diff --git a/Tourism/Prediction cancellation of hotel bookings/notebook.ipynb b/Tourism/Prediction cancellation of hotel bookings/notebook.ipynb
index c36522a0c8a4c44854d0c2bd880d4c557943b9ec..7cb476199fa6afd216a2935d6937c4bda09448fa 100644
--- a/Tourism/Prediction cancellation of hotel bookings/notebook.ipynb
+++ b/Tourism/Prediction cancellation of hotel bookings/notebook.ipynb
@@ -3,7 +3,6 @@
{
"cell_type": "markdown",
"metadata": {
- "Paragraph": "Analyse der Bewegung und Aktivität freilaufender Rinder",
"editable": true,
"include": true,
"slideshow": {
@@ -12,13 +11,12 @@
"tags": []
},
"source": [
- "# Analyse der Bewegung und Aktivität freilaufender Rinder"
+ "# 1. Geschäftsverständnis"
]
},
{
"cell_type": "markdown",
"metadata": {
- "Paragraph": "Geschäftsverstandnis",
"editable": true,
"include": true,
"slideshow": {
@@ -27,93 +25,10 @@
"tags": []
},
"source": [
- "# 1. Geschäftsverstandinis\n",
- "Landwirte geraten immer weiter unter wirtschaftlichen Druck. Beispielsweise kam es durch die Coronapandemie zu teuren logistischen Problemen. Gleichzeitig stellen immer mehr Konsumenten höhere Anforderungen an eine artgerechte Tierhaltung. Diese Marktbedingungen erscheinen für Landwirte problematisch, da letzterer Aspekt natürlich mit gewissen Kosten verbunden ist und ein generelles Umdenken notwendig macht. Durch neue Technologien wie z.B. Künstliche Intelligenz und der Verwendung von Sensoren können die Prozesse effizienter gestaltet und gleichzeitig die Anforderungen des Tierschutzes besser erfüllt werden. Ein Problem der Landwirte ist, dass sie ihre Tiere nicht gezielt bzw. individuell umsorgen können. Das Wissen darüber, welche Aktivitäten z.B. besonderen Einfluss auf die Milchproduktion oder Fleischqualität haben, kann bisher nicht umfänglich erfasst werden. Somit sind auch keine gezielten Maßnahmen zur Förderung bestimmter Aktivitäten möglich. Denkbare Maßnahmen wären die gezielte Motivation zu mehr Bewegung einzelner Tiere oder die Optimierung der Zucht. Bisher ist es jedoch schwierig kontinuierliche Aktivitätsprofile der Tiere zu erstellen. Ein weiteres Problem der Landwirte ist, dass eine frühzeitige Erkennung von Veränderungen des Gesundheitszustandes einzelner Tiere oft nicht möglich ist. Zwar ist die regelmäßige Kontrolle des Zustandes einer Herde rechtlich vorgeschrieben, jedoch können gerade in größeren Betrieben auf Grund der großen Anzahl an Tieren, gesundheitliche Veränderungen erst spät erkannt werden. Oft bleibt im Krankheitsfall nur die Behandlung mit Antibiotika oder anderen teuren Medikamenten. Gewissen Verhaltensänderungen können jedoch frühzeitig erste Hinweise liefern. Gesunde Rinder beispielsweise bewegen sich normalerweise recht viel und reduzieren ihre Aktivität, wenn sie erste Krankheitszeichen entwickeln. Eine frühe Erkennung dieser Bewegungsreduzierung wäre von großer Bedeutung für einen Landwirt. Healthy Cattle plant, zur Lösung der oben genannten Probleme, eine Aktivitätsklassifizierung zur Erstellung von Aktivitätsprofilen und zur frühzeitigen Erkennung von Krankheiten zu entwickeln. Die Hypothese ist, dass mit Hilfe von typischen Sensoren eines Smartphones und der Verwendung eines Machine Learning Algorithmus, die Aktivitäten klassifiziert werden können. Healthy Cattle möchte für die geplante Lösung zunächst testen, ob mit Hilfe von typischen Sensoren eines Smartphones eine ausreichend genaue Klassifizierung von Aktivitäten möglich ist. Um dies möglichst kostengünstig testen zu können, werden für einen ersten Prototypen keine eigenen Daten verwendet, sondern im Internet zugängliche Daten aus einem ähnlichen Setup genutzt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "Include": true,
- "Paragraph": "Datenverständnis",
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 2. Datenverständnis\n",
- "Der Datensatz dieser Hausarbeit stammt von Anguita, Ghio et al. und wurde abgerufen von Kaggle.com unter: https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones. Der Original Datensatz ist auf https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones. In einem Experiment haben 30 Probanden mit einem auf Hüfthöhe befestigten Smartphone einen festgelegten Bewegungsablauf durchgeführt. Dabei wurden mit Hilfe der verbauten Sensoren die linearen Beschleunigungs- und Winkelgeschwindigkeitssignale auf jeweils drei Achsen erfasst. Die einzelnen Datenpunkte wurden je nach Aktivität klassifiziert bzw. gelabelt. Durch verschiedene Filterverfahren wurden diese anschließend von Rauschen befreit und weitere zeit- der frequenzbezogene Features extrahiert. Aus diesen wurden weitere Features wie z.B. Mittelwert, Standardabweichung, Minimal- und Maximalwert, Median usw. berechnet. Das zu Grunde gelegte Machine Learning Problem ist in den Bereich Supervised Learning einzuordnen, da auf gelabelten Daten gelernt wird. Jeder Datenpunkt im Datensatz besitzt eine Reihe von Features - berechnet aus der Sensorwerten - und ein Target - die Aktivitätsklasse. Der zur Verfügung stehende Datensatz besteht aus 10299 Beobachtungen (Observations). Es gibt keine fehlenden Werte und es existieren auch keine Duplikate. Die Anzahl der Features ist sehr groß, was eine umfängliche Analyse der einzelnen Features stark erschwert. Eine grafische Überprüfung auf Multikollinearität mit Hilfe der Korrelationsmatrix ist deshalb nicht sinnvoll. Eine Überprüfung des Variance Inflation Factor (VIF) ergibt, dass sehr viele Features mit einem VIF>10 eine hohe Multikollinearität aufweisen (siehe Tabelle). Eine hohe Multikollinearität hat negative Auswirkungen auf die Interpretierbarkeit der später erstellten Modelle\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "Include": true,
- "Paragraph": "Import von Relevant Module",
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "## 2.1. Import von Relevant Modules "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "Include": false,
- "Paragraph": "Import von Relevant Module",
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "ename": "ModuleNotFoundError",
- "evalue": "No module named 'plotly'",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[1;32mIn[2], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m \u001b[38;5;66;03m# standard for data processing\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m \u001b[38;5;66;03m# standard for data processing\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgraph_objects\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgo\u001b[39;00m \u001b[38;5;66;03m# creates plots\u001b[39;00m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n",
- "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
- ]
- }
- ],
- "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"
+ "Abschätzung des Kundenverhaltens bezüglich Hotelstornierungen zur Planung von Kapazitäten. Der Anwendungsfall soll testen, ob es möglich ist,\n",
+ "Hotelstornierungen vorherzusagen.Ein wichtiges Ziel für jedes Unternehmen liegt in der Erhaltung wertvoller Kundenbeziehungen, \n",
+ "postive Ranking und Kapazitat plannung. Für Unternehmen ist daher eine Einschätzung der Kundenvehalten bei Buchungstonierung notwendig, \n",
+ "So dass sich das Risiko der Buchungstonierung eines Kunden vorab einschätzen lässt, können Gegenmaßnahmen eingeleitet werden."
]
},
{
@@ -121,69 +36,18 @@
"metadata": {
"editable": true,
"include": true,
- "paragragph": "Daten Auslesen",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## Daten Auslesen"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "Include": true,
- "Paragraph": "Daten Auslesen",
- "editable": true,
+ "paragraph": "Daten und Datenverständnis",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
- "Quelle der Daten:\n",
- "\n",
- "Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra und Jorge L. Reyes-Ortiz. Ein gemeinfreier Datensatz zur Erkennung menschlicher Aktivitäten mithilfe von Smartphones. 21. Europäisches Symposium zu künstlichen neuronalen Netzen, Computerintelligenz und maschinellem Lernen, ESANN 2013. Brügge, Belgien, 24.–26. April 2013.\n",
- "Downloaded from Kaggle:\n",
- "https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones"
+ "# 2. Daten und Datenverständnis"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "Include": true,
- "Pragraph": true,
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Es gibt zwei separate Datensätze aus der Testgruppe und der Trainingsgruppe. Zur weiteren Analyse werden diese zunächst noch einmal zusammengefasst. Die Daten werden unabhängig davon, ob sie aus dem Zug- oder Testdatensatz stammen, identifiziert, sodass sie später wieder aufgeteilt werden können."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
"editable": true,
"include": true,
@@ -192,70 +56,62 @@
},
"tags": []
},
- "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=',')"
+ "Die Daten Rahmen sind fuer Hotel Buchungen und Der Datensatz für diese Demo wurde auf der Kaggle Data Science Plattform als c.s.v file veröffentlicht. \n",
+ "In den Datensätzen sind allerlei Daten zu den Gästen erfasst. Merkmale zu Familie mit Kindern Buchungen über Reisebüros etc. können Aufschluss \n",
+ "darüber geben, ob bei ihnen eine höhere Stornoquote vorliegt. Der Datenrahmen enthält Buchungsinformationen von 2 verschiedenen Hotels und die\n",
+ "Anzahl der Beobachtungen = 119390, Anzahl der Merkmale = 32\n",
+ "Correlation Analysis: stays in weekend nights and stays in week night with 0.5."
]
},
{
- "cell_type": "code",
- "execution_count": 37,
+ "cell_type": "markdown",
"metadata": {
"editable": true,
"include": true,
+ "jp-MarkdownHeadingCollapsed": true,
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
- "outputs": [],
"source": [
- "df_train['type']='train'"
+ "# 2.1. Import von Relevant Module"
]
},
{
"cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 2,
+ "metadata": {},
"outputs": [],
"source": [
- "df_test['type']='test'"
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "sns.set()\n",
+ "\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn import metrics\n",
+ "from sklearn.metrics import confusion_matrix, classification_report"
]
},
{
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
+ "cell_type": "markdown",
+ "metadata": {},
"source": [
- "df = pd.concat([df_train, df_test], axis=0).reset_index(drop=True)"
+ "## 2.2. Read Data"
]
},
{
"cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 3,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -278,369 +134,292 @@
" <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>hotel</th>\n",
+ " <th>is_canceled</th>\n",
+ " <th>lead_time</th>\n",
+ " <th>arrival_date_year</th>\n",
+ " <th>arrival_date_month</th>\n",
+ " <th>arrival_date_week_number</th>\n",
+ " <th>arrival_date_day_of_month</th>\n",
+ " <th>stays_in_weekend_nights</th>\n",
+ " <th>stays_in_week_nights</th>\n",
+ " <th>adults</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",
+ " <th>deposit_type</th>\n",
+ " <th>agent</th>\n",
+ " <th>company</th>\n",
+ " <th>days_in_waiting_list</th>\n",
+ " <th>customer_type</th>\n",
+ " <th>adr</th>\n",
+ " <th>required_car_parking_spaces</th>\n",
+ " <th>total_of_special_requests</th>\n",
+ " <th>reservation_status</th>\n",
+ " <th>reservation_status_date</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>0</td>\n",
+ " <td>Resort Hotel</td>\n",
+ " <td>0</td>\n",
+ " <td>342</td>\n",
+ " <td>2015</td>\n",
+ " <td>July</td>\n",
+ " <td>27</td>\n",
" <td>1</td>\n",
- " <td>STANDING</td>\n",
- " <td>train</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>...</td>\n",
+ " <td>No Deposit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>Transient</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>Check-Out</td>\n",
+ " <td>2015-07-01</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",
+ " <td>Resort Hotel</td>\n",
+ " <td>0</td>\n",
+ " <td>737</td>\n",
+ " <td>2015</td>\n",
+ " <td>July</td>\n",
+ " <td>27</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>...</td>\n",
+ " <td>No Deposit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>Transient</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>Check-Out</td>\n",
+ " <td>2015-07-01</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>2</td>\n",
+ " <td>Resort Hotel</td>\n",
+ " <td>0</td>\n",
+ " <td>7</td>\n",
+ " <td>2015</td>\n",
+ " <td>July</td>\n",
+ " <td>27</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
" <td>1</td>\n",
- " <td>STANDING</td>\n",
- " <td>train</td>\n",
+ " <td>...</td>\n",
+ " <td>No Deposit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>Transient</td>\n",
+ " <td>75.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>Check-Out</td>\n",
+ " <td>2015-07-02</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>3</td>\n",
+ " <td>Resort Hotel</td>\n",
+ " <td>0</td>\n",
+ " <td>13</td>\n",
+ " <td>2015</td>\n",
+ " <td>July</td>\n",
+ " <td>27</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
" <td>1</td>\n",
- " <td>STANDING</td>\n",
- " <td>train</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>No Deposit</td>\n",
+ " <td>304.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>Transient</td>\n",
+ " <td>75.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>Check-Out</td>\n",
+ " <td>2015-07-02</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>4</td>\n",
+ " <td>Resort Hotel</td>\n",
+ " <td>0</td>\n",
+ " <td>14</td>\n",
+ " <td>2015</td>\n",
+ " <td>July</td>\n",
+ " <td>27</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>2</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>No Deposit</td>\n",
+ " <td>240.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0</td>\n",
+ " <td>Transient</td>\n",
+ " <td>98.0</td>\n",
+ " <td>0</td>\n",
" <td>1</td>\n",
- " <td>STANDING</td>\n",
- " <td>train</td>\n",
+ " <td>Check-Out</td>\n",
+ " <td>2015-07-03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>5 rows × 564 columns</p>\n",
+ "<p>5 rows × 32 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]"
+ " hotel is_canceled lead_time arrival_date_year arrival_date_month \\\n",
+ "0 Resort Hotel 0 342 2015 July \n",
+ "1 Resort Hotel 0 737 2015 July \n",
+ "2 Resort Hotel 0 7 2015 July \n",
+ "3 Resort Hotel 0 13 2015 July \n",
+ "4 Resort Hotel 0 14 2015 July \n",
+ "\n",
+ " arrival_date_week_number arrival_date_day_of_month \\\n",
+ "0 27 1 \n",
+ "1 27 1 \n",
+ "2 27 1 \n",
+ "3 27 1 \n",
+ "4 27 1 \n",
+ "\n",
+ " stays_in_weekend_nights stays_in_week_nights adults ... deposit_type \\\n",
+ "0 0 0 2 ... No Deposit \n",
+ "1 0 0 2 ... No Deposit \n",
+ "2 0 1 1 ... No Deposit \n",
+ "3 0 1 1 ... No Deposit \n",
+ "4 0 2 2 ... No Deposit \n",
+ "\n",
+ " agent company days_in_waiting_list customer_type adr \\\n",
+ "0 NaN NaN 0 Transient 0.0 \n",
+ "1 NaN NaN 0 Transient 0.0 \n",
+ "2 NaN NaN 0 Transient 75.0 \n",
+ "3 304.0 NaN 0 Transient 75.0 \n",
+ "4 240.0 NaN 0 Transient 98.0 \n",
+ "\n",
+ " required_car_parking_spaces total_of_special_requests reservation_status \\\n",
+ "0 0 0 Check-Out \n",
+ "1 0 0 Check-Out \n",
+ "2 0 0 Check-Out \n",
+ "3 0 0 Check-Out \n",
+ "4 0 1 Check-Out \n",
+ "\n",
+ " reservation_status_date \n",
+ "0 2015-07-01 \n",
+ "1 2015-07-01 \n",
+ "2 2015-07-02 \n",
+ "3 2015-07-02 \n",
+ "4 2015-07-03 \n",
+ "\n",
+ "[5 rows x 32 columns]"
]
},
- "execution_count": 40,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
+ "# my_file = project.get_file()\n",
+ "\n",
+ "# my_file.seek(0)\n",
+ "df = pd.read_csv(\"https://storage.googleapis.com/ml-service-repository-datastorage/Prediction_cancellation_of_hotel_bookings_data.csv\")\n",
+ "\n",
"df.head()"
]
},
{
- "cell_type": "markdown",
- "metadata": {
- "Include": true,
- "Paragraph": "Datenvorbereitung",
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
"source": [
- "# 3. Datenvorbereitung"
+ "def attribute_description(data):\n",
+ " longestColumnName = len(max(np.array(data.columns), key=len))\n",
+ " for col in data.columns:\n",
+ " description = ''\n",
+ " col_dropna = data[col].dropna()\n",
+ " example = col_dropna.sample(1).values[0]\n",
+ " if type(example) == str:\n",
+ " description = 'str '\n",
+ " if len(col_dropna.unique()) < 10:\n",
+ " description += '['\n",
+ " description += '; '.join([ f'\"{name}\"' for name in col_dropna.unique()])\n",
+ " description += ']'\n",
+ " else:\n",
+ " description += '[ example: \"'+ example + '\" ]'\n",
+ " else:\n",
+ " description = str(type(example))\n",
+ " print(col.ljust(longestColumnName)+ f': {description}')"
]
},
{
"cell_type": "code",
- "execution_count": 41,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 5,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Total number of observations: 10299\n"
+ "hotel : str [\"Resort Hotel\"; \"City Hotel\"]\n",
+ "is_canceled : <class 'numpy.int64'>\n",
+ "lead_time : <class 'numpy.int64'>\n",
+ "arrival_date_year : <class 'numpy.int64'>\n",
+ "arrival_date_month : str [ example: \"May\" ]\n",
+ "arrival_date_week_number : <class 'numpy.int64'>\n",
+ "arrival_date_day_of_month : <class 'numpy.int64'>\n",
+ "stays_in_weekend_nights : <class 'numpy.int64'>\n",
+ "stays_in_week_nights : <class 'numpy.int64'>\n",
+ "adults : <class 'numpy.int64'>\n",
+ "children : <class 'numpy.float64'>\n",
+ "babies : <class 'numpy.int64'>\n",
+ "meal : str [\"BB\"; \"FB\"; \"HB\"; \"SC\"; \"Undefined\"]\n",
+ "country : str [ example: \"SWE\" ]\n",
+ "market_segment : str [\"Direct\"; \"Corporate\"; \"Online TA\"; \"Offline TA/TO\"; \"Complementary\"; \"Groups\"; \"Undefined\"; \"Aviation\"]\n",
+ "distribution_channel : str [\"Direct\"; \"Corporate\"; \"TA/TO\"; \"Undefined\"; \"GDS\"]\n",
+ "is_repeated_guest : <class 'numpy.int64'>\n",
+ "previous_cancellations : <class 'numpy.int64'>\n",
+ "previous_bookings_not_canceled: <class 'numpy.int64'>\n",
+ "reserved_room_type : str [ example: \"A\" ]\n",
+ "assigned_room_type : str [ example: \"C\" ]\n",
+ "booking_changes : <class 'numpy.int64'>\n",
+ "deposit_type : str [\"No Deposit\"; \"Refundable\"; \"Non Refund\"]\n",
+ "agent : <class 'numpy.float64'>\n",
+ "company : <class 'numpy.float64'>\n",
+ "days_in_waiting_list : <class 'numpy.int64'>\n",
+ "customer_type : str [\"Transient\"; \"Contract\"; \"Transient-Party\"; \"Group\"]\n",
+ "adr : <class 'numpy.float64'>\n",
+ "required_car_parking_spaces : <class 'numpy.int64'>\n",
+ "total_of_special_requests : <class 'numpy.int64'>\n",
+ "reservation_status : str [\"Check-Out\"; \"Canceled\"; \"No-Show\"]\n",
+ "reservation_status_date : str [ example: \"2017-02-27\" ]\n"
]
}
],
"source": [
- "print('Total number of observations: ' + str(df.shape[0]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "Include": true,
- "Paragraph": "Suchen nach fehlenden Daten",
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "### Suchen nach fehlenden Daten\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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()"
+ "attribute_description(df)"
]
},
{
"cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df.isna().sum().sum()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": false,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "--> **no missing values** in the Data Frame"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "Include": true,
- "Paragraph": "Suchen nach Duplikate",
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "### Check for duplicates"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 6,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -663,28888 +442,639 @@
" <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>hotel</th>\n",
+ " <th>is_canceled</th>\n",
+ " <th>lead_time</th>\n",
+ " <th>arrival_date_year</th>\n",
+ " <th>arrival_date_month</th>\n",
+ " <th>arrival_date_week_number</th>\n",
+ " <th>arrival_date_day_of_month</th>\n",
+ " <th>stays_in_weekend_nights</th>\n",
+ " <th>stays_in_week_nights</th>\n",
+ " <th>adults</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",
+ " <th>deposit_type</th>\n",
+ " <th>agent</th>\n",
+ " <th>company</th>\n",
+ " <th>days_in_waiting_list</th>\n",
+ " <th>customer_type</th>\n",
+ " <th>adr</th>\n",
+ " <th>required_car_parking_spaces</th>\n",
+ " <th>total_of_special_requests</th>\n",
+ " <th>reservation_status</th>\n",
+ " <th>reservation_status_date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
+ " <tr>\n",
+ " <td>count</td>\n",
+ " <td>119390</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>119390</td>\n",
+ " <td>103050.000000</td>\n",
+ " <td>6797.000000</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390.000000</td>\n",
+ " <td>119390</td>\n",
+ " <td>119390</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>unique</td>\n",
+ " <td>2</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>12</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>3</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>4</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>3</td>\n",
+ " <td>926</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>top</td>\n",
+ " <td>City Hotel</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>August</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>No Deposit</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Transient</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Check-Out</td>\n",
+ " <td>2015-10-21</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>freq</td>\n",
+ " <td>79330</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>13877</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>104641</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>89613</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>75166</td>\n",
+ " <td>1461</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>mean</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.370416</td>\n",
+ " <td>104.011416</td>\n",
+ " <td>2016.156554</td>\n",
+ " <td>NaN</td>\n",
+ " <td>27.165173</td>\n",
+ " <td>15.798241</td>\n",
+ " <td>0.927599</td>\n",
+ " <td>2.500302</td>\n",
+ " <td>1.856403</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>86.693382</td>\n",
+ " <td>189.266735</td>\n",
+ " <td>2.321149</td>\n",
+ " <td>NaN</td>\n",
+ " <td>101.831122</td>\n",
+ " <td>0.062518</td>\n",
+ " <td>0.571363</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>std</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.482918</td>\n",
+ " <td>106.863097</td>\n",
+ " <td>0.707476</td>\n",
+ " <td>NaN</td>\n",
+ " <td>13.605138</td>\n",
+ " <td>8.780829</td>\n",
+ " <td>0.998613</td>\n",
+ " <td>1.908286</td>\n",
+ " <td>0.579261</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>110.774548</td>\n",
+ " <td>131.655015</td>\n",
+ " <td>17.594721</td>\n",
+ " <td>NaN</td>\n",
+ " <td>50.535790</td>\n",
+ " <td>0.245291</td>\n",
+ " <td>0.792798</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>min</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>2015.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>6.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>-6.380000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>25%</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>18.000000</td>\n",
+ " <td>2016.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>16.000000</td>\n",
+ " <td>8.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>9.000000</td>\n",
+ " <td>62.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>69.290000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50%</td>\n",
+ " <td>NaN</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>69.000000</td>\n",
+ " <td>2016.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>28.000000</td>\n",
+ " <td>16.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>14.000000</td>\n",
+ " <td>179.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>94.575000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>75%</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>160.000000</td>\n",
+ " <td>2017.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>38.000000</td>\n",
+ " <td>23.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>3.000000</td>\n",
+ " <td>2.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>229.000000</td>\n",
+ " <td>270.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>126.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>max</td>\n",
+ " <td>NaN</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>737.000000</td>\n",
+ " <td>2017.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>53.000000</td>\n",
+ " <td>31.000000</td>\n",
+ " <td>19.000000</td>\n",
+ " <td>50.000000</td>\n",
+ " <td>55.000000</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>535.000000</td>\n",
+ " <td>543.000000</td>\n",
+ " <td>391.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>5400.000000</td>\n",
+ " <td>8.000000</td>\n",
+ " <td>5.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>0 rows × 564 columns</p>\n",
+ "<p>11 rows × 32 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]"
+ " hotel is_canceled lead_time arrival_date_year \\\n",
+ "count 119390 119390.000000 119390.000000 119390.000000 \n",
+ "unique 2 NaN NaN NaN \n",
+ "top City Hotel NaN NaN NaN \n",
+ "freq 79330 NaN NaN NaN \n",
+ "mean NaN 0.370416 104.011416 2016.156554 \n",
+ "std NaN 0.482918 106.863097 0.707476 \n",
+ "min NaN 0.000000 0.000000 2015.000000 \n",
+ "25% NaN 0.000000 18.000000 2016.000000 \n",
+ "50% NaN 0.000000 69.000000 2016.000000 \n",
+ "75% NaN 1.000000 160.000000 2017.000000 \n",
+ "max NaN 1.000000 737.000000 2017.000000 \n",
+ "\n",
+ " arrival_date_month arrival_date_week_number \\\n",
+ "count 119390 119390.000000 \n",
+ "unique 12 NaN \n",
+ "top August NaN \n",
+ "freq 13877 NaN \n",
+ "mean NaN 27.165173 \n",
+ "std NaN 13.605138 \n",
+ "min NaN 1.000000 \n",
+ "25% NaN 16.000000 \n",
+ "50% NaN 28.000000 \n",
+ "75% NaN 38.000000 \n",
+ "max NaN 53.000000 \n",
+ "\n",
+ " arrival_date_day_of_month stays_in_weekend_nights \\\n",
+ "count 119390.000000 119390.000000 \n",
+ "unique NaN NaN \n",
+ "top NaN NaN \n",
+ "freq NaN NaN \n",
+ "mean 15.798241 0.927599 \n",
+ "std 8.780829 0.998613 \n",
+ "min 1.000000 0.000000 \n",
+ "25% 8.000000 0.000000 \n",
+ "50% 16.000000 1.000000 \n",
+ "75% 23.000000 2.000000 \n",
+ "max 31.000000 19.000000 \n",
+ "\n",
+ " stays_in_week_nights adults ... deposit_type agent \\\n",
+ "count 119390.000000 119390.000000 ... 119390 103050.000000 \n",
+ "unique NaN NaN ... 3 NaN \n",
+ "top NaN NaN ... No Deposit NaN \n",
+ "freq NaN NaN ... 104641 NaN \n",
+ "mean 2.500302 1.856403 ... NaN 86.693382 \n",
+ "std 1.908286 0.579261 ... NaN 110.774548 \n",
+ "min 0.000000 0.000000 ... NaN 1.000000 \n",
+ "25% 1.000000 2.000000 ... NaN 9.000000 \n",
+ "50% 2.000000 2.000000 ... NaN 14.000000 \n",
+ "75% 3.000000 2.000000 ... NaN 229.000000 \n",
+ "max 50.000000 55.000000 ... NaN 535.000000 \n",
+ "\n",
+ " company days_in_waiting_list customer_type adr \\\n",
+ "count 6797.000000 119390.000000 119390 119390.000000 \n",
+ "unique NaN NaN 4 NaN \n",
+ "top NaN NaN Transient NaN \n",
+ "freq NaN NaN 89613 NaN \n",
+ "mean 189.266735 2.321149 NaN 101.831122 \n",
+ "std 131.655015 17.594721 NaN 50.535790 \n",
+ "min 6.000000 0.000000 NaN -6.380000 \n",
+ "25% 62.000000 0.000000 NaN 69.290000 \n",
+ "50% 179.000000 0.000000 NaN 94.575000 \n",
+ "75% 270.000000 0.000000 NaN 126.000000 \n",
+ "max 543.000000 391.000000 NaN 5400.000000 \n",
+ "\n",
+ " required_car_parking_spaces total_of_special_requests \\\n",
+ "count 119390.000000 119390.000000 \n",
+ "unique NaN NaN \n",
+ "top NaN NaN \n",
+ "freq NaN NaN \n",
+ "mean 0.062518 0.571363 \n",
+ "std 0.245291 0.792798 \n",
+ "min 0.000000 0.000000 \n",
+ "25% 0.000000 0.000000 \n",
+ "50% 0.000000 0.000000 \n",
+ "75% 0.000000 1.000000 \n",
+ "max 8.000000 5.000000 \n",
+ "\n",
+ " reservation_status reservation_status_date \n",
+ "count 119390 119390 \n",
+ "unique 3 926 \n",
+ "top Check-Out 2015-10-21 \n",
+ "freq 75166 1461 \n",
+ "mean NaN NaN \n",
+ "std NaN NaN \n",
+ "min NaN NaN \n",
+ "25% NaN NaN \n",
+ "50% NaN NaN \n",
+ "75% NaN NaN \n",
+ "max NaN NaN \n",
+ "\n",
+ "[11 rows x 32 columns]"
]
},
- "execution_count": 44,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df[df.duplicated()]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": false,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "--> **no duplicated rows** in the data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "Include": true,
- "Paragraph": "Ziel Variable",
- "editable": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "### Target variable"
+ "df.describe(include='all')"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "metadata": {},
"source": [
- "Wir haben ein Klassifizierungsproblem und die Zielspalte ist die Spalte „Aktivität"
+ "## 2.3. Data Cleaning"
]
},
{
"cell_type": "code",
- "execution_count": 45,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 7,
+ "metadata": {},
"outputs": [
{
"data": {
+ "image/png": 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\n",
"text/plain": [
- "array(['STANDING', 'SITTING', 'LAYING', 'WALKING', 'WALKING_DOWNSTAIRS',\n",
- " 'WALKING_UPSTAIRS'], dtype=object)"
+ "<Figure size 1296x1296 with 2 Axes>"
]
},
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- "#Possible classes/labels\n",
- "df['Activity'].unique()"
+ "f,ax=plt.subplots(figsize = (18,18))\n",
+ "sns.heatmap(df.corr(),annot= True,linewidths=0.5,fmt = \".1f\",ax=ax)\n",
+ "plt.xticks(rotation=90)\n",
+ "plt.yticks(rotation=0)\n",
+ "plt.title('Correlation Map')\n",
+ "plt.show()"
]
},
{
"cell_type": "code",
- "execution_count": 46,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 8,
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<AxesSubplot:>"
+ "hotel 0\n",
+ "is_canceled 0\n",
+ "lead_time 0\n",
+ "arrival_date_year 0\n",
+ "arrival_date_month 0\n",
+ "arrival_date_week_number 0\n",
+ "arrival_date_day_of_month 0\n",
+ "stays_in_weekend_nights 0\n",
+ "stays_in_week_nights 0\n",
+ "adults 0\n",
+ "children 4\n",
+ "babies 0\n",
+ "meal 0\n",
+ "country 488\n",
+ "market_segment 0\n",
+ "distribution_channel 0\n",
+ "is_repeated_guest 0\n",
+ "previous_cancellations 0\n",
+ "previous_bookings_not_canceled 0\n",
+ "reserved_room_type 0\n",
+ "assigned_room_type 0\n",
+ "booking_changes 0\n",
+ "deposit_type 0\n",
+ "agent 16340\n",
+ "company 112593\n",
+ "days_in_waiting_list 0\n",
+ "customer_type 0\n",
+ "adr 0\n",
+ "required_car_parking_spaces 0\n",
+ "total_of_special_requests 0\n",
+ "reservation_status 0\n",
+ "reservation_status_date 0\n",
+ "dtype: int64"
]
},
- "execution_count": 46,
+ "execution_count": 8,
"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()"
+ "df.isnull().sum()"
]
},
{
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
"source": [
- "*Im Smart-Farming-Kontext dieser Aufgabe würde ein Rind normalerweise nicht über eine Treppe laufen. Daher werden die Zeilen mit den Bezeichnungen „WALKING_DOWNSTAIRS“ und „WALKING_UPSTAIRS“ entfernt:"
+ "df = df.drop(['reservation_status'], axis=1)"
]
},
{
"cell_type": "code",
- "execution_count": 47,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 10,
+ "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)"
+ "df = df.drop(['stays_in_weekend_nights'], axis=1)"
]
},
{
"cell_type": "code",
- "execution_count": 48,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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"
- }
- ],
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
"source": [
- "df['Activity'].value_counts().plot.bar()"
+ "df = df.drop(['reservation_status_date'], axis=1)"
]
},
{
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
"source": [
- "- For each activity approximately the same number of observations. We could over-sample all minority classes before modelling to have a perfect balanced dataset"
+ "df = df.drop(['arrival_date_day_of_month'], axis=1)"
]
},
{
- "cell_type": "markdown",
- "metadata": {
- "Paragraph": " Wie viele Beobachtungen gibt es zu jedem Thema?",
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
"source": [
- "### How many observations from each subject exists?"
+ "df = df.drop(['arrival_date_year'], axis=1)"
]
},
{
"cell_type": "code",
- "execution_count": 49,
- "metadata": {
- "editable": true,
- "include": true,
- "scrolled": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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"
- }
- ],
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
"source": [
- "df['subject'].value_counts().plot.bar()"
+ "df = df.drop(['arrival_date_month'], axis=1)"
]
},
{
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
"source": [
- "Die obige Abbildung ist interessant. Normalerweise haben alle Probanden den gleichen Versuchsablauf durchgeführt. Daher würden wir erwarten, dass die Anzahl der Beobachtungen von jedem Probanden nahezu gleich sein muss. Es gibt jedoch eine Spanne von 200 bis 300 Beobachtungen über alle Themen hinweg. Ein Grund könnte sein, dass der Übergang von einer Aktivität zur nächsten in der Sequenz nicht scharf oder klar genug war und die Beobachter die unklaren Beobachtungen in diesen instabilen Phasen nachträglich gelöscht hatten. "
+ "df = df.drop(['arrival_date_week_number'], axis=1)"
]
},
{
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Gesamtzahl der Beobachtungen",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
"source": [
- "### Total number of observations"
+ "df = df.drop(['required_car_parking_spaces'], axis=1)"
]
},
{
"cell_type": "code",
- "execution_count": 50,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of observations: 7349\n"
- ]
- }
- ],
- "source": [
- "print('Number of observations: ' + str(df.shape[0]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "PCA fuer Visionalisation",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "### PCA fuer Visualisation\n",
- "**Die Hauptkomponentenanalyse ** ist eine einfache Möglichkeit, hochdimensionale Daten in einem niedrigdimensionalen Raum zu visualisieren. (Achtung: Wir zahlen dafür mit einem Informationsverlust -> aber für Visualisierungszwecke ist es in Ordnung)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "data_visualisation = df.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "s = StandardScaler()\n",
- "data_visualisation = s.fit_transform(data_visualisation)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 53,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": [
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\"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\": 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\"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"
- }
- ],
- "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()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "###Ergibnis von Visualisation mit PCA\n",
- "Nach der PCA / Transformation in 3 Hauptkomponenten können wir die 3 Klassen visuell angemessen trennen.\n",
- "Die 3 Hauptkomponenten beschreiben jedoch nur 62 % der Varianz der Originaldaten. Das bedeutet, dass wir einen relativ großen Informationsverlust haben.\n",
- "Ein zweites Problem besteht darin, dass es schwierig ist, Modelle basierend auf dem Ergebnis einer PCA zu interpretieren\n",
- "-->wir verwenden die PCA nur zur Visualisierung und nicht zur Modellierung"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Funktionsübersicht",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "### Funktionsübersicht"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Aus dem Originalpapier über die Daten/Quelle der Daten können wir die Information entnehmen, dass es die folgenden 17 Hauptmerkmale von Zeit- und Frequenzbereichssignalen gibt "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "data_temp = df.drop('subject', axis =1).drop('Activity', axis=1).drop('type', axis =1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "auf Multikollinearität prüfen",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "### auf Multikollinearität prüfen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "variables = data_temp\n",
- "vif = pd.DataFrame()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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",
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- ]
- }
- ],
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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 × 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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "data_with_important_features = data_temp.drop(features_to_drop, axis = 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 71,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
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- "<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 × 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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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",
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- " <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",
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- " <td>-0.012643</td>\n",
- " <td>1.000000</td>\n",
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- " <tr>\n",
- " <th>tBodyAcc-correlation()-X,Y</th>\n",
- " <td>7349.0</td>\n",
- " <td>-0.024801</td>\n",
- " <td>0.360763</td>\n",
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- " <td>-0.242409</td>\n",
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- " <td>1.000000</td>\n",
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- " <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",
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- " <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",
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- " <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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 0, 'Feautures')"
- ]
- },
- "execution_count": 73,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
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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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Zusammenfassung des Datenverständnisses",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "### Summary of the Data Understanding\n",
- "Zusammenfassung des Datenverständnisses\n",
- "1 Spalte mit der Betreff-ID „subject“ – nur zum Filtern – muss entfernt werden\n",
- "\n",
- "1 Zielspalte „Aktivität“ -> 4 Klassen (GEHEN, SITZEN, LIEGEN, STEHEN) etwas unausgewogen\n",
- "\n",
- "Wir haben ein Klassifizierungsproblem mit mehreren Klassen – überwachtes maschinelles Lernen\n",
- "\n",
- "1 Quelle „Unterstützungsspalte“ -> ist die Beobachtung aus dem Zug oder der Testgruppe -> Zugtest, aufgeteilt entlang dieses Werts\n",
- "\n",
- "Darüber hinaus gibt es viele Merkmale, die aus den 17 Signalmerkmalen im Zeit- und Frequenzbereich berechnet werden. Zum Beispiel Mittelwert, Min., Max., Standardwert, Signalstärkebereich usw.\n",
- "\n",
- "Diese wurden nach Möglichkeit für jede der drei Achsen x,y und z berechnet\n",
- "\n",
- "Zusammenfassend bedeutet das 561 Features -> Aufgrund der hohen Anzahl an Features ist es sehr schwierig, jedes Feature im Detail zu analysieren. Schließlich haben wir Multilinearität in den Daten\n",
- "\n",
- "Nach der Multikollinearitätsprüfung waren nur noch 39 Features übrig. Wir können in den Boxplots einige Ausreißer in einigen Merkmalen erkennen, aber es ist schwierig, diese Ausreißer zu analysieren und zu interpretieren, da es den Anschein hat, dass die Daten bereits teilweise standardisiert sind und wir keine Maßeinheiten haben. Im Moment akzeptieren wir diese Ausreißer\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph ": "Train Test Split",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "### Train Test Split"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 75,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "data = df.copy()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 76,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "data_train = data[data['type']=='train']\n",
- "data_test = data[data['type']=='test']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Data Sampling",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "### Data Oversampling"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 81,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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239v+PrAeWDbkGiIiiiXbw9uY9CZgqe2319NvBX7W9rsmLLcSWFlPvhzYOrQiezcb+FbTRUwT2ZeDlf05WG3Znz9ue2Ri48whF6EubQd869heC6w98uUMjqQx26NN1zEdZF8OVvbnYLV9fw67e2cXMK9jei7w6JBriIgo1rBD//8CCyUtkHQMsBzYNOQaIiKKNdTuHdvPSHoX8FfADOBa21uGWcMR1KruqOe57MvByv4crFbvz6GeyI2IiGbljtyIiIIk9CMiCpLQj4goSEI/IqIgw745a1qSNIfqaiSAR20/02Q9bSJpLjDf9lfq6f8AHFfP/oztbY0V10KSlgFzbf9RPX0HsP+uzMts39BYcS1TjxV2rO3v1tNnA8fUs++x/VRjxfUhR/o9kLRa0u92NN0G3AjcDPxWM1W11oeAEzumfxV4mupO7f/UREEtdxnPvfdlFvAzwGuBdzZRUIv9N+DXOqavp/r9/o/A7zRS0QDkSL83FwH/rGP6cdtn1kcGXwI+2ExZrfRy2zd2TH/P9h8ASPpyQzW12TG2d3ZMf8X248Djkl7UVFEttYTqC3O/79h+oyQBrf2/mSP9Htl+umPyY3XbPuDYZipqrRdMmF7S8f5HhlnINHFS58SEwQwPGHwrJnXUhK7a9wO4urnpuO6rPP8l9HtznKSj90/Yvg5A0izghKaKaqmnJP3k/gnbTwBIegXw3caqaq87JP37iY2SfhW4s4F62uwYScfvn7B9M4CkF3PgwUprpHunNzcA/0PSu2x/D6D+0/kP63lx+K4AbpS0Bri7bjsL+G3gPY1V1V7vA/6XpLfw3P05C7iwqaJa6n8CfyLpHbZ3AEj6ceDqel4rZRiGHtR992uAtwMPUw0ZPQ+4BvidXL0zNZLOoDoBuahuug/4kO37mquq3SS9jmf35xbbX2iynraS9A6qA5D950O+C1xp++rmqupPQr8Pko4FXlZPbrP9D03WExFHhqTjqPKylZdpdkro90DSeZPNt33rsGppO0mfpMuDdGq2fekw62k7SU/RfX/OpLqyJ126h0nSJZPNt/2pYdUySPkP0Jtu1+Ib+CmqB8PM6DI/uruxS9tLgPeS/Thlto/vnK5PRP4a1f0Pn2+kqPb6mS5tAt4IzAFaGfo50h8ASf8U+ADV5XJrbP9ZwyW1kqSXUvWfngd8BLjG9vebraqdJJ1I9cV5CfAZ4CP19frRg/ra/H9Lddnm/VS/519vtqre5Ei/D5KWUN2dZ+C/2r6l4ZJaSdIrqb40z6S6Q/cdORneG0mzgd8A3gxcC5xp+8lmq2ovSTOBf0e1T+8A3mR7a6NF9SlH+j2Q9HqqkHoS+C+2/6bhklpL0p8Co8DvAxuAfZ3z91+3H4dH0tPAOPBJ4ICTjrY/PPSiWkrSKqrLhjdTXbHzcMMlDURCvweSfkj1kPev0eWkme0Lhl5US0l6iGf3oan6TPez7ZcOvagWk/R7HPzEOLYzntFhqn/P91B9iXbuU1H933xVI4X1KaHfA0n/fLL5tr80rFoi4siob8Q6qLYe+Sf0o1GSXj3ZfNt3TzY/nkvSxyebb/vdw6olnp9yIrcHku5l8j+hW/lnX0P+YJJ5Bl43rEKmibuaLmC6mOSeh/3dO60cZytH+j2Yrn/2NUHSMQe7LFPSAtvbh11TBICko23/oOk6Bi2jbPbm/cC3bT/c7afp4lpmo6RjJjZKehXw1w3U03qSVki6W9LT9c/Yoe4uja7uaLqAIyGh35uHgLvqkQyjP3cBN0l64f4GSa8F/gI4YIjgmFwd7u+luq78x6juHL0MeE+Cf8p06EXaJ907Paqfi/thYDbVUKs/3D/P9ueaqquNJH0AWAr8InA+1d24/8r2WKOFtZCk24Hlth+a0D4fWG/77CbqaiNJu6h+x7tq6z0POZHbI9uPSPpzqiGW38izoW8goT8FttdI+geqo34Br8sD0Xt2wsTAB7D9kKRWnnhs0AyqJ2RNqyP+hH4PJC2iOrp/FFhse3fDJbWWpD/j2ZuyRoBtwIeroU5yo1sPJhveO0N/T81u2/+56SIGLd07PZD0APBe2381of0FwBtt/2kzlbVPbnQbLEnfo/riPGAW8FLbeTj6YZL0hO2Tm65j0HKk35uftr0X/vEpWr8AXEzVH/1lIKF/mCYLdUl/AiT0p+aVTRcwjexquoAjIaHfA9t76wepvAV4PdUDp88FFux/Zm4MxGuaLqBtcsnwQE3LkV4T+j2oz+rvoOrX/y3bT0nansCPpknaTpfBwer3tv0Tw6+qteZONqxFW4e0SOj35rPAhVRjlu+TtJFJhmWIg5tk7B0BRw+zlmlidML0UcC/AX4TuGf45bTa/ivKppWcyO1R/SSdn6Pqy/8l4ATgUuAvbH+3ydraRNKkd93a/rlh1TKdSDoKeCvVoz2/SvWQn/sbLaplJN1te9IBAdsooT8Ako6murnoYuAXbM9uuKRpYbqOfXIk1f8X3wa8D/gK8EHbf9dsVe0k6fbpeDNbQn/AJK22/cGm62irjr+g3kJ1+eupDZfUKvX5pmeAj1Kdd3qO3C1++CSdxeSj6bZy2O+E/oBJ2mH7JU3X0TaSfpYq6P8lcDKwCthk+9uNFtYykq7j4EFl228bYjmtVnc9dj7N7Tn71XYrh/1O6A+YpJ225zVdR1tIWkN1onEHcD3weWDM9oJGC5uGJJ1q+7Gm62gLSYuBnfvvuJe0AvjXVAMu/l5bn9+cUTYHL9+iU7MSeIzq8tc/tv042YcDI+nFkt4m6X8DreyOaNB/B/bfhHke8EFgHfAksLbBuvqSSzZ7cIgn6hw75HLa7kd59o7mj9Z/Uh8raabtaXlzzJEm6VjgAqruslcDx1NdYnxrg2W10YyOo/k3A2ttfxb4rKSvNldWf3Kk3wPbx9s+ocvP8bbzRTo1vw58i+qKk5cBG4H/Azwi6TNNFtZGkj4N/C3VF+kfAvOpHvjzRds/nGzdOMAMSft/n5cAX+iY19rf89YWHtPGXOBjwCuAr1MF/rVUDwLJNfpTdwbwbeAB4Bu290lKd1lvrge+JOlbVDdqfRlA0suounhaKSdy43mhfmTiKHAO1Zg7rwGetJ0BxKZI0iuounbeDOyh+kL9J7a/2WhhLSTpbOA04GbbT9dtPwkcl0s2I/og6cVUQX9u/XoicK/tX2myrraRdLbt2zumR6nOl1wE7LJ9TmPFxfNCQj8aJWktsAh4iupB1LcDt+f6/N4cbOiA+qa38/J8gsiJ3GjaS4BZwDeBR6jGMP9OkwVNR64k8CNH+tG8+ih0EVV//jlUJyOfAG6zfUWTtbWNpO8wyaWZefxkJPTjeUPSXKo+/XOANwA/YvvERotqGUkPAm8/2Pwc7UdCPxol6d1UIX8u8APgb4Db6td7c2351Ei6x/aZTdcRz1+5Tj+aNh+4AXjf/jFOoi/flvSj+y/PlHQJ1XgxD9Pi8WJicHKkHzGNSLob+HnbT9Tjxaynuuv5p4FX2n5Tk/VF83KkHzG9HDUdx4uJwcklmxHTy8zpOF5MDE7+E0RML9NyvJgYnPTpR0wz03G8mBichH5EREHSpx8RUZCEfkREQRL6EREFSehHRBQkoR8RUZD/D/+KbF7IHHxTAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "y_train_resampled.value_counts().plot.bar()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Modellierrung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# Modeling "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "Um verschiedene Modelle zu erstellen, empfiehlt es sich, Pipelines und GridSearchCV zu verwenden. Diese beiden Tools sind eine einfache Möglichkeit, Hyperparameter-Tuning und k-fache Kreuzvalidierung auf verschiedenen Modellen durchzuführen. In diesem Fall bestehen die Pipes aus einem StandardScaler und einem One vs Rest-Klassifikator"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "__Build & Evaluate some different Models__"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph ": " KNN",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## KNN\n",
- "K-Nearest Neighbors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 82,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Pipeline for KNN\n",
- "pipeline1 = Pipeline([\n",
- " (\"scaler\", StandardScaler()),\n",
- " (\"knn\", OneVsRestClassifier(KNeighborsClassifier()))\n",
- "])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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"
- ]
- },
- {
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- ]
- },
- {
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " }\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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
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- " vertical-align: middle;\n",
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- " .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"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## Random Forest"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 90,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Pipeline\n",
- "pipeline2 = Pipeline([\n",
- " (\"scaler\", StandardScaler()),\n",
- " (\"ranfo\", OneVsRestClassifier(RandomForestClassifier()))\n",
- "])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 91,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "param_grid = {'ranfo__estimator__max_depth': [5, 15, 30]}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 92,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "[CV] ....... ranfo__estimator__max_depth=5, score=0.793, total= 7.9s\n",
- "[CV] ranfo__estimator__max_depth=5 ...................................\n"
- ]
- },
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- ]
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- ]
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- ]
- },
- {
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Logistic Regression",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## Logistic Regression"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Pipeline\n",
- "pipeline3 = Pipeline([\n",
- " (\"scaler\", StandardScaler()),\n",
- " (\"logreg\", OneVsRestClassifier(LogisticRegression(solver='newton-cg')))\n",
- "])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 97,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
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- " }\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"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Decision Tree",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## Decision Tree"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 101,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Pipeline\n",
- "pipeline4 = Pipeline([\n",
- " (\"scaler\", StandardScaler()),\n",
- " (\"decisiontree\", OneVsRestClassifier(DecisionTreeClassifier()))\n",
- "])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 102,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "paragraph": "SVM",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## SVM\n",
- "Support Vector Machine"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 106,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Pipeline\n",
- "pipeline5 = Pipeline([\n",
- " (\"scaler\", StandardScaler()),\n",
- " (\"svm\", OneVsRestClassifier(svm.SVC()))\n",
- "])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 107,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [],
- "source": [
- "param_grid = {\n",
- " 'svm__estimator__kernel': ['linear']}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 108,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
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- " .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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " }\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"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Zusammenfassung",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "## Summary"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 113,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
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- "<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",
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- " <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",
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- " <tr>\n",
- " <th rowspan=\"5\" valign=\"top\">support</th>\n",
- " <th rowspan=\"5\" valign=\"top\">test</th>\n",
- " <th>DecisionTree</th>\n",
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- " <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",
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- " <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",
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- " </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",
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- " <tr>\n",
- " <th rowspan=\"5\" valign=\"top\">support</th>\n",
- " <th rowspan=\"5\" valign=\"top\">train</th>\n",
- " <th>DecisionTree</th>\n",
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- " <td>1407.000000</td>\n",
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- " <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",
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- " <td>1407.000000</td>\n",
- " <td>1407.000000</td>\n",
- " <td>0.874911</td>\n",
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- " </tr>\n",
- " <tr>\n",
- " <th>LogReg</th>\n",
- " <td>1407.000000</td>\n",
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- " <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"
- }
- ],
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
"source": [
- "final_results.sort_index(level=1)"
+ "df = df.drop(['previous_bookings_not_canceled'], axis=1)"
]
},
{
"cell_type": "code",
- "execution_count": 115,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 18,
+ "metadata": {},
"outputs": [],
"source": [
- "final_results.sort_index(level=1).to_csv('Results_VIF_lower10.csv')"
+ "df = df.drop(['total_of_special_requests'], axis=1)"
]
},
{
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Evaluation",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
"source": [
- "# 5. Evaluation"
+ "df = df.drop(['agent'], axis=1)"
]
},
{
"cell_type": "code",
- "execution_count": 116,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 20,
+ "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"
+ "df = df.drop(['company'], axis=1)"
]
},
{
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "Genauigkeit erreicht",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
"source": [
- "## Achieved accuracy"
+ "df = df.drop(['adr'], axis=1)"
]
},
{
"cell_type": "code",
- "execution_count": 117,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "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"
- }
- ],
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
"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"
+ "df = df.dropna(axis=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2.4. Test for Multicollinearity"
]
},
{
"cell_type": "code",
- "execution_count": 118,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 720x504 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
"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')"
+ "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+ "variables = df[['lead_time', 'is_repeated_guest', 'adults', 'booking_changes', 'previous_cancellations', 'is_canceled', 'stays_in_week_nights', 'babies', 'days_in_waiting_list']]\n",
+ "vif = pd.DataFrame()\n",
+ "vif['VIF'] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
+ "vif['Features'] = variables.columns"
]
},
{
"cell_type": "code",
- "execution_count": 119,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 24,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -29561,191 +1091,127 @@
"\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",
+ " }\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>6</th>\n",
- " <td>RandomForest</td>\n",
- " <td>accuracy_test</td>\n",
- " <td>0.852140</td>\n",
+ " <td>0</td>\n",
+ " <td>2.285568</td>\n",
+ " <td>lead_time</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>7</th>\n",
- " <td>LogReg</td>\n",
- " <td>accuracy_test</td>\n",
- " <td>0.769455</td>\n",
+ " <td>1</td>\n",
+ " <td>1.033605</td>\n",
+ " <td>is_repeated_guest</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>8</th>\n",
- " <td>DecisionTree</td>\n",
- " <td>accuracy_test</td>\n",
- " <td>0.653696</td>\n",
+ " <td>2</td>\n",
+ " <td>3.354523</td>\n",
+ " <td>adults</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>9</th>\n",
- " <td>SVM</td>\n",
- " <td>accuracy_test</td>\n",
- " <td>0.766051</td>\n",
+ " <td>3</td>\n",
+ " <td>1.143147</td>\n",
+ " <td>booking_changes</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",
+ " <td>4</td>\n",
+ " <td>1.037159</td>\n",
+ " <td>previous_cancellations</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",
+ " <td>5</td>\n",
+ " <td>1.759416</td>\n",
+ " <td>is_canceled</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",
+ " <td>6</td>\n",
+ " <td>2.680081</td>\n",
+ " <td>stays_in_week_nights</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",
+ " <td>7</td>\n",
+ " <td>1.015332</td>\n",
+ " <td>babies</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",
+ " <td>8</td>\n",
+ " <td>1.049190</td>\n",
+ " <td>days_in_waiting_list</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"
+ " VIF Features\n",
+ "0 2.285568 lead_time\n",
+ "1 1.033605 is_repeated_guest\n",
+ "2 3.354523 adults\n",
+ "3 1.143147 booking_changes\n",
+ "4 1.037159 previous_cancellations\n",
+ "5 1.759416 is_canceled\n",
+ "6 2.680081 stays_in_week_nights\n",
+ "7 1.015332 babies\n",
+ "8 1.049190 days_in_waiting_list"
]
},
- "execution_count": 119,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df_temp"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "KNN -> Die Leistung bei Testdaten beträgt ca. 8 % niedriger als bei Trainingsdaten. Wahrscheinlich passt das Modell zu stark auf Trainingsdaten und lässt sich nicht gut auf nicht sichtbare Testdaten verallgemeinern.\n",
- "Random Forest -> Die Leistung bei Testdaten ist 15 % geringer als bei Trainingsdaten. Das Modell passt zu stark auf Trainingsdaten und lässt sich nicht gut auf nicht sichtbare Testdaten verallgemeinern.\n",
- "Logistische Regression -> Trainings- und Testleistung sind sehr ähnlich (Differenz = 3,9 %). Dies bedeutet wahrscheinlich, dass sich das erstellte Modell gut auf neue Daten verallgemeinern lässt.\n",
- "Entscheidungsbaum -> Die Leistung bei Testdaten ist 34 % geringer als bei Trainingsdaten. Das Modell passt zu stark auf Trainingsdaten und lässt sich nicht gut auf nicht sichtbare Testdaten verallgemeinern.\n",
- "Support Vector Machine -> Trainings- und Testleistung sind sehr ähnlich (Differenz = 4,8 %). Dies bedeutet wahrscheinlich, dass sich das erstellte Modell gut auf neue Daten verallgemeinern lässt.\n",
- "-->Lassen Sie uns die Verwirrungsmatrix der beiden besten Modelle überprüfen"
+ "vif"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragragh": "Confusion Matrix-Logistic Regression",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "metadata": {},
"source": [
- "## Confusion Matrix-Logistic Regression"
+ "## 2.5. Descriptive Analysis"
]
},
{
"cell_type": "code",
- "execution_count": 120,
- "metadata": {
- "editable": true,
- "include": true,
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
+ "execution_count": 25,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
- "<Figure size 2016x504 with 4 Axes>"
+ "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37350C08>,\n",
+ " <matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37C47A88>,\n",
+ " <matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37C572C8>],\n",
+ " [<matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37C913C8>,\n",
+ " <matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37CC8508>,\n",
+ " <matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37D02608>],\n",
+ " [<matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37D3B708>,\n",
+ " <matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37D73808>,\n",
+ " <matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37D80408>],\n",
+ " [<matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37DB85C8>,\n",
+ " <matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37E1EB48>,\n",
+ " <matplotlib.axes._subplots.AxesSubplot object at 0x000001DE37E54D08>]],\n",
+ " dtype=object)"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x1800 with 12 Axes>"
]
},
"metadata": {
@@ -29755,7 +1221,7 @@
}
],
"source": [
- "make_confusion_matrix(y_test,X_test, clf3)"
+ "df.hist(figsize=(25,25), bins=50)"
]
},
{
@@ -29763,19 +1229,19 @@
"metadata": {
"editable": true,
"include": true,
- "paragraph": "Confusion Matrix SVM",
+ "paragraph": "Datenaufbereitung",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
- "## Confusion Matrix-SVM"
+ "# 3. Datenaufbereitung"
]
},
{
"cell_type": "code",
- "execution_count": 121,
+ "execution_count": null,
"metadata": {
"editable": true,
"include": true,
@@ -29784,36 +1250,41 @@
},
"tags": []
},
- "outputs": [
- {
- "data": {
- "image/png": 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/8Q2jxr5Frx59znrOgwcOMeGbKUye/nngfu+nQsVyTJ74U2pdRuQIbWySKtJi8qIdMBr4ErjOzE48gXgAeBbI65z78CzHPwM8ecq2i4B1zrn9ybQ/jZl1NbM5ZjZnxM8Lz6vzoRIdZYzrWIdJdzdi0eY9rNy+jwSfY/+R43zYviYP1CvLI+Pn4JzjrV+Xc2vlkonVGpJ6omKiKFPlUobc/wr9Wj5G5aZVuezqy8PdrTTN5/PRpE4rqpRrQIWK5bm07MXc16srL+kP6FTh8/loXLsllS+rz5UVy1Pm0osAeOGZ16lSriFffvotne68Jcy99Jakv3MCr65J9l0HbHPOzU3p6ZLZplKZ/y6iYpOR0/86r87/V/E+H8u27uPmK4ox5vaaZIqNYeSsf/h0wVp61b2U7++qz0N1y9J/0ukxU8ncWehYpRR3fzaL7p/P4uK8WYmJMg4fT2DEHyu5++rSpx1TJl82PrilBu+0rsaGvYf9FR3O0fubP3liwnx2HjwaisuOKEMffp2mtzfjhW9fJu6CTMQfP/NcWjGxMVRueBW/f+f/ozpDXAZuuvdmxr7yyWlt1yxZzRM3PkL/tk+S/8L87N62CzOj55sP02NwT7IHHs6kN7GxMTRsWpfvvp58xjbVa1ahTfsbeb7/qwBEx0RT7vJL+ei9cTSr14ZDhw5zz/3+uTHefuM9mtVtzTNPvUyvx+7llYFDaNv+JoaMGESPXneG5JoiUXRMNOWvuJQPRo6hSZ1WHDp0mHsfuOO0dt9/9wN1qjanS/sePPx4j3Oe963XR9K4dkue7jOIhx/vwaDn3qDdbS0ZNvJl7u911zmPl/BJU8kLM8sANAO+cs7tA/4AGgM45zYBP+J/6nFGzrlfAueqdZav0ykwrnS9mRVN5hzDnXOVnXOVu9SJ7D88s8XFUvnC3Py6ejv5s8ZR/+ICmBnlC+Ykyozdh4/x1+Y9DJ62hGuGTeXjuf8wYubfjJm3OtxdT9P27thDjnw5AciRLyf7dvhL1nZt3snSmYvZv3s/x44cY/5P8yhRrmQ4u+oZ+/bt5/dfZ9O4WT2KXliYyb98zu/zJ1GwUH6+n/YpefOlj3LiUNm3bz+/zZhF3QY1T9r+5Wff0ez6Rmc4Kh0JYmlm0t85gdfwJF/pauB6M1sDjAHqm9lHwFYzKwgQ+O+JQewbgKS/14oAmhjmP4jE2KRz7fLJnSLV5M+aiXxZ4ygfmIyz4cUFWLZ1L98u3kiD0gUAaHRxARZv2Zvs8TeWL8ro22oysm11ssdl4MKcF7Bhz0E27j1Mmw9m0Oydn9i2/wi3fDSDHUkSE8453p25kq7VSvP27yvpVqM0zS4tzOg/16T2JUecTas28sxt/eh9XS9+Hf8LW9duOWPbCnUrsnrRKvYGYpMCxQqSr2g+Bk0czJAZw8ldMA8vfvcqOfLmOOm4lj1a89lrY7n5gbaMffUTfvlyGs06NU/mK3hf3YY1WbRwKTu270p2/yVlS/PC4H7c0f7+xGELWzZtZfOmrcwPVCxOGD+FcpdfetJxl5X3Vyf9s2otN7VpTvcuD3PxJRdRvOSFqXg1kWtz4J6dqPL8bvxkyl9x6Rnb//HbXIqVKErOXDlSdP6k97tV2+vp1rkXZcpeRAmv3m8NGwm5pkB24K9AoFaTQHlmgI+Ty2bP5FlOHl+6ErjQzLICOOfeC4wt3QukuYnMdh06yr4j/oz7keMJ/LF2ByVyZaHeRQWYvXYHAGt3HeB4go+cmTLw3i1XM7FbQyZ2a8itlUrSpVpp2lYscbYvIecwd+osaresB0DtlvWYO2UWAAt//pMLLy1GhrgMREVHcWnVy9j49/qznUrOIlfunGTL5p+nJS4uIzXrVGPRwmVUKFOH6hWaUL1CEzZv2krTujezfdvOMPc27Tv1fteqW51Vf68+6Zd846b1WLVCyc9QlWY65x5zzhVxzhXHPxHnj8659sB4oEOgWQfg68D78UBbM8toZiWA0sCs1LgF6Ui6j03yXJCRAlnjWLPLPw/DrHU7KJk7C3mzZGTuhl2BbTu5MEfmZI/fdcifkNi87zA//r2FppcUonTebPx4T0Mm3FmPCXfWI1/WOD5pX5M8Sebr+mbxRmqVzEu2uFiOxCcQZRBl/tgnvcmW218BYWa07NGayR9/f8a2Na+vzYzxvyR+Xrd8LXdU6kD3ml3pXrMrOzfv4JFre7Jn+57ENnVb1Wfej3M4uO8gGeMy4nwOn8+RIe70eUzSg+tvuuaMQ0YKFS7A26Nepefdj7N61drE7du37WTzxq2UvKg44J8z49TJJ3s91p2XBw4hNiaG6Gj/n2nO58iUKY70aPu2HWzauIVSgXtWs3Y1VixfdVKb4iX+H4OUu/xSYmNjU7zC3CNP9OCl598M3G//j1WfzyU7NMUTPDBsJK2NE2gH3OGcGw1gZhcAq80ss3Pu0NkP/T/n3GQzG4B/Eiycc4fMbATwppnd5Zw7Eph9PUMqXEOq23HgKH0m/InPOXwOGpcpRO2L8nM8wUffifNpOXIasVHGgGZXpotZuVNbj9cf5NLq5ciaMxtvznyXz14dw/ihX3D/0Iep26YhOzftYPDd/mW1Du47yIR3x/PsNy/hnGP+T/P488eUVnvLqfLnz8urQ58lOjoaizK+/WoSP0z+Odzd8qz8BfIyeOhzREVHERUVxTdfTmLqpJ8ZPmowpUoXx+fzsXH9Zh59sP+5TyapbSAwzsy6AOuAmwGcc4vNbBywBIgHujsvLPweXopNgN71L+PxCfOJT3AUzp6Z/k0vp26p/Az6aQnxzpExOoonG/srQrYdOMLTk//izZuqAPDQ+HnsOXycmGjj0QaXkS3u3HOSHj6ewDdLNjC0pX+eh/aVSvDQN/OIjfIvn+pl97/ei8sCccewmSMY9+po4jLH0eT2ZgDM+n4mP43zr4CRM18uur3Ynec7DgD8Q0Qur3UFwx8fmuKvlyEuA3Va1eeZ9v5VM75592seGtab+OPxDO7xcpCvLvLFZYqjVt3qPP7ggMRtt3a8GYCP3/+U+x/uRs5cORgwyJ+LTEhIoHkDfz6z76PP89rbzxMbG8u6tRt46N7/z8/QuFk9Fvy5mG2BpcbnzV7IpF8+Z9niFYlLraZHfR55jjeGv0BshljWrdnAg92f5LZOrQH48L1xNLu+Ea3aXE98fDxHDh/h7i7/Xw54yLuDqH51FXLlzsGcRT/w0sAhiROmNmlWn/nzFiUu7T531nym/volSxevYMkizZkWqSxSZ4U3Mx8nl7IOBR4CigfKMk+0+wIY65wba2bvA9865z4L7Cse+FwusOzYQ8656wL7rsf/JKqec25aYHzqAKAVsB84DHwHDHLOHTtTPw+PeCgyb6AHdX56Zbi7kK78sl/3O5Qi9Wexl23cvTjVsreHJ70ZtP9DMzW5V1nmCJFWYpNDw3vqB0oIdXj2n3M3kqCZdUDVfaEU71N+O5QUm5xdxFZeOOeSG9LyXDLtbkryvuMp+9YA5QLvpwHTkuwbT5KJy5xzx4FHAy8REZF/T0sge5JiExERSbM8EJuktTkvRERERERERCSdidjKCxERkTTLA083RERExEM8EJsoeSEiIhJsYVxGTEREROQ0HohNNGxERERERERERCKaKi9ERESCzQOlmSIiIuIhHohNlLwQEREJNg+UZoqIiIiHeCA20bAREREREREREYloqrwQEREJNg+UZoqIiIiHeCA2UfJCREQk2DxQmikiIiIe4oHYRMNGRERERERERCSiqfJCREQk2DxQmikiIiIe4oHYRMkLERGRYPNAgCAiIiIe4oHYRMNGRERERERERCSiqfJCREQk2JwLdw9ERERE/s8DsYmSFyIiIsHmgdJMERER8RAPxCZKXoiIiASbBwIEERER8RAPxCaa80JEREREREREIpoqL0RERILNpf2nGyIiIuIhHohNlLwQEREJNg+UZoqIiIiHeCA20bAREREREREREQkaM2tqZsvNbKWZPZrM/ofNbH7gtcjMEsws19nOqcoLERGRYPPAcmQiIiLiISGMTcwsGhgCNAI2ALPNbLxzbsn/u+MGAYMC7ZsDPZ1zu852XiUvREREgs0DpZkiIiLiIaGNTa4CVjrn/gEwszHADcCSM7RvB4w+10k1bEREREREREREUsTMuprZnCSvrqc0KQysT/J5Q2BbcufKDDQFPj/X11XlhYiISLCp8kJEREQiSRBjE+fccGD4WZpYcoedoW1z4NdzDRkBJS9ERESCzwPLkYmIiIiHhDY22QAUTfK5CLDpDG3bkoIhI6BhIyIiIiIiIiISPLOB0mZWwswy4E9QjD+1kZllB+oAX6fkpKq8EBERCTLn02ojIiIiEjlCGZs45+LN7F5gEhANjHTOLTazboH9wwJNbwQmO+cOpuS8Sl6IiIgEm+a8EBERkUgS4tjEOTcBmHDKtmGnfH4feD+l59SwERERERERERGJaKq8EBERCTZN2CkiIiKRxAOxiZIXIiIiwaY5L0RERCSSeCA20bAREREREREREYloqrwQEREJNk3YKSIiIpHEA7GJkhciIiLB5oEAQURERDzEA7GJho2IiIiIiIiISERT5YWIiEiwubQ/KZaIiIh4iAdiEyUvREREgs0DpZkiIiLiIR6ITTRsREREREREREQimiovREREgs0Da6mLiIiIh3ggNlHyQkREJNhc2i/NFBEREQ/xQGyiYSMiIiIiIiIiEtFUefEfNXnqz3B3Id2Y1P+KcHchXSnbe3W4u5CubDuyN9xdkGDyQGmmpF1N+i8MdxfSlUmPlgl3F9KV8v3Xh7sL6cqOo/vC3QUJFg/EJkpeiIiIBJnzwIzeIiIi4h1eiE00bEREREREREREIpoqL0RERILNA6WZIiIi4iEeiE2UvBAREQk2D8zoLSIiIh7igdhEw0ZEREREREREJKKp8kJERCTYPFCaKSIiIh7igdhEyQsREZFg88CM3iIiIuIhHohNNGxERERERERERCKaKi9ERESCzQOlmSIiIuIhHohNlLwQEREJNg/M6C0iIiIe4oHYRMNGRERERERERCSiqfJCREQk2DxQmikiIiIe4oHYRMkLERGRIHMemNFbREREvMMLsYmGjYiIiIiIiIhIRFPlhYiISLB5oDRTREREPMQDsYmSFyIiIsHmgQBBREREPMQDsYmGjYiIiIiIiIhIRFPlhYiISLB5YC11ERER8RAPxCZKXoiIiASbB0ozRURExEM8EJto2IiIiIiIiIiIRDQlL0RERILM+VzQXiIiIiL/VahjEzNrambLzWylmT16hjZ1zWy+mS02s5/PdU4NGxEREQk2JR1EREQkkoQwNjGzaGAI0AjYAMw2s/HOuSVJ2uQAhgJNnXPrzCzfuc6rygsRERERERERCZargJXOuX+cc8eAMcANp7S5BfjCObcOwDm37VwnVeWFiIhIsPnS/ozeIiIi4iGhjU0KA+uTfN4AVD2lzcVArJlNA7ICrznnPjjbSZW8EBERCTYNGxEREZFIEsTYxMy6Al2TbBrunBuetEkyh53agRigEtAAyAT8bmYznXMrzvR1lbwQERERERERkRQJJCqGn6XJBqBoks9FgE3JtNnhnDsIHDSz6cAVwBmTF5rzQkREJNh8LngvERERkf8qtLHJbKC0mZUwswxAW2D8KW2+BmqZWYyZZcY/rGTp2U6qygsREZEgc05JBxEREYkcoYxNnHPxZnYvMAmIBkY65xabWbfA/mHOuaVm9j2wEPAB7zrnFp3tvEpeiIiIBJsqJkRERCSShDg2cc5NACacsm3YKZ8HAYNSek4NGxERERERERGRiKbKCxERkWBT5YWIiIhEEg/EJkpeiIiIBJnzQIAgIiIi3uGF2ETDRkRERNIoM4szs1lmtsDMFptZ/8D2XGY2xcz+Dvw3Z5JjHjOzlWa23MyahK/3IiIiIimnygsREZFgC93TjaNAfefcATOLBWaY2UTgJuAH59xAM3sUeBTobWZl8S9XdhlQCJhqZhc75xJC1WEREREJA1VeiIiIyGl8QXydhfM7EPgYG3g54AZgVGD7KKBF4P0NwBjn3FHn3GpgJXDVf7lUERERSQNCFJukJiUvREREIpiZdTWzOUleXU/ZH21m84FtwBTn3B9AfufcZoDAf/MFmhcG1ic5fENgm4iIiEhE07ARERGRIAvmpFjOueHA8LPsTwAqmFkO4EszK3eW01lyp/hvPRQREZFI54UJO5W8EBERCbYwBAjOuT1mNg1oCmw1s4LOuc1mVhB/VQb4Ky2KJjmsCLAptD0VERGRkPNA8kLDRkRERNIoM8sbqLjAzDIBDYFlwHigQ6BZB+DrwPvxQFszy2hmJYDSwKyQdlpERETkX1DlhYiISLCFbjKrgsAoM4vG/0BinHPuWzP7HRhnZl2AdcDNAM65xWY2DlgCxAPdtdKIiIhIOhDGiTaDRckLERGRIAvVuFLn3ELgymS27wQanOGYZ4FnU7lrIiIiEkG8MOeFho2IiIiIiIiISERT5YWIiEiweaA0U0RERDzEA7GJkhceV7RUEfq91Sfxc6ELCzLypffJki0L191yLXt27QHgnYEjmPmj5mz7N47GJ9B59O8cT/AR73M0vLgg99S8GIDR81YzZt5aoqOMWiXz0bPupfy1eQ8DJv0VONrRrcbF1L+4QPguwAOioqIY/8Notmzexh239CB7jmy8OeJFChctxMb1m+je+WH27d0f7m6meW8Ne5FrmtZn+/adVKnSBIA+Tz3Iddc2wucc27ftoOtdD7Fl87ZznMn7vFCaKZJSj778EDUaVmP3jj10aHDHSfva3nUz3Z/qxnXlbmTv7n0n7ctXKC9PvPYoufLmxPkc4z/+js9GfAFA1hxZ6f9WHwoUzc+W9Vt5qtvTHNh7gPKVL6PX8w9w7Ngx+nd/lo1rNpEl2wX0f6sPvW59NGTXHG77jx6n/49LWbXzIGbQt35ZPlmwjjV7DgX2x5M1Ywxj21ZN0bFXFMzO3iPH6T1pEZv2HaZQtky82KQc2eJimb95D89NW05stPF843JcmCMz+48ep/f3ixhyfQXMkluB2bs6dbuV1u1bgHMsX7qSR3r049jRY4n7q15dibc/fIX1a/0LOU367kfefOkdAH6e9y0HDxwkIcFHQkICLRq2B+CRp+6jToOrWbpoOQ91fwqAFjdfS46c2Xh/+OjQXmAEKVy4IO+8+wr58+fF5/Px3sjRDB36XrJtK1a6nGnTvuT22+7lq68mAnDPPZ3o1KktmPH+e2MYMmQkAAMGPErjxnVYuHAJd97ZC4B27W4kZ84cZzy/F3ghNomoYSNm9oSZLTazhWY238yqmtk0M6tsZn8Etq0zs+2B93+Z2Z7A+y1mtjHwfr6ZZTCzA4HzFjczZ2Y9knytN82sY5LPD5rZssA5F5jZK2YWG4bbEFTrV22gS+O76NL4Lu5sejdHDh9l+sQZAHz6zmeJ+5S4+PcyREfxTptqjOtYm7EdavHbmu0s3LSb2et2MO3vrXzasRZfdK5DhyolAbgoT1Y+uf1qxnWsxZBWVzFgyl/E+zyQCg2jTnfdysoV/yR+vvv+zvw6fRb1r7qeX6fP4u4HuoSxd97x0Yef0aJFh5O2DX51OFWrXkP1as2YOPFHHnvs/jD1TiT1KD45u4njJvHQrY+dtj1fobxUqV2JLRu2JntcQnwCQ/oP47a6nbmr+b3c1PEGipcuBkD77u2YO2Met9TswNwZ82jfvR0Abe66mSe79mP4wJG0uP16ADo8cBsfvvFJKl1dZHpx+gpqXJibL9tXZ2zbqpTMlZkXmpZnbNuqjG1blQal8lG/ZN4UHwvw3tw1XFUkJ+Nvq8FVRXLy3ry1AHz45zoGXVOeHtVK8emijQAMn72GzpWLp7vERf4CeelwZ1taNGzPNbVaExUVRfMbm5zWbvbM+TSv147m9dolJi5OuLXFXTSv1y4xcZElaxYqVrmCa+u0ISo6iosvvYiMcRlp2a45H438NCTXFakSEuJ5/LFnqFSxIfXq3kjXu27jkksuOq1dVFQUzwx4lKlTpyduK1v2Yjp1akvt2jdQreo1XHNNfUqVKk62bFmpWq0iVateQ3R0NJddVoa4uIy0b9+K4cM/DOXlyb8QMckLM6sOXAdUdM5djn+5t/Un9jvnqjrnKgBPAWOdcxWcc+WdczkC24cBrwa2V3DOHTvlS2wD7jezDMl87W5AY6Cac648UCXQPlPQLzSMKtW8kk1rN7F1o56KBpOZkTmDv4gp3ueIT/BhwLj56+hU9SIyxEQDkOuCjABkio0mJsr/T+9YvL+t/HsFCuWjXuNajP3oy8RtjZrV4/Mx4wH4fMx4GjerF67uecqvv85i1669J23bv/9A4vsLLsiMc2k/qx8UviC+JKwUn5zbgj/+Yt+efadt79HvHoY+O/yMPxd2btvFikV/A3D44GHW/L2WPAXyAFCzSQ2+/3QyAN9/OplaTa8GID4+noxxGYnLlJH44/EUKlaQvAXyMH/mwtS4tIh04Fg88zbt4cayhQCIjY4ia8b/57Occ0xZuZWmyVR1nu3Yaat30PySggA0v6QgP/2zHYCYKONofAKH433ERBnr9x5i28EjVC6cM1WvM1LFxEQTF5eR6OhoMmXOxNYt2//T+ZzzERuII+Pi4og/Hs+d997OqHdGEx8fH4wup1lbtmxn/vzFABw4cJDly1dRqNDp39d3392Rr76eyPZtOxO3lSlzEbNm/8nhw0dISEjglxl/cP31TfD5fGTI4P9xG5cpjuPH43mg510Mfet9799vD8QmEZO8wL/c2w7n3FEA59wO59ymIJ5/O/AD/1/3PqkngLudc3sCX/uYc26gc+7038RpWP0b6vHDVz8mfr6xUwvem/IOvV9+iCzZs4SxZ2lfgs/R+v1fqD9kCtWK56F8oZys3XWQeRt20f6jX+ky+ncWbd6T2P6vTbu5aeTPtHp/Ok82Kp+YzJDz99SzjzCw36v4klSv5Mmbi+1bdwCwfesOcufJFa7upQt9+z3E8hW/0abNDTwz4JVwdyciOF/wXhJ2ik/+hasbVWf75h2sWvLPuRsDBYrk5+JyF7Hkz6UA5MyTk53bdgH+JEfO3DkA+OjN0Tz8Yk9uvrMlX7z/FV17d+HdQd4t807Oxr2HyZkpA31/WErbMX/Q/8elHD7+/xWP523aQ65MGSiWI/N5Hbvz0DHyBh605L0gI7sO+/NsnSsV55mflvHJgnW0vbwIb85cxT1VS4XgSiPP1i3beXfIh/wyfwK/L57M/n37mTFt5mntrqxcnm+njWHkmDcoXaZk4nbnHO9/NoSvf/iYtrffBMDBA4eY9O2PfPPTaNav28j+/Qe4/MrLmDrx55BdV1pw4YVFuOKKssyePf+k7QUL5af59U14952PT9q+ZMlyrr76KnLlykGmTHE0aVKPwkUKcuDAQb7+aiK/z5zA2jXr2bdvH5UqXc53304J4dWEhxdik0j6i2kyUNTMVpjZUDOrkwpfYyDQy8yiT2wws6xAFufc6pSexMy6mtkcM5uz+eDGVOhm8MXExnB14xr89K2/nOqrD76hXY3b6Ny4Kzu37aL7U93C3MO0LTrKGNexFpO6NWDR5j2s3L6fBOdj/9HjfHhrDR6oeymPfDMv8elT+UI5+aJzHT6+7WpG/LGSo/EJ5/gKkpz6jWuzY8cuFi1YGu6upGv9+71EmYtrMHbs19zVLbm/v0TStDQRnySNTbaEOTbJGJeR2++7lREvvZ+i9pkyx/HMO/14ve9QDh04dNa2KxevolvzHtx/cy8KXViQHVt3Ymb0e+tJ+rz+GDnzeL8aIN7nWLZ9PzeXK8yYtlXJFBPFyLlrEvd///dWml6c/18dm5wyebPywc1VeOfGSmzYe5i8mTOCg97f/8UTkxez89DRIF5dZMuWPSsNr6lL3UrXUaNcEzJnzsQNNzc7qc3iBcuofeW1XFe3LR+8O4ZhH/4/qd/62k7cUP9WOre5l/adW1OlekUAhr8xiub12vH8U6/S87G7GTzwLVq3b8Hr7w6k+4Ma+nrBBZn5ZPRbPPLI0ydVfAK8+OJT9Hly4EkPsQCWL1/FK68M45tvP+Krr0fx119LSQjE26+++jbVqzXjsceepc9TvXhmwKt06NiGDz58k0d63xuy65LzFzHJC+fcAaAS0BX/U4ixScd8BulrrAZmAbck2WxAYj2jmTUJjEldY2Y1znCe4c65ys65ygUvKBzMLqaaavWu4u+//mb3jt0A7N6xG5/Ph3OObz/+jksrXBLmHnpDtrhYKhfNza+rt5E/Sybqly6AmVG+YA6iMHYfPrlauGTurGSKjWblDk0m+W9UqlqBhk3r8sufE3jjnReoUasKrw57jh3bd5E3v7/0OG/+POzcsSvMPU0fxo79mhY3NA13NyKDB0ozxS+txCdJY5MCYY5NChcvRMELC/DelOGMm/kxeQvmZcSkYeTKe3piITommmfe6ceUL39InJML/HFK7nz+qrnc+XKxe+ee0469/f72vD/4Qzr2vI2RL41i0hdTadXlxlS7rkiRP0tG8mXJSPkC2QFoeFE+lm33xxHxPh8/rtpGk9LJJy/OdmzuzBnYftCfiNh+8Ci5Mp08ksk5x7tz1tC1Sgnenv0P3aqWpFmZAoxesCFVrjMSXV2nKuvXbmTXzj3Ex8cz6dsfqVjl8pPaHDhwkEMHDwMwbeqvxMTEkDNXDgC2bfFXhe7csZvJE37iioqXnXRs2fJlAFi9ai03trmO++54lIsvvYjiJYum8pVFrpiYGD75ZBhjx3zF+K8nnba/YsXLGfXBGyxZOoMWN17D4MEDuK55YwA+GDWOq2tcR5PGbdi9ew8rV52cC77iCv/9//vvf7jllpu4/bZ7KVu2DKVKFU/16woLD8QmEZO8AHDOJTjnpjnn+gL3Ai1T4cs8B/QmcO2B0suDZlYi8HlSYIzqIuC08adpVYMW9ZmaZMjIiYAAoNY1NVm9fE0YeuUNuw4dZd+R4wAcOZ7AH2t3UCJ3FuqVzs/sdf5fUmt3HeC4z0fOTBnYuOdQ4gSdm/YeYu2ugxTKdnppp5zboAGvU6N8Y2pd2Ywed/bmt19m07Pb40ydOI2Wba8HoGXb65ky4acw99S7kv6Cv/bahixfsSp8nYkgXijNlP9TfHJ+/lm2muuvaEXrarfSutqtbN+8nS5NurFr++7T2j768kOsWbmOscM/O2n7r5N/o+nN/j9Amt7cmBmTfjtp/zWtm/D7D39wYO8B4jLF4ZzD+XzEZcqYehcWIfJckJECWTKyZvdBAGat303JXBcA8Mf63RTPeQH5s8Sd97F1SuThm2WbAfhm2Wbqlshz0rHfLNtMreK5yRYXy5F4H1EYUQZH0lH16KYNW6hQuTxxmfz3t0btq1i14uQ/iPPky534/vIrLyMqyti9aw+ZMsdxQRZ/vJcpcxy16lZjxdKTf2f2fOweBg98i5iYGKIDQ4p9Pl/i10uP3nrrBZYvX8kbb4xIdv9lZWtR9tKalL20Jl99OZEHHujDt9/458vJm9f//0WRIoW4/vqmfDpu/EnH9nnqQQYMeIXY2Fiio/2Fb87nI3PmiJpWKGi8EJtEzFKpZlYG8Dnn/g5sqgCsBcoF8+s455aZ2RL8k2+dWGLjeeAtM2vrnNtj/qmTPfNTImNcRirXrsRLvV9N3Nbtya6ULlsK52DLhi0n7ZPzs+PAUfpMXIDP5/DhaFymELVL5ed4go++ExfQ8r2fiY2KYsA1V2Bm/LlxFyO/WEVMVBRRBo81KkfOzBEdh6Y5b702kjdHDqL1rS3YtHEL3Ts9FO4uecL7779OrdrVyJ07Jyv+/p1nnnmVJk3qcXHpkvh8Ptat38h99z0R7m6KBJXik3PrO+QJrqx+BdlzZefzOWMY+dIovhszMdm2ufPnpvegXjxy++OUr1KOpq0as2rJP4yc/DYAwwNLt380ZAxPD+vDte2uYdvGbfS56+nEc2SMy0jTmxvzYLtHABg7/DMGDO9L/PF4+nd/NvUvOAL0rl2GxycvJt7nKJwtjv4NygIwKZkhI9sOHOXpn5byZvMKZz22U8Xi9J70F18t2UTBrHG82LR84jkOH0/gm2WbGXr9lQC0r1CUhyYuJDY6iucbB/WfQkRbMG8R33/zA+N//JiE+AQW/7WcMR98QbuO/nzm6Pc/55rmDbmlUysS4hM4cuQo99/pX4knT97cvDXqZcBfcfTN598z/cf/J+UaXVOXhX8uTqzO+HPOQiZMH8uyJX+zbPHfpEfVq1fmlltbsuivpfw+cwIA/fq+SJGi/uqyEe9+fLbD+fiTt8iVKyfxx+N5sGcf9iSZWPi65o2ZO3dh4vLus/6Yx6xZ37No0TL++kvDkSOVRcrM8GZWCXgDyAHEAyvxl2h+BjzknJsTaNcRqOycu/eU4/sBB5xzLyXZdsA5l8XMigPfOufKBbZfAfwJdHbOvR8IBnoBdwJHgQPAr8AzzrmTp9Y/Re3CDSLjBqYDk/pfEe4upCtle/8Q7i6kK9sOn/VHjaSCg4fWpNpiPzua1Ana74Y8k37WokRhlBbjk1qKTUJq0qNlwt2FdKV8/9MnyJTUs+WQht6GkmKTs4uYygvn3FwguTkm6p7S7n3g/WSO75fMtiyB/64hyRMS59wCkgyZcf4MzkuBl4iIyH+i4R7eofhERES8wAuxSUTNeSEiIiIiIiIicqqIqbwQERHxCi883RARERHv8EJsouSFiIhIkHkhQBARERHv8EJsomEjIiIiIiIiIhLRVHkhIiISbE4LhIiIiEgE8UBsouSFiIhIkHmhNFNERES8wwuxiYaNiIiIiIiIiEhEU+WFiIhIkDlf2i/NFBEREe/wQmyi5IWIiEiQeaE0U0RERLzDC7GJho2IiIiIiIiISERT5YWIiEiQOQ/M6C0iIiLe4YXYRJUXIiIiQeZ8wXuJiIiI/Fehjk3MrKmZLTezlWb2aDL765rZXjObH3g9da5zqvJCRERERERERILCzKKBIUAjYAMw28zGO+eWnNL0F+fcdSk9r5IXIiIiQeaFGb1FRETEO0Icm1wFrHTO/QNgZmOAG4BTkxfnRcNGREREgsy54L1ERERE/qsQxyaFgfVJPm8IbDtVdTNbYGYTzeyyc51UyQsRERERERERSREz62pmc5K8up7aJJnDTk17zAOKOeeuAN4AvjrX19WwERERkSDTsBERERGJJMGMTZxzw4HhZ2myASia5HMRYNMp59iX5P0EMxtqZnmcczvOdFIlL0RERIJMyQsRERGJJCGOTWYDpc2sBLARaAvckrSBmRUAtjrnnJldhX9UyM6znVTJCxEREREREREJCudcvJndC0wCooGRzrnFZtYtsH8Y0Aq428zigcNAW+fOPqOGkhciIiJBpok2RUREJJKEOjZxzk0AJpyybViS928Cb57POc+YvDCzNzh9Uo2kX/i+8/lCIiIi6YWGjaQexSciIiLnzwuxydkqL+aErBciIiIiKaP4REREJB06Y/LCOTcq6Wczu8A5dzD1uyQiIpK2OZf2n25EKsUnIiIi588LsUnUuRqYWXUzWwIsDXy+wsyGpnrPRERE0ijnC95Lkqf4REREJOW8EJucM3kBDAaaEFi2xDm3AKidin0SEREROZfBKD4RERFJN1K02ohzbr3ZSWUmCanTHRERkbTP54HSzLRA8YmIiEjKeCE2SUnyYr2Z1QCcmWUA7iNQoikiIiKn88K40jRA8YmIiEgKeSE2ScmwkW5Ad6AwsBGoEPgsIiIiEi6KT0RERNKRc1ZeOOd2ALeGoC8iIiKe4IW11COd4hMREZGU80JskpLVRkqa2Tdmtt3MtpnZ12ZWMhSdExERSYucC95Lkqf4REREJOW8EJukZNjIJ8A4oCBQCPgUGJ2anRIREUnLnM+C9pIzUnwiIiKSQl6ITVKSvDDn3IfOufjA6yNAz4JEREQknBSfiIiIpCNnnPPCzHIF3v5kZo8CY/AHBW2A70LQNxERkTTJC8uRRSrFJyIiIufPC7HJ2SbsnIs/GDhxlXcl2eeAAanVKRERkbTMC8uRRTDFJyIiIufJC7HJGZMXzrkSoeyIiIiIyLkoPhEREUmfzrlUKoCZlQPKAnEntjnnPkitTomIiKRlWiUkNBSfiIiIpIwXYpNzJi/MrC9QF39wMAG4BpgBKDgQERFJhhfGlUY6xSciIiIp54XYJCWrjbQCGgBbnHOdgCuAjKnaKxEREZGzU3wiIiKSjqRk2Mhh55zPzOLNLBuwDSiZyv0SERFJs7wwKVYaoPhEREQkhbwQm6QkeTHHzHIA7+Cf4fsAMCs1OyUiIpKWeWFcaRqg+ERERCSFvBCbnDN54Zy7J/B2mJl9D2Rzzi1M3W6JiIiInJniExERkfTljMkLM6t4tn3OuXmp06W0ZU/CoXB3Id2o1Wd2uLuQriz5oFO4u5CuFGrzZri7IEHkhUmxIpXik3Pbq9gkpGo+p5xZKC35Y2i4u5CuFLiifbi7IEHihdjkbJUXL59lnwPqB7kvIiIinuCFcaURTPGJiIjIefJCbHLG5IVzrl4oOyIiIiJyLopPRERE0qeUTNgpIiIi58ELpZkiIiLiHV6ITZS8EBERCTIPTOgtIiIiHuKF2CQq3B0QERERERERETmbc1ZemJkBtwIlnXNPm9mFQAHnnNZSFxERSYYXSjMjneITERGRlPNCbJKSyouhQHWgXeDzfmBIqvVIREQkjXPOgvaSM1J8IiIikkJeiE1SMudFVedcRTP7E8A5t9vMMqRyv0RERETORvGJiIhIOpKS5MVxM4smMMeHmeUFfKnaKxERkTRMvyRDQvGJiIhICnnhF2RKkhevA18C+czsWaAV8GSq9kpERCQNc2i4RwgoPhEREUkhL8Qm50xeOOc+NrO5QAPAgBbOuaWp3jMRERGRM1B8IiIikr6kZLWRC4FDwDdJtznn1qVmx0RERNIqnxcWU49wik9ERERSzguxSUqGjXyHfzypAXFACWA5cFkq9ktERCTN8nmgNDMNUHwiIiKSQl6ITc65VKpzrrxz7vLAf0sDVwEzUr9rIiIiIslTfCIiIhK5zKypmS03s5Vm9uhZ2lUxswQza3Wuc6ak8uIkzrl5ZlblfI8TERFJL7wwKVZao/hERETkzEIZmwRWAxsCNAI2ALPNbLxzbkky7V4AJqXkvCmZ8+LBJB+jgIrA9hT2W0REJN3xwnJkkU7xiYiISMqFODa5CljpnPsHwMzGADcAS05p1wP4HEjRw4eUVF5kTfI+Hv8Y089TcnIRERGRVKL4REREJDIVBtYn+bwBqJq0gZkVBm4E6hOM5EWgjCOLc+7h8+qqiIhIOqZhI6lL8YmIiMj5CWZsYmZdga5JNg13zg1P2iTZLpxsMNDbOZdglrK+nTF5YWYxzrl4M6uYojOJiIgIoGEjqUnxiYiIyPkLZmwSSFQMP0uTDUDRJJ+LAJtOaVMZGBNIXOQBmplZvHPuqzOd9GyVF7Pwjx+db2bjgU+Bg0k6/MVZjhURERFJDYpPREREIttsoLSZlQA2Am2BW5I2cM6VOPHezN4Hvj1b4gJSNudFLmAn/rEoJ9ZTd4CCAxERkWSo8iIkFJ+IiIikUChjk0CF5L34VxGJBkY65xabWbfA/mH/5rxnS17kC8zkvYj/BwWJ/fk3X0xERCQ9CNWcF2ZWFPgAKIA/LhnunHvNzHIBY4HiwBqgtXNud+CYx4AuQAJwn3MuRcuTRRDFJyIiIucp1PNxOecmABNO2ZZs0sI51zEl5zxb8iIayELKJtsQERGR0IsHejnn5plZVmCumU0BOgI/OOcGmtmjwKNAbzMri7908zKgEDDVzC52ziWEqf//huITERGRdOhsyYvNzrmnQ9YTERERj/CF6OGGc24zsDnwfr+ZLcW/PNkNQN1As1HANKB3YPsY59xRYLWZrcS/FvvvoelxUCg+EREROU+hik1S09mSFx64PBERkdDzhXY5shPtigNXAn8A+QOJDZxzm80sX6BZYWBmksM2BLalJYpPREREzlMwY5NwOVvyokHIeiEiIiLJSsFyZJhZFuBz4AHn3L6zrJfuhaEWik9ERETSoTMmL5xzu0LZEREREa8IZTbAzGLxJy4+TrJM6FYzKxiouigIbAtsT8m66xFN8YmIiMj5S2tPKpITFe4OiIiIeI0viK+zMX+JxQhgqXPulSS7xgMdAu87AF8n2d7WzDIG1l4vDcz61xcqIiIiaUKoYpPUdLZhIyIiIhLZrgZuA/4ys/mBbY8DA4FxZtYFWAfcDBBYY30csAT/SiXd09hKIyIiIpJOKXkhIiISZL4zzzkRVM65GZx5Astk54Zwzj0LPJtqnRIREZGIE6rYJDUpeSEiIhJkXhhXKiIiIt7hhdhEc16IiIiIiIiISERT5YWIiEiQhXMyKxEREZFTeSE2UfJCREQkyHxpf1ipiIiIeIgXYhMNGxERERERERGRiKbKCxERkSDznXEBEBEREZHQ80JsouSFiIhIkHlhRm8RERHxDi/EJho2IiIiIiIiIiIRTZUXIiIiQeaFSbFERETEO7wQmyh5ISIiEmReWI5MREREvMMLsYmGjYiIiIiIiIhIRFPlhYiISJB5YVIsERER8Q4vxCZKXoiIiASZF8aVioiIiHd4ITZR8iIdmDj7Cw4dOERCQgIJCQm0a9IZgHZdWtGuUyviExL4ZepvvDpgSJh76g1ZsmWhz8u9KXVJCZxzPN1zIFc3qEadJrXw+Xzs3rmbfvc/x46tO8Pd1TTp6PF4Or/5NcfjE4j3+Wh4RUnuaXoVyzfu4NnPpnPo6HEK5crKc+0bkiUuA78vX8/r383keLyP2JgoejavzlWli4T7MtKkwoUL8tY7g8iXPw8+n2PUe2N4e+gobrjxGno/fh9lypSiQZ2bmP/nonB3Ney8MK5UJKX6v/oEdRrVYNeO3dxUtz0A2XJkY9DbAyhUtCCb1m/moa5Psn/v/tOOzZotC/1eeYyLypTCOcdTPZ9l4dxFNGpen7sf6kLJ0sW55ZouLFmwDIAKVS7nyRce5tixY/Tu1pf1azaQNVsWXnx7AHe36xnS6w6Xvq88Rq3A/W5d73YAGl5Xj7se6kyJ0sW4rdmdLF2w/LTjipUqysBhTyd+LlysEMMGvcsn73zK3Y/cQd0mNfH5HLt27qbv/c+yY+tOrqhSnscH9uLYseM8fnc/1q/ZSJZsWXjh7f50b9crZNccbh9++T1fTPoZzChdvAgDet7BO2O+4aeZ84iKiiJX9qwMePBO8uXOedJxqzds5pGBQxM/b9i8jXtuu4nbWjQB4JPxUxj9zVRioqOoVaUCD3Zpw5+LV/DMkFFkiI3lhd53c2Gh/Ow7cJBHBg7lrQEPYeaBv0DPU1RUFD/98hWbN22h7c1dT9s/cFAfGjWuy+HDh7nnrt4sXLD4jDELQL+nH6Zh4zr8tXApd3d9GIA2bVuQI1f2xDZe5IXYJFXnvDCzJ8xssZktNLP5ZvZT4L8rzWxv4P18M6sRaL/AzEafco73zWyjmWUMfM5jZmsC74ub2WEz+9PMlprZLDPrkOTYjmb2ZuB9PzM7ZGb5kuw/kOR9fjP7xMz+MbO5Zva7md2YmvcnlLq07E7rhh0SExdVrq5IvSa1aVn/Nm6qcyuj3vokzD30jocG3MdvP/1Bq1rtadegE6v/XsuHQ0fTrkFHbm3UmV+m/MadD3YMdzfTrAwx0bxzz/WMe7g1Yx+6md+WrWfhmi30HzeN+66txmePtKF++RKM+mk+ADkviOO1Ls347JE2DGhXnyc+/jG8F5CGxcfH8+Rjz1OtUlMa12vFHXe2p8wlF7F0yQpuv+Uefvt1dri7KHJOik2Cb/zY705LHHTpcRt//DKH5jVa88cvc+jS47Zkj+39TE9+/XEmN9RqS6sGt7H67zUArFy2igc7P8bcmfNPan97t3Y82OUxXn9uGK07+m9F1wc78e7rHwT9uiLVN+MmcO8tJycOVi3/h4e6PM68mQvOeNzaVetp16gT7Rp14tYmXThy+Ag/TZwOwAdDP6FNg460a9SJX6b8RtcHOwFwW7e2PHTHk7z5/Nu06uC/33f27MiI1z9MpauLPFt37OLj8VMY/Vp/vnzrOXwJPr7/+Q86tmrG50Of5dM3B1D7qgq8/cnXpx1bokhBPn1zAJ++OYAxr/UnLi4jDapXAmDWgqX8NHMenw99hi+HPU+HltcA8MGX3/PKEz24r0Mrxn3nj1mGjx7PHW2ap8vEBUC3ezqyYvnKZPc1alyHUqWKU+mKBjzQ40leHtwfOHPMki1bFq6qVpGa1a4jOjqKspddTFxcRtq1v4kRwz8O5WXJv5BqyQszqw5cB1R0zl0ONARudc5VAO4AfnHOVQi8fjOzSwP9qW1mF5xyugSg8xm+1Crn3JXOuUuBtkBPM+t0hrY7gNPSxOb/SfAVMN05V9I5VylwLs8+nm3d4SZGvPEhx48dB2DXjt1h7pE3XJAlM1dWu4KvP/kWgPjj8RzYd4CDBw4ltsmUORPOC4POwsTMyJwxFoD4BB/xCT7MjLXb9lCpVEEAql1clB8W/gPAJUXyki+7/0dKqQK5OBYfz7H4hPB0Po3bunU7CxcsBuDAgYOsWL6KggXzs2L5Klb+vTrMvYssviC+JHgUm6SOuTPns3fPvpO21WtSi/HjJgAwftwE6jetfdpxF2TJTKVqFfjik28A/+/M/fv8uZvVf69lzap1px0THx9PxriMxGWKI/54PEWKFSZfgbzM/f3PYF9WxJo3cwF7d598v1f/vZa1q9an+BxX1arEhjUb2bxhK8ApcUocLhCoxB+PJ+6k+12IfAXyMO/3+f/9QtKQhAQfR48dIz4hgSNHj5E3dw6yZM6UuP/wkaNwjrzCHwsWU7RAXgrlzwPAuO9+oMvN15Eh1h/T5M6RDYCY6GiOHjvGkaPHiImJZv3mrWzbuZvK5S9JnYuLcIUKFaBx07p8MGpcsvubXdeQMaO/BGDO7Plkz56N/PnznjFm8fkcsYF7HpcpjuPH4+nxwJ28/dYHxMfHh+aiwsQLsUlqDhspCOxwzh0FcM7tOEf7W4APgUuB64GkTzkG4//F/87ZTuCc+8fMHgReBt5LpslIoKOZveCc25Vke33gmHNuWJJzrQXeOEef0wbneHvMazjn+PTDr/j8o68pVrIolapdwX2P3cXRo8d4uf8bLJ6/NNw9TfMKFyvEnp176Dv4cS4uW4qlC1fwUp/XOHL4CPc8eifNWjXh4P6D3NXq/nB3NU1L8Plo98pnrN+xlzZXl6N8sfyUKpiLaYvXUK9cCaYsWMWWPQdOO27qwn+4pHAeMsREh6HX3lL0wsJcfkVZ5s4581O+9Mylz4djaYFikxDJlTcXO7b5h0fu2LaTXHlyntamSLHC7Nq5hwGvPcnFZUuzdOEyXujzKocPHTnjeUe8/gF9X3qUI0eO8sS9/Xmwbw+GvDA81a7Dq5rc0JBJX009aVv3R7tybasmHNh/kK6t7gNg5Bsf8uSgRzhy5Ch9egyg51PdGfriu+Hoctjkz5OLDjddQ+MODxKXIQPVK5ajRsXyALw+6jO++eFXslyQiREDHz3reb7/+Q+uqVst8fPaTVuZu3g5r4/6jIwZYul1R1vKXVySLq2vo//r7xOXMZZnH7qLV94dQ/fbbkrVa4xkz734JH2ffIEsWbMku79gwfxs3LA58fOmTVsoWCg/W7duT9yWNGY5cOAg33w9iem/jefnab+zb+9+Kla8nEED30z1awk3L8QmqTlsZDJQ1MxWmNlQM6tzjvZtgLH4A4N2p+xbB8wAkq85PNk84EypyQP4g4RT/3K8LHBciphZVzObY2Zzdh3amtLDwub25nfRpnFH7rn1Qdp2akmlahWIiYkma/as3NrsDl55+k1eGv5MuLvpCdEx0ZQpfzGfjfqKWxt34fDhw3TscSsAQwe+w3WVWzHxiym07pR+fwkFQ3RUFOMeas2kvrezaN02Vm7eSf829Rg7YxHtXvmUg0ePERt98o+3lVt28dq3M3ny5nP9KJJzueCCzHzw8RAe6/0M+/efniQSiWCKTSJIdEw0l5a/mHHvf0GbRh04fOgwne+9/azHLF/8N+2vvZM7Wt5LkWKF2b5lB2bGi28P4Lk3+yabJJGTxcTGULvJ1Uz55qeTtg8ZOJxmlVsy8YvJtA3EKSsWr6TDdXdxV6v7KFKsENu3+u/3wGH9eebNPunifu/bf5CfZs5j4nsvMfWjwRw+cpRvf/wVgPs6tGLKB69ybd3qjP5m6hnPcfx4PNP++JPGNa9K3BafkMD+A4f4+NWneLBLGx56fgjOOS4pVYyPX32KEQMfY8PmbeTNlQPn4OHnh/DYoGHs3L031a85UjRpWo8d23eyYP7iM7ZJbiiNS1LinFzM8vrgd6hd43r6PP48j/fpyXPPDOa2Dq0Z+cHr9HrknuBfiARNqiUvnHMHgEpAV2A7MNbMOibX1syqANsDTxR+ACqa2ak/DZ8DHk5Bn8+VU3od6GBm2c54ArMhgTGuyQ7gds4Nd85Vds5VzpU5/zm+XPht3+p/sLRrx25+nPgz5a4sy9ZN2/lhwjQAFv25BJ/PR87cOcLXSY/Ytmk72zZvZ/GfSwD44dtpXFK+zEltvv9yCg2u1R/QwZAtU0YqX1SIX5etp0T+nAzr1pzRD97MNVeWpkju7Inttu45wIPvfc+AW+pTNE/2s5xRziUmJoZRHw/h07Hj+Xb85HB3J2J5oTTTixSbhM6u7bvIky83AHny5U52eOrWTdvYunk7fwV+Z0759icuvfziFH+NOx/oyNuvvke3Xl0YOuhdvvt8Erfe0To4F+BhV9evxrK/VpxxyPD3X06h/rV1T9ve5YEOvPPqKLr26sSwl0Yw4fPJtLvj5lTubfjNnL+YIgXykit7NmJjYmhwdSXmLz15/oVmdasz9dc5ZzzHjDkLubRUMXLn/H8Mkj9PLhrUqISZUb5MKaLM2L3v/5PaOud4Z8x47mp3A8M++Yp72t/ItfVq8PH4KcG/yAhVtVolmjZrwILF0xjx/mBq1anO2+++fFKbTZu2ULhIwcTPhQoVYMvmbcC5Y5byl5cFYNXK1bS9pQWdb7+PsmUvpmSpYql4VeHjhdgkVSfsdM4lOOemOef6AvcCLc/QtB1wSWCyq1VAtlPbOudWAvOBc/1WuhI44/gH59we4BMgaVptMVAxSZvuQAMg7zm+VsTLlDmOzBdkTnxfvU5VVi77hx+/n85VNSsDUKxkUWJjY9m9c08Ye+oNO7fvYuumbRQrVRSAq2pW4p8Vayha4v9DlOs0rsmalaeP45WU2XXgMPsOHwXgyLF4/lixgRL5crBrv3+8rs/neGfqXG6u4f+FtO/wUXq8M4H7mlXlyhIFz3heSZk3hj7PiuUrGfrmyHB3JaJ5IUDwKsUmoTFt8gyub90MgOtbN+OnSb+c1mbn9l1s3biV4qUuBKBqrcr8s2JNis5/fZtm/DL1N/bv3U9cpjicz4fP5yMuU1zQrsGrmrZoyKQvT64SSBqn1G5ckzUr1560v3nra5gx9ffE++3zucD9zhiSPodTgby5WbhsJYePHMU5xx/zl1CyaCHWbtyS2GbaH39SosiZY4yJP8/kmjrVTtpWv1pFZi3w/1hYs2ELx+MTyJkta+L+8VNnUKtKBbJlvYAjR49iFkVUlHHk6NEgX2HkerrfS5QrU5MrLqtLl44P8MvPv3PXHSdPETTxux9o284/mWzlKhXYt29/4pCRc8UsT/R5gOefGUxMbAzRUf4hxT6fj8yZMiXbPq3zQmySanNemFkZwOec+zuwqQKwNpl2UcDNwOXOuY2BbfWAJ4FTB9U9C3x3lq9ZHHiJc48HfQWYzf+v/0fgOTO72zn3VmBb5nOcI03IlScXg98bCPjLMyd+MZlff5pJTGwMT7/6BF9M+4jjx+J58r4BYe6pdwx6YjADhjxFbGwsG9dtov8Dz9Hn5d4UK3UhPp9j84YtPN/7pXB3M83ase8QfUb/iM/nw+ccja+4iNqXFefj6QsZ+6t/ic4G5Utyw1X+Cu2xMxaxbudehk+Zy/ApcwEYdtd15MrqiX/iIVWteiXa3nIjixctY/pv4wEY0O9lMmTMwAsv9SVPnlyM/fxd/lq4lFYtzjQ3oUj4KDZJHS+81Z/KNSqSI1cOpsz7mqGD3mXEGx/w0vBnufGW5mzZuJVedz4BQN78eej3ymN0v9X/B8jzT7zC80P7ERsby4a1G+nzwLMA1L+mDo89+yA5c+dgyEcvs2zRisQVTeIyZeT61s3o1sY/0ubDt0fzyojnOX7sOL3vfioMdyC0nhvaj0o1KpAjVw4mzv2CYS+NYN+e/TzyzAPkzJ2D1z8cxIrFf9O9XS/y5M/NUy8/yn3t/ctBxmXKSNXaVXj2kUEnnfO+J7pRrNSFOJ+PzRu28mzv/++Py5SR61pfQ/e2/vv/8dtjGPTuM8Qfj+exu/uF7LrD5fJLStGwZhXa3NeX6OgoLi1ZjFbX1KX3C8NYs3EzUWYUzJeHPvf6FxXatnM3/V4bydCn/d/jh48c5fc/F9GnR8eTzntj49o8Nfhdbrz7cWJjYnjmwTsTh0AcPnKU8T/MYNgz/v/fbruxKQ8++waxsdG8oGENdOriH8X33ojRTJ40jUZN6jJv4Y8cPnyY7t16A2eOWaZM/hnwT/Q5b95fbNnir9KYNetPfv3jOxYvWsaiRcvCcFWSEuZSadkDM6uE/xd1DiAeWAl0dc7tMLO6wEPOuesC7wc656olOTYa2ID/icPzwLfOuc8C+77AP0t48UBAsBRYBsQB+4G3nHPvBdp2BCo75+41s37AAefcS4F9rwA9nfNPXWJmBYFXgar4S0kPAsOcc2PPdp2XF6iudSNCJIOl5vyycqpf3j3Tw0hJDYXaeH+iqEiz+8DKVJu66o2i7YP2u6HH+o88MMVWZFBsIqkhxjQJdCj9PkMPgEKpwBXtw92FdEWxydmlWvIivVCAEDpKXoSWkhehpeRF6KVmgPDahcELEO5fp+SFnB/FJqGl5EVoKXkRWkpehJZik7NL1TkvRERERERERET+Kz3KFhERCTJNtCkiIiKRxAuxiZIXIiIiQeaFAEFERES8wwuxiYaNiIiIiIiIiEhEU/JCREQkyFwQXyIiIiL/VahjEzNrambLzWylmT2azP4bzGyhmc03szlmVvNc59SwERERkSDzaX0QERERiSChjE0Cy4sPARrhX2Z8tpmNd84tSdLsB2C8c86Z2eXAOOCSs51XlRciIiIiIiIiEixXASudc/84544BY4AbkjZwzh1wzp0o5LiAFBR1qPJCREQkyLwwKZaIiIh4RzBjEzPrCnRNsmm4c254ks+FgfVJPm8AqiZznhuB54F8wLXn+rpKXoiIiASZ5qoQERGRSBLM2CSQqBh+libJDVI5rQvOuS+BL82sNjAAaHi2r6thIyIiIiIiIiISLBuAokk+FwE2namxc246UMrM8pztpKq8EBERCTKfai9EREQkgoQ4NpkNlDazEsBGoC1wS9IGZnYRsCowYWdFIAOw82wnVfJCREQkyDTnhYiIiESSUMYmzrl4M7sXmAREAyOdc4vNrFtg/zCgJXC7mR0HDgNtkkzgmSwlL0REREREREQkaJxzE4AJp2wbluT9C8AL53NOJS9ERESCTINGREREJJJ4ITZR8kJERCTINGxEREREIokXYhOtNiIiIiIiIiIiEU2VFyIiIkHmS251cxEREZEw8UJsouSFiIhIkGmpVBEREYkkXohNNGxERERERERERCKaKi9ERESCLO0/2xAREREv8UJsouSFiIhIkHlhRm8RERHxDi/EJho2IiIiIiIiIiIRTZUXIiIiQeaFSbFERETEO7wQmyh5ISIiEmRpPzwQERERL/FCbKJhIyIiIiIiIiIS0VR5ISIiEmRemBRLREREvMMLsYmSFyIiIkHmhXGlIiIi4h1eiE00bEREREREREREIpoqL0RERIIs7T/bEBERES/xQmyi5IWIiEiQeWFcqYiIiHiHF2ITDRsRERERERERkYimygsREZEgc54ozhQRERGv8EJsouSFiIhIkHmhNFNERES8wwuxiYaNiIiIiIiIiEhEU+WFiIhIkHlhLXURERHxDi/EJkpeiIiIBFnaDw9ERETES7wQm2jYiIiIiIiIiIhENFVeiIiIBJkXSjNFRETEO7wQmyh5ISIiEmRemNFbREREvMMLsYmGjYiIiIiIiIhIRFPlhYiISJA5D5RmioiIiHd4ITZR8kJERCTIvFCaKSIiIt7hhdhEyYv/aOfRfeHuQrqx7eCecHchXcl646BwdyFdObzpl3B3QUQ8QrFJaCk+Ca0LLm0Z7i6kK4pPJJIoeSEiIhJkXijNFBEREe/wQmyi5IWIiEiQeaE0U0RERLzDC7GJkhciIiJB5nNp/+mGiIiIeIcXYhMtlSoiIiIiIiIiEU2VFyIiIkGW9p9tiIiIiJd4ITZR5YWIiEiQ+XBBe4mIiIj8V6GOTcysqZktN7OVZvZoMvtvNbOFgddvZnbFuc6p5IWIiIiIiIiIBIWZRQNDgGuAskA7Myt7SrPVQB3n3OXAAGD4uc6rYSMiIiJB5oXlyERERMQ7QhybXAWsdM79A2BmY4AbgCWJ/XHutyTtZwJFznVSJS9ERESCzAvLkYmIiIh3BDM2MbOuQNckm4Y755JWThQG1if5vAGoepZTdgEmnuvratiIiIhIGmZmI81sm5ktSrItl5lNMbO/A//NmWTfY4Hxp8vNrEl4ei0iIiJplXNuuHOucpLXqUM+LLnDkjuXmdXDn7zofa6vq+SFiIhIkIV4Uqz3gaanbHsU+ME5Vxr4IfCZwHjTtsBlgWOGBsalioiIiIeFODbZABRN8rkIsOnURmZ2OfAucINzbue5TqrkhYiISJC5IP7vnF/LuenArlM23wCMCrwfBbRIsn2Mc+6oc241sBL/uFQRERHxsFDGJsBsoLSZlTCzDPgfnIxP2sDMLgS+AG5zzq1IyUmVvBAREYlgZtbVzOYkeXU991Hkd85tBgj8N19ge3JjUAsHt8ciIiKSnjnn4oF7gUnAUmCcc26xmXUzs26BZk8BufFXgc43sznnOq8m7BQREQmyYE6KFRhHes7lw1IoxWNQRURExDtCPZm4c24CMOGUbcOSvL8DuON8zqnkhYiISJA5F/Z8wFYzK+ic22xmBYFtge0pGoMqIiIi3hIBscl/pmEjIiIi3jMe6BB43wH4Osn2tmaW0cxKAKWBWWHon4iIiMh5UeWFiIhIkKVwJu6gMLPRQF0gj5ltAPoCA4FxZtYFWAfcDBAYbzoOWALEA92dcwkh66yIiIiERShjk9Si5IWIiEiQhXJcqXOu3Rl2NThD+2eBZ1OvRyIiIhJpQj3nRWrQsBERERERERERiWiqvBAREQmyFK6BLiIiIhISXohNlLwQEREJMi+MKxURERHv8EJsomEjIiIiIiIiIhLRVHkhIiISZF5YS11ERES8wwuxiZIXIiIiQeaFGb1FRETEO7wQm2jYiIiIiIiIiIhENFVeiIiIBJkXZvQWERER7/BCbKLkhYiISJB5YUZvERER8Q4vxCYaNiIiIiIiIiIiEU2VFyIiIkHmhRm9RURExDu8EJsoeSEiIhJkXijNFBEREe/wQmyiYSMiIiIiIiIiEtFUeSEiIhJkXpjRW0RERLzDC7GJkhciIiJB5vPAuFIRERHxDi/EJho2IiIiIiIiIiIRTZUXIiIiQZb2n22IiIiIl3ghNlHyQkREJMi8MKO3iIiIeIcXYhMNGxERERERERGRiKbKCxERkSDzwtMNERER8Q4vxCZKXoiIiASZ88CM3iIiIuIdXohNNGxERERERERERCKaKi9ERESCzAulmSIiIuIdXohNlLwQEREJMueBAEFERES8wwuxiYaNeFzGjBn4duoYpvzyBT/+9jW9Hu0OwMOP92DKjC+YPP1zPvl8OPkL5A1zT73p7xUz+XPeVObMnszM3yeEuzueVqRIIaZO/pS/Fk5jwfwf6XFvl3B3yTMSEhJo1bE79zzcF4AhIz6i/g3tadmhOy07dGf6b7MS2y5fuZpbu/bkhlvv4sbb7ubo0WPh6raIhEm2bFkZ/v6r/PzHN0ybOZ5KVa44af+NN1/LlBlfMGXGF3w96SPKliuTuG/mgslM/fVLJk//nAk/jk3c/ni/B5ky4wtee+u5xG0t2zSny13tU/+CIlzjxnVZtGg6S5fM4OGHu5+2P0eO7Hz66bvMmzuF3379lssu89/vjBkz8tuv3zJ3zhTmz/+Rp57qlXjMc889zry5U3hv5GuJ2269taV+twJNGtdl8aLpLFsyg0eSud8nVK50BUcPr+Omm64F/Pf798D9XjD/R/omud/P636f5IMxX3LDrXfRon03Hu47kKNHj9Grz/OJcUfjlh1o2SH5e//kc69Q+9q2tGjf7aTty/7+h1u79uTG2+6m+yN9OXDwIADzFi7mxtvvpk2X+1i3YRMA+/YfoGvPJzwxR4TXhDR5YWavmtkDST5PMrN3k3x+2cweNLMYM9thZs+fcvw0M6t8yra6ZvZtks/PBM6bMWl7M1tjZp8nadfKzN5P8rmpmc0ys2VmNt/MxprZhcG8/nA4evQYrW/oTKNaN9G4dkvqNqhJxcqX89YbI2lU079t6qSf6fnI3eHuqmc1bHQzlas0plr1ZuHuiqfFx8fz8CP9KX95Xa6u2Zy77+7IpZeWDne3POGjT7+mZPGTfxze1qYFn48awuejhlC7xlUAxMcn8OjTL9Ln4R58/fHbvPfmC8TERIejy2HnnAvaS1KXYpPge3rgY/z0wwzqVG1Oo1ot+Xv5PyftX792I62u7UijmjcxeNAwXni130n7b27eica1W9KsfhsAsmbLQuWrKtCo5k1ERUdzSdnSxMVlpHW7FowaMSZUlxWRoqKieP21Z2nevD2XX1GPtm1anPa779HePViwYDEVKzWiU+f7eeXlpwE4evQojRq3plLlRlSu3JgmjetS9aqKZMuWlerVKlOxUiOio6MoV+4S4uLiuP221rw1bFQ4LjNinLjf1zVvT/kr6tEmmft9ot3zzz3B5MnTErcdPXqUhoH7XSkF97tDOr3fW7fv4OPPvmbsyNf56qNh+Hw+Jk79mZcHPJYYdzSqW5OGdWoke3yLZo0Y9sozp23vO3AwD9zdiS8/fIsGtWvw3sf+H72jRn/B4Gef5P67OjL2y+8AePv90dx5exvMLPUuNAy8EJuEuvLiN6AGgJlFAXmAy5LsrwH8CjQGlgOt7Ty+a8zsCeBqoIVz7mgyTSqb2WWnbjSzcsAbQAfn3CXOuQrAx0DxlH7tSHbo4CEAYmJjiI2NwTnHgf0HE/dnviCTAmRJ87Zs2caf8xcBcODAQZYt+5vChQqEuVdp35Zt25n+2yxaNm9yzra/zZrLxaVKcEnpkgDkyJ6N6Oj0mbzw4YL2klSn2CSIsmS9gKo1KjH6Q/8fBsePH2ffvv0ntZkzaz579+4DYN7shRQslP+s5/T5fMTGxgIQF5eR48fj6dajMyPe/oj4+PhUuIq046oqV7Jq1RpWr17H8ePHGTvua5qf8vP60ksv5qcfZwCwfPkqihUrQr58eQA4GIgRY2NjiI2NxTmHz+cjQ4bA/c4Ux/Hjx+nVqxtvDhmh+33K/R437muuT+b3473dO/PFl9+xbfvOk7Ynvd8xydzvTIH7/VCvbryRju93fEICR48eIz4+gcNHjpI3T67Efc45vv9xOs0a1U322MoVypM9W9bTtq9Zt4HKFcoDUL1KRab87P83ERMTw5Gjxzhy9CgxMdGs27CJrdt3UOXKy4N/YWHmhdgk1MmLXwkECPgDg0XAfjPLaWYZgUuBP4F2wGvAOqBaSk5sZr2AZkBz59zhMzR7CXg8me29geecc0tPbHDOjXfOTU/J1450UVFRTJ7+OQtX/ML0ab/z59y/AOj95H3MXjSVG2++jkHPvRnmXnqTc46JE0bzx8yJ3NHl1nB3J90oVqwIFa4oxx+z/gx3V9K8F157mwfv6YL/b7r/G/35N9x4+908+dwr7A38YbJ2/UbMjK49n+DmTvcy8uNPw9FlkfOl2CSIihUrys4du3l1yLNM+vkzBr3Wn0yZM52xfdvbbuKnqb8kfnbOMfqLd5j40zhu7XAzAAcPHGLCN1OYPP1z1q/dyP59+6lQsRyTJ/6U6tcT6QoVLsCGQKk7wMaNm09L3C/8awktWvirP6tUrkCxYkUoUrgg4I8R58yezKaNC5n6w3Rmzf6TAwcO8sWXE5gzezJrVq9n7979VK5cgW++mRy6C4tQhQoXYH2S+71h42YKnXK/CxUqQIsbmvL28A9PO/7E/d68cSE/6H4nK3/ePHRs15KGN91OvRtuIesFmbm6aqXE/XMXLCJ3zpwUK1r4vM57Ucni/DRjJgCTf/qFLVt3AHDnba3p/8JrfDj2K9q1bM7rw0fR487bg3dBElQhTV445zYB8YGSxxrA78AfQHWgMrAQiAYaAN8Co/EHC+dyNdANuMY5d+As7cYBFc3solO2XwbMO49LSVN8Ph+Na7ek8mX1ubJiecpc6r/8F555nSrlGvLlp9/S6c5bwtxLb6pTtwVXVW3Kdc3bc/fdHalZs2q4u+R5F1yQmXFj3+HBh/qyf//ZfhzIuUz79Q9y5czBZZecXBLb5sZrmThuJJ+/P4S8uXMx6M13AP+Tkj8XLuaFvo/wwVsv8cPPvzFzTvpMIHmhNDO9UGwSXNEx0ZS/4lI+GDmGJnVacejQYe594I5k29aoeRXt2t/Ec/1eSdzWoml7mta9mfY3d6PjHe2oWsP/R8tbr4+kce2WPN1nEA8/3oNBz71Bu9taMmzky9zf666QXFskSq4I6NSfGy+++CY5c2ZnzuzJdO/emfnzFxGfkAD4Y8TKVRpTvERlqlS+MnE+jJdffovKVRrzSO+n6d/vYfr3H0TnTu345JNhPPbY/al/YREqJff7lZf789jjz+Hz+U5re+J+Fzvlfr8UuN8PB+53v8D9Hv3JMB5PZ/d77779/PTLTCZ9+h4/fv0xh48c5ZtJPybunzBlGs0a1Tnv8w54vCejP/+G1p17cPDQYWJj/etWXHJxKT55ZzDvvfkCGzZtIV+e3Djn6NXneXr3f5Edu3YH7drCzQuxSTgm7DzxhONEgPB7ks+/AdcBPznnDgGfAzea2bnqjlcChr+k82wSgEHAY2dqYGa5A+NKV5jZQ2do09XM5pjZnINH08439L59+/ltxizqNqh50vYvP/uOZtc3ClOvvG3z5q0AbN++k6++nkiVKhXC2yGPi4mJ4dOx7zB69Jd89dXEcHcnzftz4RKmzZhJ45YdeLjvQGbNXUDv/i+SJ1dOoqOjiYqKotX117BoyQoA8ufLQ+UK5cmZIzuZ4uKoVb0KS5avCvNVhIcXSjPTGcUmQbJ501Y2b9qaWOX53fjJlL/i0tPaXXrZxQx6vT+db+3B7t17E7dv3bIdgJ07djHx26lUqFj+pOMuK38JAP+sWkurttfTrXMvypS9iBIlI34qkFSxccNmihQplPi5cOGCbArEHifs33+AO+58kMpVGtOx033kyZOb1avXndRm7959/Dz9Nxo3rnvS9goV/COaVqz4h/btW3HLLd247LIyXHRRidS5oAi3ccNmiia530UKF0yM9U6oVPFyPv5oKCtXzKTlTdfy5uvPcf31Jw8tOXG/m5zlft/WvhXt0uH9njlnPoUL5SdXzhzExsTQoE4N5v+1BPDPrTX1599o2qD2eZ+3ZLGivDP4OcaNfINmDetQNFB9dIJzjrffH81dHdvx1siP6X5He5o3qc/Hn34dlOuKBF6ITcKRvDgxtrQ8/tLMmfifbpwYU9oOaGhma4C5QG6g3jnOuRV/WearZnauth8CtYGkv+UWAxUBnHM7A+NKhwNZkjuBc264c66yc67yBRlznuPLhVeu3DnJFhj3FReXkVp1q7Pq79Un/ZJv3LQeq1asDlcXPStz5kxkyXJB4vtGDeuwePHyMPfK294Z/jJLl61k8GvDw90VT+h5dyd++OojJn8+ikH9H+WqSlfwQt9H2L5jV2KbH37+jYtKFgPg6qsqsWLVag4fOUJ8fAJz5v9FqRLp8w8KSXMUmwTJ9m072LRxC6UuKg5AzdrVWHFKErNQkYK888Fr3N/tMf5ZtTZxe6bMmbggS+bE93Xq12D50pUnHfvIEz146fk3iY2JSZxTx+dzZx2a4mWz58znootKULx4UWJjY2nT+ga+/fbk4QbZs2dLnDOkS+dbmDHjD/bvP0CePLnInj0bAHFxcTSoX4vlp/x/1a/vI/Tr/xKxsbFJ7rePzLrfxMbG0rr1DXxzyv0uXaY6F11cjYsursbnX3zHvfc9zvjxk1J0v/vrflMwf14WLlrG4SNHcM7xx5z5lCxWFICZc/6kZLEiFMh3/qsk7ty9B/Dfz7dHjaF1i5Mn0v96wlRq17iK7NmycvjoUaLMMDOOHEluqiIJl5gwfM1fgV7AP865BGCXmeXAXx55P1ATKHpiUisz64Q/aJh6tpM651aY2U3AV2Z2rXNu/hnaHTezV4FHgRM1SC8CX5rZzCRjSzP/h2uMGPkL5GXw0OeIio4iKiqKb76cxNRJPzN81GBKlS6Oz+dj4/rNPPpg/3B31XPy58/LZ5+OAPxltGPGfHXSrNMSXFfXqMJt7Vux8K8lzJntDyT69BnIxO9/PMeRcr5eHjqC5X//AwaFC+Sn7yP3AZA9W1Zub3sTbbvcj5lRq3oV6gRWIklvvLCWejqj2CSI+jzyHG8Mf4HYDLGsW7OBB7s/yW2dWgPw4Xvj6PlwN3Lmys5zL/UB/KtFNavfhrx5czPio9cBiI6O5qvPv2PaDzMSz9ukWX3mz1uUWJ0xd9Z8pv76JUsXr2DJovT5cCAhIYH7H3iS7777hOioKN4fNZYlS1bQ9c7bABj+zodceklpRo58jQRfAkuXrqBrV3/xTsGC+Rk5YjDR0VFYVBSfffYNEyb8/1v6+uubMGfu/MTKgpkz5/LnvKn89ddSFi5cEvqLjQAn7veEs9zvM0l6v6MC9/s73e/TXH7ZJTSqV5PWnXoQHR3NJReX4uYbrgFg4tSfuaZh3ZPab9u+k74DB/PWywMAeLjvQGb/uZA9e/bRoEV77ulyGy2bN2HClGmM+cK/CFTDOjW48dr/F8UdPnKErydOZfjgZwHo0OYmej7xLLGxMbzYr3cIrjo0Qh2bmFlT/HNFRQPvOucGnrL/EuA9/In6J5xzL53znKEesxIos9wNvO6cezKw7X38TzieB5o659omaZ8L/+zeRYBJ+CfOOh7Y/TswBHjIOXddoH1j4F38T0RGBPbNCTwtqeyc2xGYgGs1MNk51zFw3LVAPyArsBP/hFx9nXMrznY9hXNepgg1RLYd3BPuLqQr+sYOrcObfjl3Iwmq2DwlU20NtHL5qwXtn9CirTO9tVZbBFJsIv+F4pPQ0jd3aCk+CS2vxCaB36srgEbABmA20M45tyRJm3xAMaAFsDsikxdeowAhdBQchJa+sUNLwUHoeSVAEDmVYpPQUnwSWvrmDi3FJ6HlldjEzKoD/ZxzTQKfHwNwzj2fTNt+wIGUJC/CMWxERETE0zRsRERERCJJMGMTM+sKdE2yabhzLumkc4WB9Uk+bwD+87KLSl6IiIgEmU9VjSIiIhJBghmbBBIVZ5shP7nKjP/cgXCsNiIiIiIiIiIi3rQBKJrkcxFg0389qSovREREgkzDRkRERCSShDg2mQ2UNrMSwEagLXDLfz2pkhciIiJBpmEjIiIiEklCGZs45+LN7F78K3JFAyOdc4vNrFtg/zAzKwDMAbIBPjN7ACjrnNt3pvMqeSEiIhJkqrwQERGRSBLq2MQ5NwGYcMq2YUneb8E/nCTFNOeFiIiIiIiIiEQ0VV6IiIgEmYaNiIiISCTxQmyi5IWIiEiQadiIiIiIRBIvxCYaNiIiIiIiIiIiEU2VFyIiIkHmnC/cXRARERFJ5IXYRMkLERGRIPN5oDRTREREvMMLsYmGjYiIiIiIiIhIRFPlhYiISJA5D8zoLSIiIt7hhdhEyQsREZEg80JppoiIiHiHF2ITDRsRERERERERkYimygsREZEg80JppoiIiHiHF2ITJS9ERESCzOeBAEFERES8wwuxiYaNiIiIiIiIiEhEU+WFiIhIkDkPTIolIiIi3uGF2ETJCxERkSDzwrhSERER8Q4vxCYaNiIiIiIiIiIiEU2VFyIiIkHmhbXURURExDu8EJsoeSEiIhJkXijNFBEREe/wQmyiYSMiIiIiIiIiEtFUeSEiIhJkXlhLXURERLzDC7GJkhciIiJB5oXSTBEREfEOL8QmGjYiIiIiIiIiIhFNlRciIiJB5oUZvUVERMQ7vBCbKHkhIiISZF4ozRQRERHv8EJsomEjIiIiIiIiIhLRVHkhIiISZF6Y0VtERES8wwuxiZIXIiIiQeY8MK5UREREvMMLsYmGjYiIiIiIiIhIRFPlhYiISJB5oTRTREREvMMLsYmSFyIiIkHmhRm9RURExDu8EJto2IiIiIiIiIiIRDRVXoiIiASZFybFEhEREe/wQmyi5IWIiEiQeaE0U0RERLzDC7GJho2IiIiIiIiISERT5YWIiEiQeeHphoiIiHiHF2ITJS9ERESCLO2HByIiIuIlXohNzAsZGDl/ZtbVOTc83P1IL3S/Q0v3O7R0v0UkGPSzJLR0v0NL9zu0dL+9SXNepF9dw92BdEb3O7R0v0NL91tEgkE/S0JL9zu0dL9DS/fbg5S8EBEREREREZGIpuSFiIiIiIiIiEQ0JS/SL40BCy3d79DS/Q4t3W8RCQb9LAkt3e/Q0v0OLd1vD9KEnSIiIiIiIiIS0VR5ISIiIiIiIiIRTckLEREREREREYloSl6IBIGZWbj7ICIiIpKU4hMR8RIlLzzIzLKGuw/phZkVMrNYp8ljQsLMcun7O3TMrJSZFTSzuHD3RUTSNv3sDi3FJ6Gl+CR0FJukb0peeIyZNQZeMbN24e6L15nZNcBI4CEzyxzu/nidmTXBP3P0XWaWJ9z98Tozawq8D7QCLgxvb0QkLVNsElqKT0JL8UnoKDaRmHB3QILHzJoBzwJPAAfC3B1PM7PrgGeA7sA259yhwHbTU47gC9zvfsDjwALn3I4k+3TPg8zMrgVeALoCi5xz+wLbMznnDoe1cyKSpig2CS3FJ6Gl+CR0FJsIaKlUzzCzy4DPgI7OuT+SbL8BmOqcOxi2znmMmRUCPgcedM79nmR7d2ApMMM5dyxc/fMaMyuJ/3v7Xufcb0m29wRmOed+VYAQPGaWBfgUGOSc+zHJ9p5ARuB959yWcPVPRNIOxSahpfgktBSfhI5iEzlBw0a8Izcwxzn3h5lFA5jZIOAL4PXAP3oJjmhgP/BXknvdD3ga/5OOumamf1vBcxyY55z7zcxiAczsaeAO4CMzu1aBQVDFATmAtSc2mNkAoAeQD7jHzLKHp2siksYoNgktxSehpfgkdBSbCKDkRZpnZhkDb6OB7ADOuQQzKwVsAbLiDx56h6eH3mFmeQK/nA4B8ZB4r3MAfzrncgPfALcCGmP6H50IvIC8QG0zu8g5dzwQ7O5zzl0GdAO6mdkFYeuoRwQmwLJAyetioHiS3V8450oCo/D//6HvbxE5I8UmoaX4JLQUn4SOYhM5lZIXaZiZXQk8YGa5gT+AS8zscQDn3CpgSGCs41dAxhNZYTl/gdLAvkAj59xOYDfwHYBzbs+J9/jH88YAWprsXzK/S4G/zSyHc24e/qd095hZMefcAWBwoHluYAeBYE3+HTPLBTyMf1w6wEr897swgHPuz8D2S4CigMqORSRZik1CS/FJ6Cg+CS3FJpIcJS/StgSgIf7ytASgPVDfzJ4CcM4dMbNb8Gd/Rzrnjoetp2nfZmAf0NTMauK/1zvNbLqZ5Qdymll7/E+RnnHO7Q9jX9OsE2NDnXNLgenA74EnGSOBWPwzp5cFfIH73QN40Tl3NHy99oRDwJf4v497OecGAjvxl3XfYGaXmVkX4FHgoUCALCKSHMUmoaX4JAQUn4SFYhM5jSbsTIPMLAPgc87Fm9nl+Mcy/g58jH/c18fAavz/6MsA7Zxzi8LV37Qs8IvfOee2mVkm/L+MSgGf4P/lNQJ/dj0DkAm4zzm3MFz9TevMLM8pM3UPA+oCFYFCQCegNfAb/kz7Hc65v8LQVU8IPEE6FngaipnVAjoAS5xzr5jZA/jv80X4x1E/pfstIslRbBJaik9CS/FJ6Cg2kbNR8iKNMbP6wKvAL8DbwN/4x5O+DfwEvAM44LLAIZucc5vC0NU0z8wq4i95XYp/aaYNzrmfzewJ/OPqpjjnpplZVvwTCR1zzu0NX4/TNjOrhP+X/if4v69fCTyh648/IKjsnDsYeLKxBYhKGkjI+TGzMsB8YDswBH9J8Xv4g7HawE7n3KBA26z4v7/1BElETqPYJLQUn4SW4pPQUWwi5xIT7g7IvxKH/4dltsDrB2AScF1g/yjn3Jww9c0znHPzzGws/vtaHmgeKE9bjf/pRq7ApE0/qQzzvzEzAw7jf1qUE//3dyEzK42/1PUaYJKZNXPOLQlfT73BzGKcc8vN7G3gNmBd4L8VgPzATKBJoEz2RX1/i0gKKDYJEcUnoaP4JHQUm0hKqPIijTCz6kAZ59z7ZlYHuAXYhT8r2Qv/uNJO+H+wPgP0c875wtXftMzMrgIedc7dFPjl/wr+YOBGoB5QDn/5WnngZ6CZc+5wuPqb1gWy7LcAA/E/lWuBf0zjz/hLiyvhv/83ALOA6lp67N8zs3LAffi/x3eZ2Tv4S4s74F8BoDVQFWgKrAfqOOd2h6u/IhK5FJuEluKT0FJ8EjqKTSSllLxIAwKz6hYFPsI/rusTM6sL9AS+ds6NDLRrhv8X17fK/v47ZnYFsAT4C//a9O0DWfcP8Y8ZbRtYDqs4kAfY65z7O2wdTsNOTH4VKDduA2wAXgSqADcHPr/tnNtn/uXergd+1/3+9wKB2HpgNP610p9yzu0xs4/wj0m/yTl3wPzLHOYFYpxza8LWYRGJWIpNQkvxSegoPgktxSZyPpS8iHBmVggYhD/zWD/wfpBz7qNAkHA/8Ktz7qWwddIjzKwx/rWiKznnNpnZn8By51zbQIDwPpALaOmc03JMQWRmVfHPkL4D/xOOivjXo/8HGO+cWxnG7nmCmRUBPgNq4g9038H/hPQx59xeMxsFFARu1thoETkbxSahpfgkfBSfpC7FJnK+tFRq5MuEf6xXUefcZPxlmA+bWXvn3DT8E2Q1NbMe4eti2mdmTfBPLNb+xCRizrkrgYvNbKzz6wAcxf+USf4DM6thZveaWX8zu9059wf+5cby4l/yah7++1wOuMbMYgIBmvw3BmQPjBO9A3+w+7yZZQ98fx8A3te9FpFzUGwSIopPQkvxSVgoNpEUU+VFGmBmr+DP7k4LfG6Af5zjC4EyzZrAGufchjB2M80KBAbD8Zdivg384Jw7lGT/PGCpc+7WwOdCmiX93zOzpsBr+O/1RUBJ/CWADc3sSqAzsBX/k7zLgXXOua3h6m9aFyg13oH/nv6Af1w0zjmf+deoHwFsw1+muVvf3yKSEopNUp/ik9BSfBI6ik3k31LyIgIFSi7b4P9H+wEwAFjlnOuTpE19/GWCvZxzn4a+l95gZi3wTyx2K/5Mb11gLjDWOXckSbvV+Gft7hyGbnqGmTUH+gDdnXOzA9uigB+Bo865JmZWDX/m/W/n3Avh623aZ2ZxQF/866H3BZ4AOpzyvR2Hv2RzGfCwJhsTkeQoNgktxSehpfgkdBSbyH+h5EUEMrMLgSb4ZzbOBFQHNgIPOOeWJ2lXB1jvnPsnLB1N4wJPNF7AP7Px92aWAf/43XL4ywJPDRBKOOdWh6e3aVug1C8GmIL/506dwPYMzrljZhYLjAeGO+e+NLPa+Mfz6onGf2RmJfAvNXY1UBZ4A9gDrAQy4y/HXAcc0VMNETkTxSaho/gkdBSfhIdiE/m3lLyIIIGnGhmdc5OSbMsIFAfuxb8802fOuUXh6J+XBAKDofgnt5pvZhmdc0fNLAboiH+ZsTnAp0kDBPl3zKy4c26NmRXAXwK71Tl3Z2BfBuA4/jGkc5xzr4axq54UCBLuAe4CfgOWA9mBIkAG4HbN3C0iyVFsElqKT0JL8Un4KDaRf0PJiwhhZtmB74AS+Mfb+ZxzL5klLtd0EfAQ/kzkCOfc0jB2N00LzNo9EliK/6nG3MD2aOdcQiBAuB1/NniKc25M+Hqb9gXGLs4FPnTOPWNm+fGXFa93znVN0m4A/lLMD05834enx2mbmV0KHHDOrT9l+4X4Z0wvB9zj/MuQReP/WaN7LSKnUWwSWopPQkvxSegoNpFg0WojEcL5l//5FjgGrMA/S/dI4EYzy+f8SzENAqLxT3Aj/4KZNQQG41+H/j2gW2CCJgKBQbRzLh7/eN5pwE9h6qpnOOcOAO2ANmb2cKDUsiNQ1MyGA5hZK+AmYEbgGP3C+hcC5a99gP7mX34sqfXAJ8BqYHRg8qsE3WsRORPFJqGj+CT0FJ+EhmITCSZVXkSAJE8wMuD/xfW5c+4HM/sEqAocBJ7DH0Acds4lhK+3aVfgB+ZlwH7n3G9mVhy4Dv+M0V84574PtIsJBAjyH5hZefwTLUUFSl6vxF96+ZFz7vnAE4538ZceHwDucM4tDluHPSIwydXb+O/pwGSeclQCqgFfOec2hqGLIpIGKDYJHcUnoaX4JPQUm0iwqPIiTAJZSOCkLK7Dv2TQlWZWEv8/4s74n2pcC8QpOPh3zCwX/lLMGsDiQFC2Bv8kTAuAmwLlmigw+O/M7GL8TynmAO+a2TXAWqAp/icc9weecHTFn23vqsDg3zOzPIEySwJjoO8CcgCPmlmxJO06AA8CoxQciMipFJuEnuKT0FJ8EjqKTSQ1qPIiTE5kz0/NoptZYeBPIAvQ2jn3bWB7ZpdkbW9JOTO7DrgC+Bl4GFgIvO6c2x7YfyH+AKw28K5z7odw9dUrAsFYf6AY/qcbpYF4/DPTrwMew79291snxvKGrbNpnJlVBz4GfgEexz+mdK+ZZcK/Tvpe/JPqdcJftnmdc+6vcPVXRCKXYpPQUnwSeopPQkOxiaQWJS/CwMzy4M/4VnTO7UoSLJyYkKkrcLlz7l4LLNUU5i6nWYHA4FlggHPuM/v/bNJ/AkMD2XUCJZoNge+cc5vD1V8vMbO8+AOElfiXdluAfywvwKP4A4Urgb0a2/jvBCYby4B/Qr044B/gb2CGc+5b868IMBT/RFgFgObOuYXh6q+IRC7FJqGl+CR8FJ+kLsUmkpqUvAgTM2uOv+Syuvtfe3cetHs5x3H8/TntkqZIkymjCS1CmSIiB01STFNTEsYYLQoly8RYQ5YIoymVohpNadJiStspS6VVR2mbtMk20Yo4wvH1x3U9HEd1nnNOz/27n+e8X/+ce67nd/9+133Pmbk/cy3fq+rBtArS8/v+0s2BbwM7VdUdg3Z0GutB4BTgoKq6ZmKGKMlWtMBwDnBKVd3Tr3cv6VJI8hJg86o6eoG2dekj7sCxVXVbb98K+O3Cex41eUk2AN5LOxt9Z1o4uAVYD/gucCRwNnAF8FHasXqeBCDpMZlNRsN8Mlrmk9Exm2iqLT90B5ZVVXV2kn8CP02yRQ8JK9DOk/4Hraq3NUmWziO07/JvvVDQQWnn1d9P28O7C7BWkq9U1X0GgyXXv9/DgS2TbEFbJnhFVf0i7YixjwB7JTmjqq6qqiuH7O8MsRywEW12aA5wJrAhLYjNo812fAa4u6rePlAfJU0jZpORMZ+MiPlk5MwmmlKuvBhYLxR0BDAREvYHDgBmW7Rm6fTCY+8HtqNV8b6IVqTpFtpo8N20wmMHTuwv1eJLsl5V/TrJNsB+wK20H68dgMOAC2hV6Y+kHYn1hap6ZKj+TndJ1gce6HtHt6PN3m0NvJJWnf6FtLPSz+mzpuv24m+SNClmk6llPhkN88nomE00Kg5ejIEeEg4FTgD2BvaoquuG7NNM0ffdPZ+2XO17Ez9KSU4EvlNV5w3Zv+kuyY60WYvdaLNxHwaOr6prkxxKq9Y9hxbIjqIVsP/9UP2d7nqV9FNpAezAqronyd60gHAZ8G7aMuQ5SVYyhElaUmaTqWU+mVrmk9Exm2iU3DYyBqrqvCSzaHvANq+q64fu00xRVQ/T9tVdMdGWZDdakaDbhurXTJDktbS90XtU1e96273Ax5N8HXgD7QfrKtoxcEcZDJbancBNtJmMQ5OcSqvYfSfwh/7vrsAcw4GkpWE2mVrmk6ljPhk5s4lGxpUXY8Qjx6ZWknWA3WkzSLtX1Y0Dd2naSrI9cBxwH7BLVd3Z20NbKrgD8LaqOmuwTs4g/bi8lfse3acB+9Nmkn5DO+ZtNu2UgBNo/8cPrqo/DtNbSTOJ2WTqmU+eOOaT0TGbaAiuvBgjhoMp9xBtNmOnqrp94L5MW72o2CG0/aOrA4cnOaQXuZoFXA4wEQz6zF153NiSSbIq7Qz0FZOcWVVnJbkT+BtwIfAMYGPaEtg1gX0NB5KeKGaTkXgI88lSM5+MjtlEQ3HlhaRJ60WYDgYOqapzk6wNvJFWdOyzVXVlktVo+x4/WlXHD9fbmaMfq/ca4Iu0YHY7sC/wub5/d23gncBJE7NMkiQtK8wno2c20RAcvJA0KUleT/tx+lhVnbNA+1OBtwLbAp+vqsuT7AzMraq7h+ntzJTkRbQCenNohbBWB/asqjuSzKqqfw3aQUmSRsx8MiyziUbJwQtJi9RH108BPlRVVydZEViR9gN1LzCfVvxqV2B/C7tNnSTrAtvTzlDfD/gA8DVc+ipJWsaYT8aD2USjYs0LSZPxCPAPYF6SlWlHjr2cFhBuB94HnEjb6/jAUJ1cFlTVb/pReqcCKwDnOqshSVpGmU/GgNlEo+LKC0mL1Kt0v5+2d/R5wEW0s7tvAN4FnFZV5yRZrqrmD9dTSZK0rDCfSMsWV15IWqSqqiTH0Cp1rwd8b+Ks7iT7AGv06wwGkiRpJMwn0rLFlReSlliS3YAP0c6lv2Po/kiSJJlPpJnJlReSFluSdYDdgb0xGEiSpDFgPpFmNldeSFpsSVYBXg3cWlW3D90fSZIk84k0szl4IUmSJEmSxtqsoTsgSZIkSZL0eBy8kCRJkiRJY83BC0mSJEmSNNYcvJAkSZIkSWPNwQtpjCSZn+S6JDcmOS3Jk5biXick2bW/Pi7JJo9z7ewkL1uCZ/wyydMm277QNQ8v5rMOTvLBxe2jJElacmaTx73ebCKNkIMX0niZV1WbVdWmwN+BfRf8Y5LlluSmVbVXVd38OJfMBhY7IEiSpBnPbCJpLDh4IY2vS4Fn95mHHyY5GbghyXJJvpTkmiQ/T/JOgDRHJLk5yfeBp0/cKMmPkmzRX2+fZG6S65NcnORZtCDyvj6z8ookayU5vT/jmiRb9/c+NcmFSX6W5Bggi/oQSc5Kcm2Sm5Lss9Dfvtz7cnGStXrbBknO7++5NMlGT8i3KUmSlpbZxGwiDWb5oTsg6f8lWR54HXB+b3oxsGlV3dV/ZP9YVVsmWQn4SZILgc2BDYHnA2sDNwPfWui+awHHAtv0e61ZVQ8kORp4uKoO69edDHy1qi5L8kzgAmBj4JPAZVX16SQ7Av/zg/8Y3tGfsQpwTZLTq+p+YFVgblV9IMkn+r3fA3wD2LeqbkvyEuDrwKuX4GuUJElPELOJ2UQamoMX0nhZJcl1/fWlwDdpSyavrqq7evt2wAvS94wCqwPPAbYBTqmq+cDvkvzgUe6/FXDJxL2q6oHH6Me2wCbJfyYvnpJktf6MXfp7v5/kwUl8pgOS7Nxfr9f7ej/wL+DU3n4ScEaSJ/fPe9oCz15pEs+QJElTw2xiNpHGgoMX0niZV1WbLdjQfyj/smATsH9VXbDQdTsAtYj7ZxLXQNtS9tKqmvcofZnM+yeun00LGy+tqr8m+RGw8mNcXv25Dy38HUiSpMGYTcwm0liw5oU0/VwA7JdkBYAkz02yKnAJ8Ka+73Qd4FWP8t4rgFcmWb+/d83e/mdgtQWuu5C2TJJ+3Wb95SXAW3rb64A1FtHX1YEHezjYiDa7MmEWMDFD82baks8/AXcl2a0/I0leuIhnSJKkYZlNJE05By+k6ec42p7RuUluBI6hraI6E7gNuAE4Cvjxwm+sqntpe0HPSHI9/10aeTaw80RRLOAAYIu0ols389/K4p8Ctkkyl7ZE9FeL6Ov5wPJJfg58Brhygb/9BXhekmtp+0Y/3dvfAuzZ+3cTsNMkvhNJkjQcs4mkKZeqSa+ykiRJkiRJGjlXXkiSJEmSpLHm4IUkSZIkSRprDl5IkiRJkqSx5uCFJEmSJEkaaw5eSJIkSZKksebghSRJkiRJGmsOXkiSJEmSpLHm4IUkSZIkSRpr/wbj5EfFOAWP7AAAAABJRU5ErkJggg==",
- "text/plain": [
- "<Figure size 2016x504 with 4 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "make_confusion_matrix(y_test,X_test, clf5)"
+ "Zunächst wird der Typ der Daten nach dem Einlesen in das Notebook überprüft. Einlesefehler werden entsprechend korrigiert.\n",
+ "Dimensionalitäts reduktion: entfernte Attribute ohne Beschreibung. Fehlende Daten: Zeilen mit fehlenden Daten werden entfernt.\n",
+ "Datenkonvertierung: Dummy-Variablen werden erstellt."
]
},
{
- "cell_type": "code",
- "execution_count": 122,
+ "cell_type": "markdown",
"metadata": {
"editable": true,
"include": true,
- "scrolled": true,
+ "paragraph": "Recoding of Categorical Variables",
"slideshow": {
"slide_type": ""
},
"tags": []
},
+ "source": [
+ "## 3.1. Recoding of Categorical Variables"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "active_ipynb"
+ ]
+ },
"outputs": [
{
"data": {
@@ -29836,224 +1307,335 @@
" <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",
+ " <th>is_canceled</th>\n",
+ " <th>lead_time</th>\n",
+ " <th>stays_in_week_nights</th>\n",
+ " <th>adults</th>\n",
+ " <th>children</th>\n",
+ " <th>babies</th>\n",
+ " <th>is_repeated_guest</th>\n",
+ " <th>previous_cancellations</th>\n",
+ " <th>booking_changes</th>\n",
+ " <th>days_in_waiting_list</th>\n",
+ " <th>...</th>\n",
+ " <th>assigned_room_type_H</th>\n",
+ " <th>assigned_room_type_I</th>\n",
+ " <th>assigned_room_type_K</th>\n",
+ " <th>assigned_room_type_L</th>\n",
+ " <th>assigned_room_type_P</th>\n",
+ " <th>deposit_type_Non Refund</th>\n",
+ " <th>deposit_type_Refundable</th>\n",
+ " <th>customer_type_Group</th>\n",
+ " <th>customer_type_Transient</th>\n",
+ " <th>customer_type_Transient-Party</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",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>342</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>3</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>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",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>737</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>4</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>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",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</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>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",
+ " <td>3</td>\n",
+ " <td>0</td>\n",
+ " <td>13</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</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>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",
+ " <td>4</td>\n",
+ " <td>0</td>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</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",
" </tbody>\n",
"</table>\n",
+ "<p>5 rows × 226 columns</p>\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 "
+ " is_canceled lead_time stays_in_week_nights adults children babies \\\n",
+ "0 0 342 0 2 0.0 0 \n",
+ "1 0 737 0 2 0.0 0 \n",
+ "2 0 7 1 1 0.0 0 \n",
+ "3 0 13 1 1 0.0 0 \n",
+ "4 0 14 2 2 0.0 0 \n",
+ "\n",
+ " is_repeated_guest previous_cancellations booking_changes \\\n",
+ "0 0 0 3 \n",
+ "1 0 0 4 \n",
+ "2 0 0 0 \n",
+ "3 0 0 0 \n",
+ "4 0 0 0 \n",
+ "\n",
+ " days_in_waiting_list ... assigned_room_type_H assigned_room_type_I \\\n",
+ "0 0 ... 0 0 \n",
+ "1 0 ... 0 0 \n",
+ "2 0 ... 0 0 \n",
+ "3 0 ... 0 0 \n",
+ "4 0 ... 0 0 \n",
+ "\n",
+ " assigned_room_type_K assigned_room_type_L assigned_room_type_P \\\n",
+ "0 0 0 0 \n",
+ "1 0 0 0 \n",
+ "2 0 0 0 \n",
+ "3 0 0 0 \n",
+ "4 0 0 0 \n",
+ "\n",
+ " deposit_type_Non Refund deposit_type_Refundable customer_type_Group \\\n",
+ "0 0 0 0 \n",
+ "1 0 0 0 \n",
+ "2 0 0 0 \n",
+ "3 0 0 0 \n",
+ "4 0 0 0 \n",
+ "\n",
+ " customer_type_Transient customer_type_Transient-Party \n",
+ "0 1 0 \n",
+ "1 1 0 \n",
+ "2 1 0 \n",
+ "3 1 0 \n",
+ "4 1 0 \n",
+ "\n",
+ "[5 rows x 226 columns]"
]
},
- "execution_count": 122,
+ "execution_count": 26,
"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')), :]"
+ "df_dummies = pd.get_dummies(df, drop_first=True) # 0-1 encoding for categorical values\n",
+ "df_dummies.head()"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df_dummies.to_csv('train_dummies.csv', index = False) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
"metadata": {
"editable": true,
"include": true,
- "paragrph": "Funktionsbedeutung",
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7,\n",
+ " 8, 9,\n",
+ " ...\n",
+ " 119380, 119381, 119382, 119383, 119384, 119385, 119386, 119387,\n",
+ " 119388, 119389],\n",
+ " dtype='int64', length=118898)"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "## Funktionsbedeutung\n",
- "\n",
- "* Ein One vs Rest-Klassifikator besteht aus n einzelnen Klassifikatoren (wobei n = Anzahl der Klassen). Das macht es schwierig, die Bedeutung der Merkmale für den gesamten OvsR-Klassifikator zu interpretieren. Eine Möglichkeit besteht darin, die Koeffizienten für jedes einzelne Modell zu bestimmen und den Mittelwert über jeden Koeffizienten zu bilden."
+ "df_dummies.axes[0]"
]
},
{
"cell_type": "code",
- "execution_count": 123,
+ "execution_count": 29,
"metadata": {
"editable": true,
"include": true,
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['is_canceled', 'lead_time', 'stays_in_week_nights', 'adults',\n",
+ " 'children', 'babies', 'is_repeated_guest', 'previous_cancellations',\n",
+ " 'booking_changes', 'days_in_waiting_list',\n",
+ " ...\n",
+ " 'assigned_room_type_H', 'assigned_room_type_I', 'assigned_room_type_K',\n",
+ " 'assigned_room_type_L', 'assigned_room_type_P',\n",
+ " 'deposit_type_Non Refund', 'deposit_type_Refundable',\n",
+ " 'customer_type_Group', 'customer_type_Transient',\n",
+ " 'customer_type_Transient-Party'],\n",
+ " dtype='object', length=226)"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_dummies.axes[1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Modellierung und Auswertung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "active_ipynb"
+ ]
},
- "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)"
+ "# 4. Modellierung und Auswertung"
]
},
{
"cell_type": "code",
- "execution_count": 124,
+ "execution_count": null,
"metadata": {
"editable": true,
"include": true,
+ "paragraph": "Modellierung und Auswertung",
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
"outputs": [],
"source": [
- "feature_importance = pd.DataFrame({},[])\n",
- "feature_importance['Features'] = X_test.columns\n",
- "feature_importance['Importance'] = get_the_feature_importances(clf3,'logreg',2)"
+ "Der Datensatz wird mit seinen Dummy-Variablen hochgeladen und in einen Trainings- und einen Testsatz aufgeteilt.\n",
+ "Dann wird der Trainings- und Testprozess mit 3 verschiedenen Algorithmen durchgeführt und ausgewertet - Logistische Regression, Entscheidungsbaum,\n",
+ "Random Forest.\n",
+ "Fur Bewertung, Hyperparameter: \n",
+ "Output: überwachtes Lernen, Klassifikation\n",
+ "Datenaufteilung: 80% Trainingsdaten, 20% Testdaten.\n",
+ "Auswertungsmetriken DecisionTree: Genauigkeit= 0.82, Rückruf= 0.74, Präzision=0.76.\n",
+ "Auswertungsmetriken Logistische Regression: Genauigkeit= 0.78, Rückruf= 0.55,\n",
+ "Präzision= 0.78.\n",
+ "Auswertungsmetriken Random Forest: Genauigkeit= 0.82, Rückruf= 0.57, Genauigkeit= 0,90.\n"
]
},
{
@@ -30061,36 +1643,50 @@
"metadata": {
"editable": true,
"include": true,
- "jp-MarkdownHeadingCollapsed": true,
- "paragraph": "ergebnis",
+ "paragraph": "Test und Train Daten",
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
"source": [
- "## Results"
+ "## 4.1. Test and Train Data"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": 30,
"metadata": {
"editable": true,
+ "include": true,
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
+ "outputs": [],
"source": [
- "* The best model is the Logistic Regression OvsR Classifier. \n",
- "* It has a good accuracy (0.77:test, 0.81:train) and a good precision (0.87:test) for the class 'WALKING'.\n",
- "* The 10 most important feautures of this OvsR-Classifier are the following:"
+ "target = df_dummies['is_canceled'] # feature to be predicted\n",
+ "predictors = df_dummies.drop(['is_canceled'], axis = 1) # all other features are used as predictors"
]
},
{
"cell_type": "code",
- "execution_count": 125,
- "metadata": {},
+ "execution_count": 31,
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "active_ipynb"
+ ]
+ },
"outputs": [
{
"data": {
@@ -30113,126 +1709,368 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>Features</th>\n",
- " <th>Importance</th>\n",
+ " <th>lead_time</th>\n",
+ " <th>stays_in_week_nights</th>\n",
+ " <th>adults</th>\n",
+ " <th>children</th>\n",
+ " <th>babies</th>\n",
+ " <th>is_repeated_guest</th>\n",
+ " <th>previous_cancellations</th>\n",
+ " <th>booking_changes</th>\n",
+ " <th>days_in_waiting_list</th>\n",
+ " <th>hotel_Resort Hotel</th>\n",
+ " <th>...</th>\n",
+ " <th>assigned_room_type_H</th>\n",
+ " <th>assigned_room_type_I</th>\n",
+ " <th>assigned_room_type_K</th>\n",
+ " <th>assigned_room_type_L</th>\n",
+ " <th>assigned_room_type_P</th>\n",
+ " <th>deposit_type_Non Refund</th>\n",
+ " <th>deposit_type_Refundable</th>\n",
+ " <th>customer_type_Group</th>\n",
+ " <th>customer_type_Transient</th>\n",
+ " <th>customer_type_Transient-Party</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",
+ " <td>0</td>\n",
+ " <td>342</td>\n",
+ " <td>0</td>\n",
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" <tr>\n",
- " <th>29</th>\n",
- " <td>fBodyAccJerk-maxInds-X</td>\n",
- " <td>0.510996</td>\n",
+ " <td>1</td>\n",
+ " <td>737</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>4</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
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+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>18</th>\n",
- " <td>tBodyGyro-correlation()-X,Y</td>\n",
- " <td>0.501925</td>\n",
+ " <td>2</td>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</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",
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+ " <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>13</th>\n",
- " <td>tBodyGyro-mean()-X</td>\n",
- " <td>0.490046</td>\n",
+ " <td>3</td>\n",
+ " <td>13</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0.0</td>\n",
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+ " <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>19</th>\n",
- " <td>tBodyGyro-correlation()-X,Z</td>\n",
- " <td>0.468363</td>\n",
+ " <td>4</td>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>0.0</td>\n",
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+ " <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>5 rows × 225 columns</p>\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"
+ " lead_time stays_in_week_nights adults children babies \\\n",
+ "0 342 0 2 0.0 0 \n",
+ "1 737 0 2 0.0 0 \n",
+ "2 7 1 1 0.0 0 \n",
+ "3 13 1 1 0.0 0 \n",
+ "4 14 2 2 0.0 0 \n",
+ "\n",
+ " is_repeated_guest previous_cancellations booking_changes \\\n",
+ "0 0 0 3 \n",
+ "1 0 0 4 \n",
+ "2 0 0 0 \n",
+ "3 0 0 0 \n",
+ "4 0 0 0 \n",
+ "\n",
+ " days_in_waiting_list hotel_Resort Hotel ... assigned_room_type_H \\\n",
+ "0 0 1 ... 0 \n",
+ "1 0 1 ... 0 \n",
+ "2 0 1 ... 0 \n",
+ "3 0 1 ... 0 \n",
+ "4 0 1 ... 0 \n",
+ "\n",
+ " assigned_room_type_I assigned_room_type_K assigned_room_type_L \\\n",
+ "0 0 0 0 \n",
+ "1 0 0 0 \n",
+ "2 0 0 0 \n",
+ "3 0 0 0 \n",
+ "4 0 0 0 \n",
+ "\n",
+ " assigned_room_type_P deposit_type_Non Refund deposit_type_Refundable \\\n",
+ "0 0 0 0 \n",
+ "1 0 0 0 \n",
+ "2 0 0 0 \n",
+ "3 0 0 0 \n",
+ "4 0 0 0 \n",
+ "\n",
+ " customer_type_Group customer_type_Transient customer_type_Transient-Party \n",
+ "0 0 1 0 \n",
+ "1 0 1 0 \n",
+ "2 0 1 0 \n",
+ "3 0 1 0 \n",
+ "4 0 1 0 \n",
+ "\n",
+ "[5 rows x 225 columns]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "predictors.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "active_ipynb"
+ ]
+ },
+ "outputs": [],
+ "source": [
+ "X_train, X_test, y_train, y_test = train_test_split(predictors, target, test_size=0.2, random_state=123)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Decision Tree",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "active_ipynb"
+ ]
+ },
+ "source": [
+ "## 4.2. DecisionTree"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "active_ipynb"
+ ]
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DecisionTreeClassifier()"
]
},
- "execution_count": 125,
+ "execution_count": 33,
"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)"
+ "tree = DecisionTreeClassifier()\n",
+ "tree.fit(X_train, y_train)"
]
},
{
- "cell_type": "markdown",
- "metadata": {},
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "active_ipynb"
+ ]
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "12983 2041 2295 6461\n"
+ ]
+ }
+ ],
"source": [
- "-->The most of this important features comes from the **gyroscope sensor** in the smartphone"
+ "tn, fp, fn, tp = confusion_matrix(y_test, tree.predict(X_test)).ravel() \n",
+ "print(tn, fp, fn, tp)"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": 35,
"metadata": {
"editable": true,
"include": true,
- "paragraph": "Modell Deployment",
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.97 0.99 0.98 59721\n",
+ " 1 0.99 0.94 0.97 35397\n",
+ "\n",
+ " accuracy 0.98 95118\n",
+ " macro avg 0.98 0.97 0.97 95118\n",
+ "weighted avg 0.98 0.98 0.98 95118\n",
+ "\n"
+ ]
+ }
+ ],
"source": [
- "## Final model for deployment"
+ "print(classification_report(y_train, tree.predict(X_train)))"
]
},
{
"cell_type": "code",
- "execution_count": 126,
+ "execution_count": 36,
"metadata": {
"editable": true,
"include": true,
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.85 0.86 0.86 15024\n",
+ " 1 0.76 0.74 0.75 8756\n",
+ "\n",
+ " accuracy 0.82 23780\n",
+ " macro avg 0.80 0.80 0.80 23780\n",
+ "weighted avg 0.82 0.82 0.82 23780\n",
+ "\n"
+ ]
+ }
+ ],
"source": [
- "final_model = clf3.best_estimator_"
+ "print(classification_report(y_test, tree.predict(X_test)))"
]
},
{
@@ -30240,300 +2078,189 @@
"metadata": {
"editable": true,
"include": true,
- "paragraph": "Deployment",
+ "paragraph": "Logistic Regression",
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
"source": [
- "# 6. Deployment\n",
- "Test the prediction locally:"
+ "## 4.3. Logistic Regression "
]
},
{
"cell_type": "code",
- "execution_count": 127,
+ "execution_count": 37,
"metadata": {
"editable": true,
"include": true,
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+ "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+ "\n",
+ "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+ " https://scikit-learn.org/stable/modules/preprocessing.html\n",
+ "Please also refer to the documentation for alternative solver options:\n",
+ " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+ " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
+ ]
+ },
{
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- " <th></th>\n",
- " <th>tBodyAcc-mean()-Y</th>\n",
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- " <th>tBodyAcc-correlation()-X,Z</th>\n",
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"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",
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- "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",
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- "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",
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- "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",
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- "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",
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- "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]"
+ "LogisticRegression()"
]
},
- "execution_count": 127,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# recap: print first rows of predictors (here: training data without predicted column \"Survived\")\n",
- "X_test.head(4)"
+ "logreg = LogisticRegression()\n",
+ "logreg.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
- "execution_count": 128,
+ "execution_count": 38,
"metadata": {
"editable": true,
"include": true,
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
"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"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[13634 1390]\n",
+ " [ 3870 4886]]\n"
+ ]
}
],
"source": [
- "# recap: print first rows of training data\n",
- "data_test['Activity'].head(4)"
+ "print(confusion_matrix(y_test, logreg.predict(X_test)))"
]
},
{
"cell_type": "code",
- "execution_count": 129,
+ "execution_count": 39,
"metadata": {
"editable": true,
"include": true,
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
},
"outputs": [
{
"data": {
"text/plain": [
- "array(['LAYING', 'STANDING', 'STANDING', 'STANDING'], dtype='<U8')"
+ "Text(0.5, 39.5, 'Predicted label')"
]
},
- "execution_count": 129,
+ "execution_count": 39,
"metadata": {},
"output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x504 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "conf_mat = confusion_matrix(y_test, logreg.predict(X_test))\n",
+ "df_cm = pd.DataFrame(conf_mat, index=['0','1'], columns=['0', '1'],)\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": 40,
+ "metadata": {
+ "include": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.78 0.91 0.84 15024\n",
+ " 1 0.78 0.56 0.65 8756\n",
+ "\n",
+ " accuracy 0.78 23780\n",
+ " macro avg 0.78 0.73 0.74 23780\n",
+ "weighted avg 0.78 0.78 0.77 23780\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(classification_report(y_test, logreg.predict(X_test)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "include": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.78 0.91 0.84 59721\n",
+ " 1 0.78 0.56 0.66 35397\n",
+ "\n",
+ " accuracy 0.78 95118\n",
+ " macro avg 0.78 0.74 0.75 95118\n",
+ "weighted avg 0.78 0.78 0.77 95118\n",
+ "\n"
+ ]
}
],
"source": [
- "final_model.predict(X_test.head(4))"
+ "print(classification_report(y_train, logreg.predict(X_train)))"
]
},
{
@@ -30541,21 +2268,160 @@
"metadata": {
"editable": true,
"include": true,
+ "paragraph": "Random Forest",
"slideshow": {
"slide_type": ""
},
- "tags": []
+ "tags": [
+ "active_ipynb"
+ ]
+ },
+ "source": [
+ "## 4.4. Random Forest"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "active_ipynb"
+ ]
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Max tree depth: 5\n",
+ "Train results: precision recall f1-score support\n",
+ "\n",
+ " 0 0.73 1.00 0.84 59721\n",
+ " 1 1.00 0.37 0.54 35397\n",
+ "\n",
+ " accuracy 0.77 95118\n",
+ " macro avg 0.86 0.69 0.69 95118\n",
+ "weighted avg 0.83 0.77 0.73 95118\n",
+ "\n",
+ "Test results: precision recall f1-score support\n",
+ "\n",
+ " 0 0.73 1.00 0.84 15024\n",
+ " 1 1.00 0.36 0.53 8756\n",
+ "\n",
+ " accuracy 0.76 23780\n",
+ " macro avg 0.86 0.68 0.69 23780\n",
+ "weighted avg 0.83 0.76 0.73 23780\n",
+ "\n",
+ "Max tree depth: 10\n",
+ "Train results: precision recall f1-score support\n",
+ "\n",
+ " 0 0.75 0.98 0.85 59721\n",
+ " 1 0.94 0.45 0.61 35397\n",
+ "\n",
+ " accuracy 0.78 95118\n",
+ " macro avg 0.85 0.71 0.73 95118\n",
+ "weighted avg 0.82 0.78 0.76 95118\n",
+ "\n",
+ "Test results: precision recall f1-score support\n",
+ "\n",
+ " 0 0.75 0.98 0.85 15024\n",
+ " 1 0.94 0.44 0.59 8756\n",
+ "\n",
+ " accuracy 0.78 23780\n",
+ " macro avg 0.84 0.71 0.72 23780\n",
+ "weighted avg 0.82 0.78 0.76 23780\n",
+ "\n",
+ "Max tree depth: 20\n",
+ "Train results: precision recall f1-score support\n",
+ "\n",
+ " 0 0.81 0.97 0.88 59721\n",
+ " 1 0.92 0.62 0.74 35397\n",
+ "\n",
+ " accuracy 0.84 95118\n",
+ " macro avg 0.87 0.79 0.81 95118\n",
+ "weighted avg 0.85 0.84 0.83 95118\n",
+ "\n",
+ "Test results: precision recall f1-score support\n",
+ "\n",
+ " 0 0.80 0.96 0.87 15024\n",
+ " 1 0.90 0.58 0.70 8756\n",
+ "\n",
+ " accuracy 0.82 23780\n",
+ " macro avg 0.85 0.77 0.79 23780\n",
+ "weighted avg 0.83 0.82 0.81 23780\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "tree_depth = [5, 10, 20]\n",
+ "for i in tree_depth:\n",
+ " rf = RandomForestClassifier(max_depth=i)\n",
+ " rf.fit(X_train, y_train)\n",
+ " print('Max tree depth: ', i)\n",
+ " print('Train results: ', classification_report(y_train, rf.predict(X_train)))\n",
+ " print('Test results: ',classification_report(y_test, rf.predict(X_test)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "active_ipynb"
+ ]
},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Max tree depth: 20\n",
+ "Train results: precision recall f1-score support\n",
+ "\n",
+ " 0 0.80 0.98 0.88 59721\n",
+ " 1 0.94 0.60 0.73 35397\n",
+ "\n",
+ " accuracy 0.84 95118\n",
+ " macro avg 0.87 0.79 0.81 95118\n",
+ "weighted avg 0.85 0.84 0.83 95118\n",
+ "\n",
+ "Test results: precision recall f1-score support\n",
+ "\n",
+ " 0 0.79 0.97 0.87 15024\n",
+ " 1 0.91 0.56 0.69 8756\n",
+ "\n",
+ " accuracy 0.82 23780\n",
+ " macro avg 0.85 0.76 0.78 23780\n",
+ "weighted avg 0.83 0.82 0.80 23780\n",
+ "\n"
+ ]
+ }
+ ],
"source": [
- "-->final_model works and can be deplyoed"
+ "rf = RandomForestClassifier(max_depth=20)\n",
+ "rf.fit(X_train, y_train)\n",
+ "print('Max tree depth: ', i)\n",
+ "print('Train results: ', classification_report(y_train, rf.predict(X_train)))\n",
+ "print('Test results: ',classification_report(y_test, rf.predict(X_test)))"
]
}
],
"metadata": {
- "branche": "Landwirtschaft",
- "category": "Forschung",
- "dataSource": "https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones",
- "funktion": "Operations",
+ "branche": "Tourismus",
+ "category": "Hotel",
+ "dataSource": "https://www.kaggle.com/jessemostipak/hotel-booking-demand",
+ "funktion": "Marketforschung",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
@@ -30573,10 +2439,10 @@
"pygments_lexer": "ipython3",
"version": "3.12.3"
},
- "repoLink": "https://github.com/AlexRossmann/machine-learning-services/tree/main/Agriculture/Analysis%20of%20the%20movement%20and%20activity%20of%20free-ranging%20cattle",
+ "repoLink": "https://github.com/AlexRossmann/machine-learning-services/tree/main/Marketing/Generation%20of%20Individual%20Playlists",
"skipNotebookInDeployment": false,
- "teaser": "Landwirte geraten immer weiter unter wirtschaftlichen Druck. Beispielsweise kam es durch die Coronapandemie zu teuren logistischen Problemen. Gleichzeitig stellen immer mehr Konsumenten höhere Anforderungen an eine artgerechte Tierhaltung. Diese Marktbedingungen erscheinen für Landwirte problematisch, da letzterer Aspekt natürlich mit gewissen Kosten verbunden ist und ein generelles Umdenken notwendig macht ",
- "title": "Erkennung der Aktivitäten von Kühen auf dem Feld"
+ "teaser": "Ein wichtiges Ziel für jedes Unternehmen liegt in der Kunden Zufriedenheit und Gewinn maximierung. Problematisch ist wenn die Kunde Ihre Buchungen plotzlich stonieren ohne vorkentnisse. Diese kann nicht nur zu Gewinn velust und negative Bewertung das Hotel fuhren sondern auch zu wirtschaft unsicherheit. Abschätzung des Kundenverhaltens in vorraus bei Buchungsstornierungen für Planung von Kapazität ist notwending.. ",
+ "title": "Vorherzusagen von Buchungstornierungen"
},
"nbformat": 4,
"nbformat_minor": 4