃ¼hrt, ist dies essentiell, denn im Zweifelsfall wÃ¼rde ein Kunde fÃ¤lschlicherweise vom Serviceteam angerufen werden und diesen Anruf sogar als guten Service wahrnehmen und lÃ¤ngerfristig an das Unternehmen binden."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "include": true,
+    "paragraph": "Deployment"
+   },
+   "source": [
+    "# 5. Deployment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separate individual (scaled) customer\n",
+    "customer_df = X_test.iloc[896]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SeniorCitizen                     0.000000\n",
+       "Dependents                        0.000000\n",
+       "tenure                           -0.302393\n",
+       "PhoneService                      1.000000\n",
+       "PaperlessBilling                  0.000000\n",
+       "MultipleLines_Yes                 0.000000\n",
+       "InternetService_Fiber optic       0.000000\n",
+       "InternetService_No                1.000000\n",
+       "OnlineSecurity_Yes                0.000000\n",
+       "TechSupport_Yes                   0.000000\n",
+       "StreamingTV_Yes                   0.000000\n",
+       "StreamingMovies_Yes               0.000000\n",
+       "Contract_One year                 0.000000\n",
+       "Contract_Two year                 1.000000\n",
+       "PaymentMethod_Electronic check    0.000000\n",
+       "Name: 1544, dtype: float64"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Overview about the customer\n",
+    "customer_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# execute prediction\n",
+    "cust_pred = logistic_model.predict([customer_df])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# evaluate results\n",
+    "def check_prediction(pred):\n",
+    "    if pred[0] == 1:\n",
+    "        print(\"The customer will probably churn! Inform Customer Relationship Management!\")\n",
+    "    else:\n",
+    "        print(\"The customer probably will not churn.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The customer probably will not churn.\n"
+     ]
+    }
+   ],
+   "source": [
+    "check_prediction(cust_pred)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Zusammenfassung"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Das Notebook hat gezeigt, wie eine logistische Regression verwendet werden kann, um die Abwanderung von Kunden im Telekommunikationssegment vorherzusagen."
+   ]
+  }
+ ],
+ "metadata": {
+  "category": "CRM",
+  "interpreter": {
+   "hash": "07673f924689cda9590af2124148040e2ce6fe7ee39bbd541ae69d9c32a71079"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  },
+  "title": "Customer Churn Prediction"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}