From ca11c4f7e254989388373c4d716068c014733859 Mon Sep 17 00:00:00 2001
From: Hannes Sattler <hannes.sattler@student.reutlingen-university.de>
Date: Tue, 25 Jun 2024 08:45:22 +0000
Subject: [PATCH] Improvement rating red wine

---
 .../neues_notebook.ipynb                      | 3882 +++++++++++++++++
 1 file changed, 3882 insertions(+)
 create mode 100644 Rating/What Quality does the Red wine have/neues_notebook.ipynb

diff --git a/Rating/What Quality does the Red wine have/neues_notebook.ipynb b/Rating/What Quality does the Red wine have/neues_notebook.ipynb
new file mode 100644
index 0000000..8421879
--- /dev/null
+++ b/Rating/What Quality does the Red wine have/neues_notebook.ipynb	
@@ -0,0 +1,3882 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "695e20ce",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Welche Qualität hat der Rotwein? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "692fe04c",
+   "metadata": {
+    "editable": true,
+    "include": false,
+    "paragraph": "BusinessUnderstanding",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# 1. Business Understanding\n",
+    "Eine Weinkellerei möchte sicherstellen, dass sie stets qualitativ hochwertigen und schmackhaften Wein produziert.\n",
+    "Sie beschäftigen professionelle Weinverkoster, die alle neuen Weinpartien testen.\n",
+    "Diese Fachleute bewerten den Wein dann auf einer Skala von 1 bis 10.\n",
+    "Dieses Verfahren ist jedoch teuer und subjektiv.\n",
+    "Die Weinmanufaktur möchte ein maschinelles Lernmodell einsetzen, um Vorhersagen über die Qualität zu treffen.\n",
+    "Das ultimative Ziel ist die Entwicklung eines Modells \n",
+    "zu entwickeln, das neue Weinpartien anhand ihrer chemischen Eigenschaften automatisch kategorisiert.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dff3671b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# 2. Data Understanding  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7516a850",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 2.1. Bibliotheken Importieren"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "id": "0115c9e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import math\n",
+    "import pickle\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Activation, Dropout\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3798a6d9",
+   "metadata": {},
+   "source": [
+    "## 2.2. Daten Auslesen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "id": "df283471",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/What_Quality_does_the_Red_wine_have_winequality.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68adb76f",
+   "metadata": {},
+   "source": [
+    "## 2.3. Deskriptive Datenanalyse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0348321",
+   "metadata": {
+    "editable": true,
+    "include": false,
+    "paragraph": "DataUnderstanding",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### Datenbeschreibung\n",
+    "Der Datensatz mit dem Namen \"Wine Quality Data Set\" wurde der UCI-Website entnommen \n",
+    "entnommen und liefert uns reale Daten für unsere Weinmanufaktur. Die Daten \n",
+    "wurden im Jahr 2009 erhoben und stammen aus Nordportugal. Der Datensatz enthält \n",
+    "Einträge für Rot- und Weißweine. Der Datensatz für die \n",
+    "Rotweine hat also 12 Spalten und 1600 Einträge. Die Spalten sind \n",
+    "nach den Inhaltsstoffen und der Qualität eines Rotweins in Tabelle 1 benannt.  \n",
+    "\n",
+    "| Feature  | Data Type|\n",
+    "|-----|------|\n",
+    "|fixed acidity       |   float64  |\n",
+    "|volatile acidity    |   float64  |\n",
+    "|citric acid         |   float64  |\n",
+    "|residual sugar      |   float64  |\n",
+    "|chlorides           |   float64  |\n",
+    "|free sulfur dioxide |   float64  |\n",
+    "|total sulfur dioxide|   float64  |\n",
+    "|density             |   float64  |\n",
+    "|pH                  |   float64  |\n",
+    "|sulphates           |   float64  |\n",
+    "|alcohol             |   float64  |\n",
+    "|quality             |   int64    |\n",
+    "\n",
+    "Ein Rotwein besteht aus Wasser, Alkohol und Extrakten. Die genaue \n",
+    "Mischung dieser 3 wesentlichen Bestandteile kann einen Rotwein zu einem echten \n",
+    "ein wahrer Genuss. Meiner Meinung nach enthält ein guter Rotwein \n",
+    "einen hohen Alkohol- und Zuckergehalt. Ob meine Meinung über einen guten Wein richtig ist \n",
+    "wahr ist, können wir im nächsten Kapitel näher betrachten.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "id": "5819055a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1599 entries, 0 to 1598\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   fixed acidity         1599 non-null   float64\n",
+      " 1   volatile acidity      1599 non-null   float64\n",
+      " 2   citric acid           1599 non-null   float64\n",
+      " 3   residual sugar        1599 non-null   float64\n",
+      " 4   chlorides             1599 non-null   float64\n",
+      " 5   free sulfur dioxide   1599 non-null   float64\n",
+      " 6   total sulfur dioxide  1599 non-null   float64\n",
+      " 7   density               1599 non-null   float64\n",
+      " 8   pH                    1599 non-null   float64\n",
+      " 9   sulphates             1599 non-null   float64\n",
+      " 10  alcohol               1599 non-null   float64\n",
+      " 11  quality               1599 non-null   int64  \n",
+      "dtypes: float64(11), int64(1)\n",
+      "memory usage: 150.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "id": "efeeee27",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "      <td>1599.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>8.319637</td>\n",
+       "      <td>0.527821</td>\n",
+       "      <td>0.270976</td>\n",
+       "      <td>2.538806</td>\n",
+       "      <td>0.087467</td>\n",
+       "      <td>15.874922</td>\n",
+       "      <td>46.467792</td>\n",
+       "      <td>0.996747</td>\n",
+       "      <td>3.311113</td>\n",
+       "      <td>0.658149</td>\n",
+       "      <td>10.422983</td>\n",
+       "      <td>5.636023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.741096</td>\n",
+       "      <td>0.179060</td>\n",
+       "      <td>0.194801</td>\n",
+       "      <td>1.409928</td>\n",
+       "      <td>0.047065</td>\n",
+       "      <td>10.460157</td>\n",
+       "      <td>32.895324</td>\n",
+       "      <td>0.001887</td>\n",
+       "      <td>0.154386</td>\n",
+       "      <td>0.169507</td>\n",
+       "      <td>1.065668</td>\n",
+       "      <td>0.807569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.600000</td>\n",
+       "      <td>0.120000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.012000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.990070</td>\n",
+       "      <td>2.740000</td>\n",
+       "      <td>0.330000</td>\n",
+       "      <td>8.400000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>7.100000</td>\n",
+       "      <td>0.390000</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>0.070000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>0.995600</td>\n",
+       "      <td>3.210000</td>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>9.500000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>0.520000</td>\n",
+       "      <td>0.260000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>0.079000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>0.996750</td>\n",
+       "      <td>3.310000</td>\n",
+       "      <td>0.620000</td>\n",
+       "      <td>10.200000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>9.200000</td>\n",
+       "      <td>0.640000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>0.997835</td>\n",
+       "      <td>3.400000</td>\n",
+       "      <td>0.730000</td>\n",
+       "      <td>11.100000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>15.900000</td>\n",
+       "      <td>1.580000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>15.500000</td>\n",
+       "      <td>0.611000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>289.000000</td>\n",
+       "      <td>1.003690</td>\n",
+       "      <td>4.010000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>14.900000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
+       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
+       "mean        8.319637          0.527821     0.270976        2.538806   \n",
+       "std         1.741096          0.179060     0.194801        1.409928   \n",
+       "min         4.600000          0.120000     0.000000        0.900000   \n",
+       "25%         7.100000          0.390000     0.090000        1.900000   \n",
+       "50%         7.900000          0.520000     0.260000        2.200000   \n",
+       "75%         9.200000          0.640000     0.420000        2.600000   \n",
+       "max        15.900000          1.580000     1.000000       15.500000   \n",
+       "\n",
+       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
+       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
+       "mean      0.087467            15.874922             46.467792     0.996747   \n",
+       "std       0.047065            10.460157             32.895324     0.001887   \n",
+       "min       0.012000             1.000000              6.000000     0.990070   \n",
+       "25%       0.070000             7.000000             22.000000     0.995600   \n",
+       "50%       0.079000            14.000000             38.000000     0.996750   \n",
+       "75%       0.090000            21.000000             62.000000     0.997835   \n",
+       "max       0.611000            72.000000            289.000000     1.003690   \n",
+       "\n",
+       "                pH    sulphates      alcohol      quality  \n",
+       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
+       "mean      3.311113     0.658149    10.422983     5.636023  \n",
+       "std       0.154386     0.169507     1.065668     0.807569  \n",
+       "min       2.740000     0.330000     8.400000     3.000000  \n",
+       "25%       3.210000     0.550000     9.500000     5.000000  \n",
+       "50%       3.310000     0.620000    10.200000     6.000000  \n",
+       "75%       3.400000     0.730000    11.100000     6.000000  \n",
+       "max       4.010000     2.000000    14.900000     8.000000  "
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe(include=\"all\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "098d741c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Der Datensatz enthält 1599 Einträge. Die Spalten \"freies Schwefeldioxid\" und \"Gesamtschwefeldioxid\" haben sehr hohe Maximalwerte. Außerdem sehen wir, dass unsere Zielvariable den kleinsten Wert von 3 und den größten Wert von 8 hat. Hier müssen wir überlegen, ob die Zielvariable in gut und schlecht unterteilt werden muss."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fce4d938",
+   "metadata": {},
+   "source": [
+    "## 2.4 Datenbereinigung"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "100b49f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "fixed acidity           0\n",
+       "volatile acidity        0\n",
+       "citric acid             0\n",
+       "residual sugar          0\n",
+       "chlorides               0\n",
+       "free sulfur dioxide     0\n",
+       "total sulfur dioxide    0\n",
+       "density                 0\n",
+       "pH                      0\n",
+       "sulphates               0\n",
+       "alcohol                 0\n",
+       "quality                 0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f56072a9",
+   "metadata": {},
+   "source": [
+    "There are no Null values in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "id": "c99e11ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.35</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.35</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.430</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.106</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>0.99660</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1567</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.695</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.99546</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1579</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.560</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0.99402</td>\n",
+       "      <td>3.54</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1581</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.560</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.053</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0.99402</td>\n",
+       "      <td>3.54</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1592</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>0.510</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.99574</td>\n",
+       "      <td>3.42</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1596</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>0.510</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.99574</td>\n",
+       "      <td>3.42</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>460 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0               7.4             0.700         0.00             1.9      0.076   \n",
+       "4               7.4             0.700         0.00             1.9      0.076   \n",
+       "9               7.5             0.500         0.36             6.1      0.071   \n",
+       "11              7.5             0.500         0.36             6.1      0.071   \n",
+       "22              7.9             0.430         0.21             1.6      0.106   \n",
+       "...             ...               ...          ...             ...        ...   \n",
+       "1567            7.2             0.695         0.13             2.0      0.076   \n",
+       "1579            6.2             0.560         0.09             1.7      0.053   \n",
+       "1581            6.2             0.560         0.09             1.7      0.053   \n",
+       "1592            6.3             0.510         0.13             2.3      0.076   \n",
+       "1596            6.3             0.510         0.13             2.3      0.076   \n",
+       "\n",
+       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
+       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
+       "9                    17.0                 102.0  0.99780  3.35       0.80   \n",
+       "11                   17.0                 102.0  0.99780  3.35       0.80   \n",
+       "22                   10.0                  37.0  0.99660  3.17       0.91   \n",
+       "...                   ...                   ...      ...   ...        ...   \n",
+       "1567                 12.0                  20.0  0.99546  3.29       0.54   \n",
+       "1579                 24.0                  32.0  0.99402  3.54       0.60   \n",
+       "1581                 24.0                  32.0  0.99402  3.54       0.60   \n",
+       "1592                 29.0                  40.0  0.99574  3.42       0.75   \n",
+       "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
+       "\n",
+       "      alcohol  quality  \n",
+       "0         9.4        5  \n",
+       "4         9.4        5  \n",
+       "9        10.5        5  \n",
+       "11       10.5        5  \n",
+       "22        9.5        5  \n",
+       "...       ...      ...  \n",
+       "1567     10.1        5  \n",
+       "1579     11.3        5  \n",
+       "1581     11.3        5  \n",
+       "1592     11.0        6  \n",
+       "1596     11.0        6  \n",
+       "\n",
+       "[460 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data.duplicated(keep=False)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0776de27",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Es gibt Duplikate in dem Datensatz. Aber in diesem Fall wollen wir diese nicht entfernen, da es sich eigentlich um verschiedene Weinpartien handelt, die zufällig die gleichen chemischen Eigenschaften und die gleiche Qualität haben. Es wäre nicht schlecht, wenn sich das Modell auf die häufigeren Weine spezialisieren würde"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "id": "dccbf1e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_clean = data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "id": "3d804f5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='quality', ylabel='count'>"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Analyse the target variable\n",
+    "plt.figure(figsize =(12, 8))\n",
+    "sns.countplot(x=\"quality\", data=data_clean )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5b72886",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# 3. Data Preparation  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3085e107",
+   "metadata": {},
+   "source": [
+    "## 3.1 Ausreißer entfernen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "id": "9e621458",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:title={'center':'fixed acidity'}>,\n",
+       "        <AxesSubplot:title={'center':'volatile acidity'}>,\n",
+       "        <AxesSubplot:title={'center':'citric acid'}>],\n",
+       "       [<AxesSubplot:title={'center':'residual sugar'}>,\n",
+       "        <AxesSubplot:title={'center':'chlorides'}>,\n",
+       "        <AxesSubplot:title={'center':'free sulfur dioxide'}>],\n",
+       "       [<AxesSubplot:title={'center':'total sulfur dioxide'}>,\n",
+       "        <AxesSubplot:title={'center':'density'}>,\n",
+       "        <AxesSubplot:title={'center':'pH'}>],\n",
+       "       [<AxesSubplot:title={'center':'sulphates'}>,\n",
+       "        <AxesSubplot:title={'center':'alcohol'}>,\n",
+       "        <AxesSubplot:title={'center':'quality'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x1440 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_clean.hist(figsize=(20,20), bins= 60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fabf51f0",
+   "metadata": {
+    "editable": true,
+    "include": false,
+    "paragraph": "DataPreparation",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Anhand der Histogramme aller Spalten können wir nun auf einen Blick sehen, wie deren Werte verteilt sind. Es fällt auf, dass einige Merkmale keine Normalverteilung aufweisen und daher Ausreißer in ihren Werten haben."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb933c25",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Fester Säuregehalt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "id": "249168e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='fixed acidity', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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V9dlR1zdMg/4l59g6q8cgYy2Bf91rdLq4UtgNvL6qrm2m5LwmyeXNuvdU1Ts7qGkkBv1LzrF1Vo9BxloC/7rX6Iw8FKpqO7C9Wf5pkpuAw0ZdhyTpvjp9+ijJWuAo4KtN02uTXJ/knCSPmGWfzUm2Jtk6NTU10yaS5jDdZTXXz7r1G7ouUx3p7EZzkocAlwCnVdVPkpwF/CW9+wx/CbwLeNme+1XVFmALwPj4eI2uYqk7iznPwyBdVnZXrV6dhEKS+9MLhPOr6hMAVbWjb/0HgU93UZu0FDnPg0Zl5N1HSQKcDdxUVe/uaz+kb7PnATeOujZJWu26uFJ4GvBi4IYk1zVtbwVOTrKRXvfR7cArO6hNkla1Lp4++icgM6xaku8kDPpuwT0F95vprPr4/oGkpc43muexN+8WvOCsf5x3G2klGeSPJl+8W14MBUkLNsgfTT7JtLw4SqokqWUoSJJahoIkqWUoSJJahoIkqWUoSJJahoIkqeV7CpLuY9AZ4QZ5S9/Z5ZYXQ0HSfQw6I9wgb+k7u9zyYveRJKnllYKkJWGQbia7mIbPUJC0JDgj3NJg95EkqWUoSJJaS677KMnxwHuB/YAPVdUZw/quQcaCd2IcaelYDY+3Djqx17DOcUmFQpL9gP8FPAuYAL6e5LKq+vYwvm+QseCdGEdaOgZ9vPWi1xw90pvWiz1D40nvv3reYw3r/sqSCgXgycCtVXUbQJKPAScCQwkFSSvTIOExSHDAYOGxkmZoTFV1WkC/JM8Hjq+qP2w+vxj43ap6bd82m4HNzccNwPK8RpzdwcCPui5iCDyv5cXzWl729rx+q6rGZlqx1K4U5lVVW4AtXdcxLEm2VtV413UsNs9refG8lpfFPK+l9vTRJHBE3+fDmzZJ0ggstVD4OrA+yZFJHgC8ELis45okadVYUt1HVbU7yWuBL9B7JPWcqvpWx2WN2krtGvO8lhfPa3lZtPNaUjeaJUndWmrdR5KkDhkKkqSWobCEJDkwycVJvpPkpiS/13VNiyHJnyT5VpIbk1yQ5EFd17QQSc5JsjPJjX1tByW5PMktze9HdFnjQsxyXu9o/j28PsmlSQ7ssMQFmem8+ta9PkklObiL2hZqtnNK8rrmn9e3kvzNvnyHobC0vBf4fFU9BngCcFPH9eyzJIcBfwyMV9Xj6D1A8MJuq1qwc4Hj92h7M3BlVa0Hrmw+Lzfnct/zuhx4XFU9Hvhn4C2jLmoRnMt9z4skRwDPBr4/6oIWwbnscU5JjqE38sMTqup3gHfuyxcYCktEkocDzwDOBqiqXVX1/zotavHsD/xGkv2BBwPbOq5nQarqauCuPZpPBM5rls8DNo2ypsUw03lV1Reranfz8Sv03hlaVmb55wXwHuBNwLJ7ymaWc3o1cEZV/bLZZue+fIehsHQcCUwBH07yjSQfSjL/wCxLXFVN0vvL5fvAduDHVfXFbqtaVGuqanuz/ENgTZfFDMnLgM91XcRiSHIiMFlV3+y6lkX0aODpSb6a5KokT9qXgxkKS8f+wBOBs6rqKOBnLM+uiF/T9LGfSC/0DgUOSPJfuq1qOKr3fPey++tzLkneBuwGzu+6ln2V5MHAW4E/67qWRbY/cBDwFOCNwEVJ5hmLdXaGwtIxAUxU1VebzxfTC4nl7jjge1U1VVW/Aj4BPLXjmhbTjiSHADS/9+nSfSlJcirw+8CLamW80PQoen+cfDPJ7fS6xK5N8m87rWrfTQCfqJ6vAffQGyBvQQyFJaKqfgj8IMmGpulYVsaQ4d8HnpLkwc1fL8eyAm6g97kMOKVZPgX4VIe1LJpmsqs3Ac+tqp93Xc9iqKobquo3q2ptVa2l9x/TJzb/31vOPgkcA5Dk0cAD2IeRYA2FpeV1wPlJrgc2An/dbTn7rrnyuRi4FriB3r9zy3KogSQXAF8GNiSZSPJy4AzgWUluoXdVNLSZAodllvN6H/BQ4PIk1yX5QKdFLsAs57WszXJO5wDrmsdUPwacsi9Xdg5zIUlqeaUgSWoZCpKklqEgSWoZCpKklqEgSWoZClrxkvxxM+rs+Umem2Sf3xRPcnSSTy/Ccf4iyXFzHb+/5iSbkjx2X79Xms2Smo5TGpI/Ao6rqonm85KZ97uq5h1yoaou496aNwGfZmW82KglyCsFrWjNS1frgM818zqcmuR9zbpPJXlJs/zKJOc3y89O8uUk1yb5eJKHNO3HN2PWXwv8wSzftzbJPzb7XpvkqX3r/jTJDUm+meSMpu3cJM+f6/jTNTfHei7wjuaFskc1205vt77/s7QQXiloRauqVzVDNhxTVT9qxvOZthn4P0m+B7ye3nAcBwP/jd6Vxc+S/ClwejNxyQeBZwK3AhfO8pU7gWdV1S+SrAcuAMaTnEBvYMDfraqfJzmof6f0Jh6a8/hV9X+TXAZ8uqoubvb7cZKNVXUd8FLgw3v9P5LUxysFrVpVtYPeiJlfAl5fVXfRG2nysfTC4jp64xn9FvAYegP73dIMIfC/Zzns/YEPJrkB+HhzLOgNgfHh6XGEmu/qN+jx9/Qh4KVJ9gNeAHx0wP2kGXmloNXu3wN30hvWGyDA5VV1cv9GSTYOeLw/AXbQmznvfsAvFqfMWV0CvB34e+CaqrpzyN+nFc4rBa1aSZ4MnAAcBbwhyZH0Zhl7WpLfbrY5oBl58jvA2iSPanY/eaZjAg8HtlfVPcCL6U0/Cr3pLV/ajOnPnt1He3H8n9IbqA6AqvoF8AXgLOw60iIwFLQqJXkgvT78l1XVNnr3FM6hN+TwqcAFzWi1XwYe0/zHdzPwmeZm7mzzJrwfOCXJN+l1Cf0MoKo+T+8Joq1Nt9Qb+nfai+N/DHhjerPzTQfI+fTG0F9JM9qpI46SKi1zSd4APLyq/nvXtWj5856CtIwluZTejGLP7LoWrQxeKUiSWt5TkCS1DAVJUstQkCS1DAVJUstQkCS1/j8OsRDwURBfOQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(data_clean['fixed acidity'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "912b4931",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Das Diagramm sieht normalverteilt aus, deshalb bleiben die Werte im ersten Schritt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb85b95b",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Volatile Acidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "id": "0a7426cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='volatile acidity', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 207,
+     "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": [
+    "sns.histplot(data_clean['volatile acidity'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "id": "ae9099ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.02"
+      ]
+     },
+     "execution_count": 208,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q1 = data_clean['volatile acidity'].quantile(0.99)\n",
+    "q1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "740748ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='volatile acidity', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 209,
+     "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": [
+    "data1 = data_clean[data_clean['volatile acidity']<q1]\n",
+    "sns.histplot(data1['volatile acidity'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4de95712",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Citric Acid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "d835ff39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='citric acid', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 210,
+     "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": [
+    "sns.histplot(data1['citric acid'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42156bfe",
+   "metadata": {},
+   "source": [
+    "Residual Sugar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "id": "fdafcc69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='residual sugar', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 211,
+     "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": [
+    "sns.histplot(data1['residual sugar'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "id": "d7a897c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='quality', ylabel='residual sugar'>"
+      ]
+     },
+     "execution_count": 212,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 7))\n",
+    "sns.boxplot(x='quality',y='residual sugar', data=data1,palette='winter')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0115385",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Residual Sugar zeigt viele Ausreißer in Bezug auf die Zielvariable. Ein Teil der Ausreißer wird nun entfernt, um ein genaueres Datenbild zu erhalten."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "92452f22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.36300000000001"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q2 = data1['residual sugar'].quantile(0.99)\n",
+    "q2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "id": "de6fef3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='residual sugar', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 214,
+     "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": [
+    "data2= data1[data1['residual sugar']<q2]\n",
+    "sns.histplot(data2['residual sugar'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "693be7ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='quality', ylabel='residual sugar'>"
+      ]
+     },
+     "execution_count": 215,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 7))\n",
+    "sns.boxplot(x='quality',y='residual sugar', data=data2,palette='winter')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e708e5a",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Es sind noch Ausreißer zu sehen, die aber nach einem ersten Durchlauf des Modells noch verändert werden können"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6afae003",
+   "metadata": {},
+   "source": [
+    "Chlorides\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "id": "5c67dc4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='chlorides', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 216,
+     "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": [
+    "sns.histplot(data2['chlorides'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "id": "3d3c8dfe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.226"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q3 = data2['chlorides'].quantile(0.98)\n",
+    "q3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "id": "842281bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='chlorides', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 218,
+     "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": [
+    "data3= data2[data2['chlorides']<q3]\n",
+    "sns.histplot(data3['chlorides'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4873fb90",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Free Sulfur Dioxide"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "id": "b8b59992",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='free sulfur dioxide', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 219,
+     "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": [
+    "sns.histplot(data3['free sulfur dioxide'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "e4b81e94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='quality', ylabel='free sulfur dioxide'>"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 7))\n",
+    "sns.boxplot(x='quality',y='free sulfur dioxide', data=data3,palette='winter')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "id": "217e3b60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "47.700000000000045"
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q4 = data3['free sulfur dioxide'].quantile(0.99)\n",
+    "q4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "id": "9380e945",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='free sulfur dioxide', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 222,
+     "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": [
+    "data4= data3[data3['free sulfur dioxide']<q4]\n",
+    "sns.histplot(data4['free sulfur dioxide'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "id": "a34fc87f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='quality', ylabel='free sulfur dioxide'>"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 7))\n",
+    "sns.boxplot(x='quality',y='free sulfur dioxide', data=data4,palette='winter')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93ba5bac",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Zuvor erkannte Ausreißer der Spalte 'free sulfur dioxide' entfernt und ein ausgeglichenes Bild zu unserer Zielvariablen geschaffen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0dea854",
+   "metadata": {},
+   "source": [
+    "Total Sulfur Dioxide"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "id": "67e43a52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='total sulfur dioxide', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 224,
+     "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": [
+    "sns.histplot(data4['total sulfur dioxide'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "id": "029dbff6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='quality', ylabel='total sulfur dioxide'>"
+      ]
+     },
+     "execution_count": 225,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 7))\n",
+    "sns.boxplot(x='quality',y='total sulfur dioxide', data=data4,palette='winter')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "id": "ad07e876",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "143.8599999999999"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q5 = data4['total sulfur dioxide'].quantile(0.99)\n",
+    "q5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "id": "df05ec3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='total sulfur dioxide', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYWklEQVR4nO3de5QmdX3n8feHixgvEZCRxblk0IwmxI2D2xpE5RhJFIgr6rpc4lFU4mACWe8G9Bw15niOWa9xXdFRCLAHEQWJxKBCCBGVgA6IXERWUHBmGIYJ3nWDDPPdP6q6eGh6Znp65nnq6en365zndNWvqp7+ds10f576VdWvUlVIkgSwS98FSJLGh6EgSeoYCpKkjqEgSeoYCpKkzm59F7A99tlnn1q6dGnfZUjSnHL11Vf/e1UtmG7ZnA6FpUuXsmrVqr7LkKQ5Jcntm1tm95EkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMozMLCxUtIMqvXwsVL+i5fkjZrTg9z0Zc71qzm6I9fMattzz3h4B1cjSTtOB4pSJI6QwuFJIuTXJbkO0luTPLatn3vJJck+V77da+2PUk+nOSWJNclecqwapMkTW+YRwobgTdW1QHAQcCJSQ4ATgYuraplwKXtPMDhwLL2tQI4dYi1SZKmMbRQqKp1VXVNO/1z4CZgIXAkcGa72pnAC9vpI4GzqnElsGeS/YZVnyTpwUZyTiHJUuBA4Cpg36pa1y66E9i3nV4IrB7YbE3bNvW9ViRZlWTVhg0bhle0JM1DQw+FJI8AzgdeV1U/G1xWVQXUtrxfVa2sqomqmliwYNoHB0mSZmmooZBkd5pAOLuqPtc2r5/sFmq/3tW2rwUWD2y+qG2TJI3IMK8+CnAacFNVfWBg0YXAce30ccDnB9pf3l6FdBDw04FuJknSCAzz5rVnAC8Drk9ybdv2VuA9wGeSHA/cDhzVLrsIOAK4BfgV8Moh1iZJmsbQQqGqvgZkM4sPnWb9Ak4cVj2SpK3zjmZJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1hvk4ztOT3JXkhoG2c5Nc275um3wiW5KlSf7fwLKPDasuSdLmDfNxnGcAHwHOmmyoqqMnp5O8H/jpwPq3VtXyIdYzHnbZjebx1dtm19334L5775nVt3zsosWsXf3DWW0raX4Z5uM4L0+ydLplaf4qHgU8Z1jff2xt2sjRH79imzc794SDZ7Xd5LaSNBN9nVN4FrC+qr430LZ/km8l+UqSZ21uwyQrkqxKsmrDhg3Dr1SS5pG+QuFY4JyB+XXAkqo6EHgD8KkkvzndhlW1sqomqmpiwYIFIyhVkuaPkYdCkt2AFwPnTrZV1T1VdXc7fTVwK/CEUdcmSfNdH0cKfwR8t6rWTDYkWZBk13b6ccAy4Ps91CZJ89owL0k9B/g34IlJ1iQ5vl10DA/sOgI4BLiuvUT1POA1VfWjYdUmSZreMK8+OnYz7a+Ypu184Pxh1SJJmhnvaJYkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVJnmE9eOz3JXUluGGh7Z5K1Sa5tX0cMLDslyS1Jbk7yvGHVJUnavGEeKZwBHDZN+werann7ugggyQE0j+n8vXabj04+s1mSNDpDC4WquhyY6XOWjwQ+XVX3VNUPgFuApw2rNknS9Po4p3BSkuva7qW92raFwOqBdda0bQ+SZEWSVUlWbdiwYdi1StK8MupQOBV4PLAcWAe8f1vfoKpWVtVEVU0sWLBgB5cnSfPbSEOhqtZX1X1VtQn4BPd3Ea0FFg+suqhtkySN0EhDIcl+A7MvAiavTLoQOCbJHkn2B5YB3xhlbZIk2G1Yb5zkHODZwD5J1gDvAJ6dZDlQwG3ACQBVdWOSzwDfATYCJ1bVfcOqTZI0vaGFQlUdO03zaVtY/93Au4dVjyRp67yjWZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRTmg112I8k2vxYuXtJ35ZJGbJhPXjsdeD5wV1U9qW17L/BfgV8DtwKvrKqfJFkK3ATc3G5+ZVW9Zli1zTubNnL0x6/Y5s3O/fNDSDKrb/nYRYtZu/qHs9pWUn+GFgrAGcBHgLMG2i4BTqmqjUn+FjgF+Kt22a1VtXyI9WhbzTJMAM494eAdXIykURha91FVXQ78aErbxVW1sZ29Elg0rO8vSdp2fZ5TeBXwxYH5/ZN8K8lXkjxrcxslWZFkVZJVGzZsGH6VkjSP9BIKSd4GbATObpvWAUuq6kDgDcCnkvzmdNtW1cqqmqiqiQULFoymYEmaJ0YeCkleQXMC+qVVVQBVdU9V3d1OX01zEvoJo65Nkua7kYZCksOAtwAvqKpfDbQvSLJrO/04YBnw/VHWJkka7iWp5wDPBvZJsgZ4B83VRnsAl7SXOk5eenoI8K4k9wKbgNdU1Y+mfWNJ0tDMKBSSPKOqvr61tkFVdew0zadtZt3zgfNnUoskaXhm2n30v2bYJkmaw7Z4pJDk6cDBwIIkbxhY9JvArsMsTJI0elvrPnoI8Ih2vUcOtP8MeMmwipIk9WOLoVBVXwG+kuSMqrp9RDVJknoy06uP9kiyElg6uE1VPWcYRUmS+jHTUPgs8DHgk8B9wytHktSnmYbCxqo6daiVSJJ6N9NLUv8xyV8k2S/J3pOvoVYmSRq5mR4pHNd+ffNAWwGP27HlSJL6NKNQqKr9h11IHxYuXsIda1b3XYYkjY2ZDnPx8unaq+qs6drnijvWrJ7dYyp9qpikndRMu4+eOjD9UOBQ4Boe+KhNSdIcN9Puo78cnE+yJ/DpYRQkSerPbJ+n8EtgpzzPIEnz2UzPKfwjzdVG0AyE97vAZ4ZVlCSpHzM9p/C+gemNwO1VtWYI9UiSejSj7qN2YLzv0oyUuhfw65lsl+T0JHcluWGgbe8klyT5Xvt1r7Y9ST6c5JYk1yV5yrb/OJKk7TGjUEhyFPAN4L8DRwFXJZnJ0NlnAIdNaTsZuLSqlgGXtvMAh9M8m3kZsAJwWA1JGrGZdh+9DXhqVd0FkGQB8M/AeVvaqKouT7J0SvORNM9uBjgT+Ffgr9r2s6qqgCuT7Jlkv6paN8MaNU522Y32Odzb5LGLFrN29Q+HUJCkmZhpKOwyGQitu5n9lUv7DvyhvxPYt51eCAzeXrymbXtAKCRZQXMkwZIlS2ZZgoZu00ZvDJTmoJmGwpeSfBk4p50/Grhoe795VVWS2vqaD9hmJbASYGJiYpu2lSRt2dae0fzbNJ/s35zkxcAz20X/Bpw9y++5frJbKMl+wOQRyFpg8cB6i9o2SdKIbK0L6EM0z2Omqj5XVW+oqjcAF7TLZuNC7h919Tjg8wPtL2+vQjoI+KnnEyRptLbWfbRvVV0/tbGqrp/mBPKDJDmH5qTyPknWAO8A3gN8JsnxwO00VzNB0x11BHAL8CvglTP8GSRJO8jWQmHPLSz7ja29eVUdu5lFh06zbgEnbu09JUnDs7Xuo1VJXj21McmfAVcPpyRJUl+2dqTwOuCCJC/l/hCYAB4CvGiIdUmSerDFUKiq9cDBSf4QeFLb/E9V9S9Dr0ySNHIzfZ7CZcBlQ65FktSz2d6VLEnaCRkKkqSOoaDx0g6kt62vhYsdB0vaEWY69pE0Gg6kJ/XKIwVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1DAVJUsdQkCR1Rn7zWpInAucOND0OeDvNA31eDWxo299aVReNtjpJmt9GHgpVdTOwHCDJrsBammc+vxL4YFW9b9Q1SZIafXcfHQrcWlW391yHJIn+Q+EY4JyB+ZOSXJfk9CR7TbdBkhVJViVZtWHDhulWkSTNUm+hkOQhwAuAz7ZNpwKPp+laWge8f7rtqmplVU1U1cSCBQtGUaokzRt9HikcDlzTPvKTqlpfVfdV1SbgE8DTeqxNkualPkPhWAa6jpLsN7DsRcANI69Ikua5Xp6nkOThwB8DJww0/88ky4ECbpuyTJI0Ar2EQlX9Enj0lLaX9VGLJOl+fV99JEkaI4aCJKljKEiSOoaCJKljKGjeW7h4CUm2+bVw8ZK+S5d2uF6uPpLGyR1rVnP0x6/Y5u3OPeHgIVQj9csjBUlSxyMF7Rx22Y0kfVchzXmGgnYOmzbOqgsI7AaSBtl9JEnqGAqSpI6hIEnqGAqSpI6hIEnqGAqSpI6hIEnq9HafQpLbgJ8D9wEbq2oiyd7AucBSmqevHVVVP+6rRkmab/o+UvjDqlpeVRPt/MnApVW1DLi0nZckjUjfoTDVkcCZ7fSZwAv7K0WS5p8+Q6GAi5NcnWRF27ZvVa1rp+8E9u2nNEman/oc++iZVbU2yWOAS5J8d3BhVVWSmrpRGyArAJYscTx7SdqRejtSqKq17de7gAuApwHrk+wH0H69a5rtVlbVRFVNLFiwYJQlS9JOr5dQSPLwJI+cnAaeC9wAXAgc1652HPD5PuqTxtVsnxLnk+I0U311H+0LXNCOf78b8Kmq+lKSbwKfSXI8cDtwVE/1SWNptk+JA4cI18z0EgpV9X3gydO03w0cOvqKJEkwfpekSpJ6ZChIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgjRbu+zmjWTa6fQ59pE0t23a6I1k2ul4pCBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgK0nwxy5vtvNFufvHmNWm+mOXNdt5oN7+M/EghyeIklyX5TpIbk7y2bX9nkrVJrm1fR4y6Nkma7/o4UtgIvLGqrknySODqJJe0yz5YVe/roSZJm9N2O83GrrvvwX333rPN2z120WLWrv7hrL6nts/IQ6Gq1gHr2umfJ7kJWDjqOiTN0HaO8WSX1dzS64nmJEuBA4Gr2qaTklyX5PQke21mmxVJViVZtWHDhlGVKu1YszzpKw1bbyeakzwCOB94XVX9LMmpwN8A1X59P/CqqdtV1UpgJcDExESNrmJpB/Kkr8ZUL0cKSXanCYSzq+pzAFW1vqruq6pNwCeAp/VRmyTNZ31cfRTgNOCmqvrAQPt+A6u9CLhh1LVJ0nzXR/fRM4CXAdcnubZteytwbJLlNN1HtwEn9FCbJM1rfVx99DVgujNmF426FknSAznMhSSpYyhIkjqGgiSpYyhI2qksXLzE0WC3g6OkStqp3LFmtTcGbgePFCRJHY8UJI2f7RiZVdvHUJA0frZzZNZRW7h4CXesWb3N243jEOGGgiTBdh+d7CznMQwFSYI5d3QyLJ5oliR1DAVJ6sssH7Y0zPsq7D6SpL6MYZeVRwqSpI6hIEnqGAqSpM7YhUKSw5LcnOSWJCf3XY8kzSdjFQpJdgX+N3A4cADNIzoP6LcqSZo/xioUgKcBt1TV96vq18CngSN7rkmS5o1UVd81dJK8BDisqv6snX8Z8AdVddLAOiuAFe3sE4GbgX2Afx9xudtrrtU81+qFuVez9Q7fXKt5WPX+VlUtmG7BnLtPoapWAisH25KsqqqJnkqalblW81yrF+ZezdY7fHOt5j7qHbfuo7XA4oH5RW2bJGkExi0UvgksS7J/kocAxwAX9lyTJM0bY9V9VFUbk5wEfBnYFTi9qm6cwaYrt77K2JlrNc+1emHu1Wy9wzfXah55vWN1olmS1K9x6z6SJPXIUJAkdeZ8KIz7sBhJFie5LMl3ktyY5LVt+95JLknyvfbrXn3XOijJrkm+leQL7fz+Sa5q9/O57YUAYyPJnknOS/LdJDclefo47+Mkr2//P9yQ5JwkDx23fZzk9CR3JblhoG3afZrGh9var0vylDGp973t/4nrklyQZM+BZae09d6c5HmjrndzNQ8se2OSSrJPOz+SfTynQ2GODIuxEXhjVR0AHASc2NZ4MnBpVS0DLm3nx8lrgZsG5v8W+GBV/TbwY+D4XqravL8DvlRVvwM8mab2sdzHSRYC/wOYqKon0VxUcQzjt4/PAA6b0ra5fXo4sKx9rQBOHVGNg87gwfVeAjypqn4f+L/AKQDt7+AxwO+123y0/Xsyamfw4JpJshh4LvDDgeaR7OM5HQrMgWExqmpdVV3TTv+c5o/VQpo6z2xXOxN4YS8FTiPJIuBPgE+28wGeA5zXrjJu9T4KOAQ4DaCqfl1VP2GM9zHNlX+/kWQ34GHAOsZsH1fV5cCPpjRvbp8eCZxVjSuBPZPsN5JCW9PVW1UXV9XGdvZKmnufoKn301V1T1X9ALiF5u/JSG1mHwN8EHgLMHgl0Ej28VwPhYXA6oH5NW3bWEqyFDgQuArYt6rWtYvuBPbtq65pfIjmP+Smdv7RwE8GfrnGbT/vD2wA/r7t8vpkkoczpvu4qtYC76P5FLgO+ClwNeO9jydtbp/Ohd/FVwFfbKfHtt4kRwJrq+rbUxaNpOa5HgpzRpJHAOcDr6uqnw0uq+a64LG4NjjJ84G7qurqvmvZBrsBTwFOraoDgV8ypatozPbxXjSf+vYHHgs8nGm6EMbdOO3TrUnyNpqu3LP7rmVLkjwMeCvw9r5qmOuhMCeGxUiyO00gnF1Vn2ub108e+rVf7+qrvimeAbwgyW003XHPoemv37Pt6oDx289rgDVVdVU7fx5NSIzrPv4j4AdVtaGq7gU+R7Pfx3kfT9rcPh3b38UkrwCeD7y07r8xa1zrfTzNh4Vvt7+Di4BrkvwnRlTzXA+FsR8Wo+2PPw24qao+MLDoQuC4dvo44POjrm06VXVKVS2qqqU0+/NfquqlwGXAS9rVxqZegKq6E1id5Ilt06HAdxjTfUzTbXRQkoe1/z8m6x3bfTxgc/v0QuDl7RUyBwE/Hehm6k2Sw2i6Ql9QVb8aWHQhcEySPZLsT3Py9ht91Dioqq6vqsdU1dL2d3AN8JT2//ho9nFVzekXcATNVQW3Am/ru55p6nsmzSH2dcC17esImn76S4HvAf8M7N13rdPU/mzgC+3042h+aW4BPgvs0Xd9U2pdDqxq9/M/AHuN8z4G/hr4LnAD8H+APcZtHwPn0JzzuJfmj9Pxm9unQGiuBLwVuJ7myqpxqPcWmn74yd+9jw2s/7a23puBw8dlH09Zfhuwzyj3scNcSJI6c737SJK0AxkKkqSOoSBJ6hgKkqSOoSBJ6hgKGmvt6Kd/MYP1lib50xmu96ARKWdR1zuTvKmd/p0k17ZDbDx+e9+7fc/bBkbHvGKW7/GaJC+fpn2H7APtnAwFjbs9ga2GArAU2GooDMkLgfOq6sCqunUmGwzcubxVVXXwbIqqqo9V1Vmz2Vbzl6Ggcfce4PHtJ/H3tndzvjfNcwiuT3L0wHrPatd7fftp+KtJrmlfW/zDmmS/JJe329+Q5Flt+y8G1nlJkjOmbHcE8Drgz9M8N+MBn8KTvCnJO9vpf03yoSSraIYmH3yfRye5OM0zFj5Jc6PS5LJftF+n/dmT/F2St7fTz2t/jl2mHM38lyTfTvJt4MSB9961fc9vphmj/4St/otopzbjTytST06mGQ9/OUCS/0Zz9/KTgX2Abya5vF3vTVX1/Ha9hwF/XFX/kWQZzZ2jE1v4Pn8KfLmq3p1mXP2HzaS4qrooyceAX1TV+9KMhLslD6mq6ep4B/C1qnpXkj9h+mcpvJjpf/ZT2umvAh8GjqiqTc0IGp2/B06qqsuTvHeg/Xia4RKemmQP4OtJLq5mOGnNQ4aC5ppnAudU1X00g7N9BXgq8LMp6+0OfCTJcuA+4Albed9vAqenGbzwH6rq2h1a9f3O3Uz7ITR/9Kmqf0ry42nWmfZnr6oLk7wauBx4/dQurDRPG9uzmrH7oRlW4/B2+rnA7yeZHHPpUTTjABkK85ShoJ3V64H1NJ+qdwH+Y0srt5+gD6F5uNAZST7Q9scPjgPz0Bl83408sFt26ja/nMF7zMZ/Bu6mGYp7WwT4y6r68o4vSXOR5xQ07n4OPHJg/qvA0W1f+AKaT9jfmGa9RwHrqmoT8DKaR15uVpLfAtZX1Sdonjg3+fzb9Ul+N8kuwItmUO964DHtOYI9aIZsnonLaU+UJzmcZkC/qab92dva30jzAKfDk/zB4EbVPIXuJ0me2Ta9dGDxl2nOh+zefu8npHlAkeYpjxQ01qrq7iRfb0/efpFmGOSnA9+m+RT/lqq6M8ndwH3tidQzgI8C57eXZH6JrX9Cfzbw5iT3Ar8AJi/lPBn4As2T3VYBj9hKvfcmeRdNUK2lGQl1Jv4aOCfJjcAVPPDZvJMuYMrPThNCl9CcT7kjyfE0RzpPnbLtK2m6xwq4eKD9kzRXbl2T5iTEBsbrsaUaMUdJlSR17D6SJHUMBUlSx1CQJHUMBUlSx1CQJHUMBUlSx1CQJHX+P/aH46o8zpPYAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data5 = data4[data4['total sulfur dioxide']<q5]\n",
+    "sns.histplot(data5['total sulfur dioxide'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "190d3a3e",
+   "metadata": {},
+   "source": [
+    "Sulphates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "id": "00f4067e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='sulphates', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 228,
+     "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": [
+    "sns.histplot(data5['sulphates'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "id": "f4ff4b54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.1602"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q6 = data5['sulphates'].quantile(0.99)\n",
+    "q6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "id": "0a56a6e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='sulphates', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 230,
+     "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": [
+    "data6 = data5[data5['sulphates']<q6]\n",
+    "sns.histplot(data6['sulphates'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "id": "c66651e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='alcohol', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 231,
+     "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": [
+    "sns.histplot(data6['alcohol'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "id": "89d782e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='quality', ylabel='alcohol'>"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 7))\n",
+    "sns.boxplot(x='quality',y='alcohol', data=data6,palette='winter')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d873c6d0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "je höher der Alkoholgehalt des Weins ist, desto besser wird er von den Fachleuten bewertet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1d0a1a6",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Alle Merkmale wurden im ersten Schritt bereinigt und können nun für das Modell verwendet werden.  \n",
+    "ie Zielvariable Qualität wird in gut 1 und schlecht 0 aufgeteilt.\n",
+    "Dies ändert das Problem von einer Regressionsaufgabe zu einer (binären) Kategorisierungsaufgabe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65eaa0b2",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Umwandlung der Zielvariablen 'Qualität' in eine binäre Klassifikation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "id": "d6f1bc7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def quality_range(quality):\n",
+    "    if quality <= 5:\n",
+    "        return 0\n",
+    "    elif quality >=6:\n",
+    "        return 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "id": "dc72e74d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make a (deep) copy, to make sure pandas doesn't complain and makes the correct thing\n",
+    "# see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+    "data6 = data6.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "id": "5cf3572f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data6['quality_range'] = data6['quality'].apply(quality_range)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "id": "4d46e6e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>quality_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>8.331267</td>\n",
+       "      <td>0.520044</td>\n",
+       "      <td>0.266961</td>\n",
+       "      <td>2.427763</td>\n",
+       "      <td>0.081051</td>\n",
+       "      <td>15.176550</td>\n",
+       "      <td>43.828841</td>\n",
+       "      <td>0.996705</td>\n",
+       "      <td>3.316846</td>\n",
+       "      <td>0.643652</td>\n",
+       "      <td>10.455492</td>\n",
+       "      <td>5.661725</td>\n",
+       "      <td>0.546496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.761040</td>\n",
+       "      <td>0.165732</td>\n",
+       "      <td>0.192996</td>\n",
+       "      <td>0.975109</td>\n",
+       "      <td>0.020813</td>\n",
+       "      <td>9.343802</td>\n",
+       "      <td>29.133109</td>\n",
+       "      <td>0.001854</td>\n",
+       "      <td>0.149700</td>\n",
+       "      <td>0.129927</td>\n",
+       "      <td>1.071880</td>\n",
+       "      <td>0.800449</td>\n",
+       "      <td>0.498001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.600000</td>\n",
+       "      <td>0.120000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.012000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.990070</td>\n",
+       "      <td>2.860000</td>\n",
+       "      <td>0.330000</td>\n",
+       "      <td>8.400000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>7.100000</td>\n",
+       "      <td>0.390000</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>0.070000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.995570</td>\n",
+       "      <td>3.220000</td>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>9.500000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>0.520000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>0.079000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.996700</td>\n",
+       "      <td>3.315000</td>\n",
+       "      <td>0.620000</td>\n",
+       "      <td>10.200000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>9.225000</td>\n",
+       "      <td>0.630000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>0.089000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>0.997800</td>\n",
+       "      <td>3.400000</td>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>11.100000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>15.900000</td>\n",
+       "      <td>1.010000</td>\n",
+       "      <td>0.790000</td>\n",
+       "      <td>8.300000</td>\n",
+       "      <td>0.222000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>143.000000</td>\n",
+       "      <td>1.003200</td>\n",
+       "      <td>4.010000</td>\n",
+       "      <td>1.160000</td>\n",
+       "      <td>14.900000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
+       "count    1484.000000       1484.000000  1484.000000     1484.000000   \n",
+       "mean        8.331267          0.520044     0.266961        2.427763   \n",
+       "std         1.761040          0.165732     0.192996        0.975109   \n",
+       "min         4.600000          0.120000     0.000000        0.900000   \n",
+       "25%         7.100000          0.390000     0.090000        1.900000   \n",
+       "50%         7.900000          0.520000     0.250000        2.200000   \n",
+       "75%         9.225000          0.630000     0.420000        2.600000   \n",
+       "max        15.900000          1.010000     0.790000        8.300000   \n",
+       "\n",
+       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
+       "count  1484.000000          1484.000000           1484.000000  1484.000000   \n",
+       "mean      0.081051            15.176550             43.828841     0.996705   \n",
+       "std       0.020813             9.343802             29.133109     0.001854   \n",
+       "min       0.012000             1.000000              6.000000     0.990070   \n",
+       "25%       0.070000             7.000000             21.000000     0.995570   \n",
+       "50%       0.079000            13.000000             37.000000     0.996700   \n",
+       "75%       0.089000            21.000000             59.000000     0.997800   \n",
+       "max       0.222000            47.000000            143.000000     1.003200   \n",
+       "\n",
+       "                pH    sulphates      alcohol      quality  quality_range  \n",
+       "count  1484.000000  1484.000000  1484.000000  1484.000000    1484.000000  \n",
+       "mean      3.316846     0.643652    10.455492     5.661725       0.546496  \n",
+       "std       0.149700     0.129927     1.071880     0.800449       0.498001  \n",
+       "min       2.860000     0.330000     8.400000     3.000000       0.000000  \n",
+       "25%       3.220000     0.550000     9.500000     5.000000       0.000000  \n",
+       "50%       3.315000     0.620000    10.200000     6.000000       1.000000  \n",
+       "75%       3.400000     0.720000    11.100000     6.000000       1.000000  \n",
+       "max       4.010000     1.160000    14.900000     8.000000       1.000000  "
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data6.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "id": "ec301957",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>quality_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.9968</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.9970</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.9980</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0            7.4              0.70         0.00             1.9      0.076   \n",
+       "1            7.8              0.88         0.00             2.6      0.098   \n",
+       "2            7.8              0.76         0.04             2.3      0.092   \n",
+       "3           11.2              0.28         0.56             1.9      0.075   \n",
+       "4            7.4              0.70         0.00             1.9      0.076   \n",
+       "\n",
+       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "\n",
+       "   alcohol  quality  quality_range  \n",
+       "0      9.4        5              0  \n",
+       "1      9.8        5              0  \n",
+       "2      9.8        5              0  \n",
+       "3      9.8        6              1  \n",
+       "4      9.4        5              0  "
+      ]
+     },
+     "execution_count": 237,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data6.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "515ae823",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Mit der Methode quality_range() haben wir die Werte der Spalte \"quality\" aufgeteilt. Alle Werte kleiner und gleich 5 erhielten den Wert 0 (, was auf eine schlechtere als durchschnittliche Qualität hinweist). Alle Werte von 6 und größer erhielten den Wert 1 (für überdurchschnittliche Qualität)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "id": "6b0faec0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='quality_range', ylabel='count'>"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize =(12, 8))\n",
+    "sns.countplot(data=data6, x=\"quality_range\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6697a0ab",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Die Zielvariable ist ausgeglichen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "id": "b562e63c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data6.drop('quality', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "id": "ae7ec03a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "      <td>1484.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>8.331267</td>\n",
+       "      <td>0.520044</td>\n",
+       "      <td>0.266961</td>\n",
+       "      <td>2.427763</td>\n",
+       "      <td>0.081051</td>\n",
+       "      <td>15.176550</td>\n",
+       "      <td>43.828841</td>\n",
+       "      <td>0.996705</td>\n",
+       "      <td>3.316846</td>\n",
+       "      <td>0.643652</td>\n",
+       "      <td>10.455492</td>\n",
+       "      <td>0.546496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.761040</td>\n",
+       "      <td>0.165732</td>\n",
+       "      <td>0.192996</td>\n",
+       "      <td>0.975109</td>\n",
+       "      <td>0.020813</td>\n",
+       "      <td>9.343802</td>\n",
+       "      <td>29.133109</td>\n",
+       "      <td>0.001854</td>\n",
+       "      <td>0.149700</td>\n",
+       "      <td>0.129927</td>\n",
+       "      <td>1.071880</td>\n",
+       "      <td>0.498001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.600000</td>\n",
+       "      <td>0.120000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.012000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>0.990070</td>\n",
+       "      <td>2.860000</td>\n",
+       "      <td>0.330000</td>\n",
+       "      <td>8.400000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>7.100000</td>\n",
+       "      <td>0.390000</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>1.900000</td>\n",
+       "      <td>0.070000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.995570</td>\n",
+       "      <td>3.220000</td>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>9.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>0.520000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>2.200000</td>\n",
+       "      <td>0.079000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.996700</td>\n",
+       "      <td>3.315000</td>\n",
+       "      <td>0.620000</td>\n",
+       "      <td>10.200000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>9.225000</td>\n",
+       "      <td>0.630000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>2.600000</td>\n",
+       "      <td>0.089000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>59.000000</td>\n",
+       "      <td>0.997800</td>\n",
+       "      <td>3.400000</td>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>11.100000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>15.900000</td>\n",
+       "      <td>1.010000</td>\n",
+       "      <td>0.790000</td>\n",
+       "      <td>8.300000</td>\n",
+       "      <td>0.222000</td>\n",
+       "      <td>47.000000</td>\n",
+       "      <td>143.000000</td>\n",
+       "      <td>1.003200</td>\n",
+       "      <td>4.010000</td>\n",
+       "      <td>1.160000</td>\n",
+       "      <td>14.900000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
+       "count    1484.000000       1484.000000  1484.000000     1484.000000   \n",
+       "mean        8.331267          0.520044     0.266961        2.427763   \n",
+       "std         1.761040          0.165732     0.192996        0.975109   \n",
+       "min         4.600000          0.120000     0.000000        0.900000   \n",
+       "25%         7.100000          0.390000     0.090000        1.900000   \n",
+       "50%         7.900000          0.520000     0.250000        2.200000   \n",
+       "75%         9.225000          0.630000     0.420000        2.600000   \n",
+       "max        15.900000          1.010000     0.790000        8.300000   \n",
+       "\n",
+       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
+       "count  1484.000000          1484.000000           1484.000000  1484.000000   \n",
+       "mean      0.081051            15.176550             43.828841     0.996705   \n",
+       "std       0.020813             9.343802             29.133109     0.001854   \n",
+       "min       0.012000             1.000000              6.000000     0.990070   \n",
+       "25%       0.070000             7.000000             21.000000     0.995570   \n",
+       "50%       0.079000            13.000000             37.000000     0.996700   \n",
+       "75%       0.089000            21.000000             59.000000     0.997800   \n",
+       "max       0.222000            47.000000            143.000000     1.003200   \n",
+       "\n",
+       "                pH    sulphates      alcohol  quality_range  \n",
+       "count  1484.000000  1484.000000  1484.000000    1484.000000  \n",
+       "mean      3.316846     0.643652    10.455492       0.546496  \n",
+       "std       0.149700     0.129927     1.071880       0.498001  \n",
+       "min       2.860000     0.330000     8.400000       0.000000  \n",
+       "25%       3.220000     0.550000     9.500000       0.000000  \n",
+       "50%       3.315000     0.620000    10.200000       1.000000  \n",
+       "75%       3.400000     0.720000    11.100000       1.000000  \n",
+       "max       4.010000     1.160000    14.900000       1.000000  "
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data6.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4879c2cc",
+   "metadata": {},
+   "source": [
+    "## 3.3. Test for Multicollinearity  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "id": "0a0d7ec5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "feature_corr = data6.corr()\n",
+    "sns.heatmap(feature_corr, annot=True, cmap='coolwarm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "id": "b71481de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data6.drop('fixed acidity', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6b2a1ad",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "der feste Säuregehalt entfällt, da er stark mit dem pH-Wert korreliert"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "id": "07b208a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "feature_corr = data6.corr()\n",
+    "sns.heatmap(feature_corr, annot=True, cmap='coolwarm')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "957639bc",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 3.4. Test- und Trainingsdaten erstellen "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "id": "6adf3854",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(data6.drop('quality_range', axis=1),data6['quality_range'], test_size=0.2, random_state=365)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "433012d9",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# 4. Modellierung und Bewertung"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37f38cd9",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 4.1. Daten skalieren und transformieren"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "id": "6d81cb02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "scaler.fit(x_train)\n",
+    "\n",
+    "X_train = scaler.transform(x_train)\n",
+    "X_test = scaler.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d34d9e8",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## 4.1 Modellbildung logistsiche Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "id": "8954d858",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_logistic_regression = LogisticRegression(random_state=0, C=1e8, max_iter=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "id": "15371bca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train LogistModel\n",
+    "model_logistic_regression.fit(x_train,y_train)\n",
+    "prediction_test = model_logistic_regression.predict(x_test)\n",
+    "prediction_train = model_logistic_regression.predict(x_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f994d16c",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "### Evaluierung"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "id": "1f61d6d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy on the Test dataset: 0.6936026936026936\n"
+     ]
+    }
+   ],
+   "source": [
+    "acc = metrics.accuracy_score(y_test, prediction_test)\n",
+    "print('Accuracy on the Test dataset: {}'.format(acc))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "id": "61ea2245",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training dataset:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.74      0.74      0.74       534\n",
+      "           1       0.79      0.79      0.79       653\n",
+      "\n",
+      "    accuracy                           0.76      1187\n",
+      "   macro avg       0.76      0.76      0.76      1187\n",
+      "weighted avg       0.76      0.76      0.76      1187\n",
+      "\n",
+      "Test dataset:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.67      0.68      0.67       139\n",
+      "           1       0.71      0.71      0.71       158\n",
+      "\n",
+      "    accuracy                           0.69       297\n",
+      "   macro avg       0.69      0.69      0.69       297\n",
+      "weighted avg       0.69      0.69      0.69       297\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Trainingsdaten:\")\n",
+    "print(classification_report(y_train,prediction_train))\n",
+    "print(\"Testdaten:\")\n",
+    "print(classification_report(y_test,prediction_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "id": "d11bf032",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 42.0, 'Predicted label')"
+      ]
+     },
+     "execution_count": 250,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Confusion-Matrix Test dataset\n",
+    "cm = confusion_matrix(y_test,prediction_test)\n",
+    "df_cm = pd.DataFrame(cm, index=['worse wine','better wine'], columns=['worse wine', 'better wine'],)\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": 251,
+   "id": "dfff8b9e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True Negatives: 94\n",
+      "False Positives: 45\n",
+      "False Negatives: 46\n",
+      "True Positives: 112\n",
+      "Recall: 0.7088607594936709\n",
+      "Precision: 0.7133757961783439\n"
+     ]
+    }
+   ],
+   "source": [
+    "# confusion Matrix \n",
+    "tn, fp, fn, tp = cm.ravel()\n",
+    "recall = tp/(fn+tp)\n",
+    "precision = tp/(tp+fp)\n",
+    "print(\"True Negatives: \" + str(tn))\n",
+    "print(\"False Positives: \" + str(fp))\n",
+    "print(\"False Negatives: \" + str(fn))\n",
+    "print(\"True Positives: \" + str(tp))\n",
+    "print(\"Recall: \" + str(recall))\n",
+    "print(\"Precision: \" + str(precision))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "id": "95a65252",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1ffeab0a280>]"
+      ]
+     },
+     "execution_count": 252,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ROC-Kurve, AUC\n",
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "ax.set_title('ROC Curve')\n",
+    "plot = metrics.plot_roc_curve(model_logistic_regression, x_test, y_test, ax=ax)\n",
+    "ax.plot([0,1], [0,1], '--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "id": "59f03746",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sulphates               5.210340\n",
+       "alcohol                 0.913617\n",
+       "free sulfur dioxide     0.019869\n",
+       "total sulfur dioxide   -0.018066\n",
+       "residual sugar         -0.177201\n",
+       "citric acid            -1.293662\n",
+       "pH                     -1.618701\n",
+       "density                -1.921585\n",
+       "volatile acidity       -3.134072\n",
+       "chlorides              -3.875051\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 253,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print out Regression parameters (weights and biases)\n",
+    "weights = pd.Series(model_logistic_regression.coef_[0], index=x_train.columns.values)\n",
+    "weights.sort_values(ascending = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "id": "1f6e4d6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# grafically ilustrate the most important features of a good wine \n",
+    "weights = pd.Series(model_logistic_regression.coef_[0], index=x_train.columns.values)\n",
+    "print (weights.sort_values(ascending = False)[:7].plot(kind='bar'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4d337d5",
+   "metadata": {},
+   "source": [
+    "## 3. Lineare Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "id": "d9ec9a06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data6.drop(['quality_range'], axis=1)\n",
+    "Y = data6['quality_range']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "id": "e1cae716",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=365)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "id": "0c5f271a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_linear_regression = LinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "id": "a8d5af1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_linear_regression.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "id": "efd76e2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-10.073788720357635\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(model_linear_regression.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "id": "63eb079f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Coefficient</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <td>-0.560376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>citric acid</th>\n",
+       "      <td>-0.234547</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>residual sugar</th>\n",
+       "      <td>-0.035414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>chlorides</th>\n",
+       "      <td>-0.557458</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <td>0.004425</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <td>-0.003436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>density</th>\n",
+       "      <td>9.804198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>pH</th>\n",
+       "      <td>-0.254166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sulphates</th>\n",
+       "      <td>0.843459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alcohol</th>\n",
+       "      <td>0.164131</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Coefficient\n",
+       "volatile acidity        -0.560376\n",
+       "citric acid             -0.234547\n",
+       "residual sugar          -0.035414\n",
+       "chlorides               -0.557458\n",
+       "free sulfur dioxide      0.004425\n",
+       "total sulfur dioxide    -0.003436\n",
+       "density                  9.804198\n",
+       "pH                      -0.254166\n",
+       "sulphates                0.843459\n",
+       "alcohol                  0.164131"
+      ]
+     },
+     "execution_count": 260,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coeff_data = pd.DataFrame(model_linear_regression.coef_, X.columns, columns=['Coefficient'])\n",
+    "coeff_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "id": "55af5820",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model_linear_regression.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "id": "4d3dd3f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1ffeabd7a60>"
+      ]
+     },
+     "execution_count": 262,
+     "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": [
+    "plt.scatter(y_test,predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "id": "39443494",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='quality_range', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 263,
+     "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": [
+    "sns.histplot((y_test-predictions),bins=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "id": "a5dfc6c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE: 0.3896858212406587\n",
+      "MSE: 0.20325428149258176\n",
+      "RMSE: 0.4508373115577079\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
+    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
+    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "id": "6e679938",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R sq:  0.3315244678627577\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('R sq: ',model_linear_regression.score(x_train,y_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "id": "26a1b11d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation:  0.5757816147314515\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Correlation: ', math.sqrt(model_linear_regression.score(x_train,y_train)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "id": "e3ebabba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R sq:  0.1836418852481948\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('R sq: ',model_linear_regression.score(x_test,y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39be2636",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Tesnorflow Classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "id": "f5dfd832",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.4.1'"
+      ]
+     },
+     "execution_count": 268,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "id": "0dc13695",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1187, 10)"
+      ]
+     },
+     "execution_count": 269,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "id": "e876d6a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the Tensor flow Neural Network with Inputlayer, 2x Hiddenlayer and one Outputlayer\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Dense(units=10,activation='relu')) \n",
+    "model.add(Dense(units=8,activation='relu')) #Hiddenlayer\n",
+    "model.add(Dense(units=5,activation='relu')) #Hiddenlayer\n",
+    "\n",
+    "model.add(Dense(units=1,activation='sigmoid')) #Outputlayer 1 Target\n",
+    "\n",
+    "# For a binary classification Problem\n",
+    "model.compile(loss='binary_crossentropy', optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "id": "b68e27c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/7\n",
+      "38/38 [==============================] - 1s 6ms/step - loss: 0.6612 - val_loss: 0.6592\n",
+      "Epoch 2/7\n",
+      "38/38 [==============================] - 0s 2ms/step - loss: 0.6298 - val_loss: 0.6465\n",
+      "Epoch 3/7\n",
+      "38/38 [==============================] - 0s 1ms/step - loss: 0.6198 - val_loss: 0.6371\n",
+      "Epoch 4/7\n",
+      "38/38 [==============================] - 0s 1ms/step - loss: 0.5942 - val_loss: 0.6305\n",
+      "Epoch 5/7\n",
+      "38/38 [==============================] - 0s 2ms/step - loss: 0.6049 - val_loss: 0.6230\n",
+      "Epoch 6/7\n",
+      "38/38 [==============================] - 0s 2ms/step - loss: 0.5675 - val_loss: 0.6186\n",
+      "Epoch 7/7\n",
+      "38/38 [==============================] - 0s 1ms/step - loss: 0.5691 - val_loss: 0.6111\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x1ffeb282c70>"
+      ]
+     },
+     "execution_count": 271,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(x=X_train, \n",
+    "          y=y_train, \n",
+    "          epochs=7,\n",
+    "          validation_data=(X_test, y_test), verbose=1\n",
+    "          )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "id": "e84551ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 272,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UhkY15j/DO+Pv6waXKzgcsHcFrP/QmtM/t0wz+haIHK7LNJXycDp1Y4N3ftzNvxdt55r2Ibw5Jpoqfr52l1Qs95i1WidxZollmkOt0A+/UpdpKuWByn1lrIgMEJEdIpIiIk9cYJ8RIpIsIltF5JPznqslIhki8tall++Z7rmqJU9f357vkg9xx6xEcvOL7C6pWNW6EHeH1TL5zh8haizs+BZmDoY3u8DPL8PJg3ZXqZRykd8d0YuIL7AT6A9kAPHAaGNMcol9IoC5QD9jzDERaWCMOVzi+deBYOCoMeb+i72ft4zoz5kbn87jn28iNrwe0ybGUqOKm54WyT8D2760TuCeW6YZcY01lx9xjS7TVMrNlXdEHwekGGNSjTH5wGzg/Nug3wFMNsYcAzgv5LsCIcB3ZSne042IDeP1UdEk7j3G2KlrOX6mwO6SfltANeg8CiZ9A/cnQo8HYP96mD0aXm0P3z8N2bvtrlIpVQalCfomQHqJxxnObSW1BlqLyEoRWSMiAwBExAd4GfjTxd5ARO4UkQQRScjKyip99R5iSOfGvDO2C9v2n2DUlDUcOXXW7pIuLqgV9P+H1Vht1CfQuAusfN2a1pk+CDbOtr4BKKU8gquWg/gBEcBVwGhgiojUAe4FvjHGXPROGsaY940xMcaYmODgYBeV5F6u6dCQqRNi2HPkFCPfW83B4x7QuOncMs0xs+HhZOsOWCcyYcFd8HJb+PoR2J9kd5VKqd9RmqDPBMJKPA51bispA/jSGFNgjNmDNacfAXQH7heRNOA/wHgReaHcVXuo3q2DmTkpjoPH8xjx3mrSj3rQqLhWI+j1KDywHiZ8bbVZSPoY3u8D714J66ZYq3mUUm6nNCdj/bCC+2qsgI8HxhhjtpbYZwDWCdoJIhIEbACijDHZJfaZCMRUtpOxvyUpPYfxH6ylehU/Pr69Gy2Ca9hdUtmcW6a5fiYcdC7TbDfE2U3zSvBxg+sHlKokynUy1hhTCNwPLAa2AXONMVtF5BkRGeLcbTGQLSLJwDLgsZIhr34pKqwOs+/sTn6hgxHvrWH7wRN2l1Q2/1umuQLu/MnqqbNzMcy83prPX/4fOFEBN2dRSl0SvWDKRimHTzF26hrOFjqYdWscnULr2F1S+eWfgW1fOZdprgDxgRZXWZ00W/aD4DZ6QZZSFUCvjHVj+7LPMGbqGo6fKWD6pFhiwuvZXZLrZO+2euxs+xqyd1nbajWBln2t0G/RF6p50edVykYa9G5uf04u46au5cDxPKZOiKFnqyC7S3K9nH2w+wfrJ/VHyDsOCDSOtkK/1dUQGmut9FFKXTINeg+QdfIs46auZU/2ad4Z24Wr24XYXVLFKSqE/Rucwb8UMhLAFEFATWjeywr+lv20h75Sl0CD3kMcO53PhOnrSN5/gtdHRTOoUyO7S7o8cnNgz/Li4M/ZZ22vG14c+s17Q2BtO6tUyq1p0HuQE3kF3Do9nvX7jvH0kA6M7x5ud0mXlzFwNLV4mmfPcsg/ZfXeCY0tnuZpHA0+btQRVCmbadB7mDP5hTz46Qa+33aY269szv8NbGff3arsVpgPGfHFo/39SYCBwDrWap5zI/46YRc/jlJeToPeAxU5DM98tZWZq/cyoENDXh0ZRdUAHcFyOhv2/AgpzhH/yf3W9qDWxaEffiUEVLe1TKUuNw16D2WMYdrKNJ5dmEzn0DpMnRBDUI0qdpflPoyBrO3F0zxpK6EwF3z8oekV1hRPy34QEqlX6Sqvp0Hv4RZtOcgf52wguGYVpk+Mo1UDD22ZUNEK8mDf6uLgP7TF2l492Fqz37KftYa/ZkN761SqAmjQe4Gk9BxunxlPQZHhvVu6ckWL+naX5P5OHrTW7KcshdRlcNrZAjukY/FFW017gH+grWUq5Qoa9F4i/egZJk5fx76jZ3jp5s7cEH3+bQHUBTkc1gh/91JrtL9vDRTlW43YmvUsnuYJbqstGpRH0qD3IsfPFHDXRwmsST3Ko/1bc3+/VogG06XLP23N6Z9bzXNkp7W9ZuPiKZ4WfaG6fnNSnkGD3sucLSziifmbWbAhk+FdQ/nXsEj8ffVkY7nkpFvTOylLnS0acrBaNEQVr+YJjQO/AHvrVOoCNOi9kDGGV7/fxRtLd3FlqyDeHteFWoHaJ8YlHEXWev1z0zzp65wtGmpYI/22g60bpmtDNuVGNOi92LzEDJ6Yv4kWwdWZNjGW0LrV7C7J++Qdhz0/W8G/41s4ecC6Ujf8Siv02w6C2nq+RNlLg97LrUw5wt0fJRLo78u0CbFEhmpPmArjcFgN2bZ/bf2cm9tvHG0Fftvrtee+soUGfSWw89BJJk2P5+jpfN4cHc0f2ntx90t3krUTdiy0eu5nOv+7rdfSCv1210OTGL1YS10WGvSVxOGTedw+M4EtmccrZ0M0u504ADu+sUb6e5aDoxBqhECbgdYUT/Ne4KdXNquKoUFfiVgN0ZL4ftshbnM2RPOtrA3R7JSbA7uWWKGf8r3VgbNKLYjob432W/WHwFp2V6m8iAZ9JVPkMPzz62RmrErj2g4hvDYyWhui2akgD/b85JzX/wbOHAHfAGjexwr9NgOhpk61qfLRoK+kpq3Ywz8XJtMptA4faEM09+AospZrnjuZeywNEAiLc57MHax31lJlokFfiS3eepCHZmtDNLdkDBxOhu0LYdtXcHCTtT24HbRzLttsFKUreFSpaNBXcucaouUXOnh/fIw2RHNXOfus0N++EPauBOOAWqHOkf4gaNZDb56uLkiDXpF+9AyTZsSzN/s0L97ciRujQ+0uSV3M6WzYucgK/d1LoTDPuqtWm+us0G95NQToxXGqmAa9An7ZEO2R/q15QBuieYb801Yrhu0LrStz83LAr6rVf6ftICv8tR1DpadBr/4nv9DBE/M38fmGTG7uGsq/bowkwE8v6PEYRQWwd1XxFM+JDKsdQ7MeznYMA6FOU7urVDbQoFe/YIzhte938frSXfRsVZ+3x3aldlWd+/U4xsCBJOuq3O0LIWubtb1hJ+uq3LaDoEF7PZlbSWjQq9+kDdG8TPZu57LNhdYSTgzUbV68bDMsDnz0egpvpUGvLmhVyhHucjZE+2BCDJ1C69hdknKFk4eK2zGk/gSOAuveuW2uc7Zj6KO3UPQyGvTqonYdOslEbYjmvfJOQMoSa4pn1xLIPwn+1aFRJ+v+uSEdoGEkNGgHAdXtrlaVkQa9+l0lG6L9/foOTOgRbndJqiIUnrV66+9aDAc2waGtVvADIFCvBTTsCCGRzj8AHaF2mM7zewANelUqZ/ILeWh2EkuStSFapeFwQM5eK/APbYGDm63fj+0p3qdK7eLQD3H+NGin6/jdjAa9KrUih+HZhclMX6kN0Sq1syfhUDIccgb/wS3WPwtOW8+Lj9V3v6Fz6ick0vq9VhMd/duk3EEvIgOA1wFfYKox5oXf2GcE8DRggI3GmDEiEgW8A9QCioDnjDFzLvZeGvTuoWRDtKnjYwiuqQ3RKj2HA3LSnKHvDP6Dm61vBOcE1rFG/P/7A+Ac/ftXtavqSqNcQS8ivsBOoD+QAcQDo40xySX2iQDmAv2MMcdEpIEx5rCItAaMMWaXiDQGEoF2xpicC72fBr37+G7rQR6cvYGgGlWYMSmWVg1q2l2Sckd5J6zmbAc3F/8BOJT8y9F//VYl/gA4f2o11tG/C5U36LsDTxtjrnU+fhLAGPN8iX1eBHYaY6b+zrE2AjcbY3ZdaB8NeveyMT2H25wN0d67JYbuLbUhmioFh8Oa5z+0pcQ3gC1W47ZzqtYtDv1zfwCC2+qyzzK6WND7leL1TYD0Eo8zgG7n7dPa+UYrsaZ3njbGLDqviDggANj9GwXeCdwJ0LSpXr7tTjqH1WHBvT2ZNCOe8dPWakM0VTo+PlZf/fotof3Q4u15x50j/q3F3wASZ0BhrvW8+EJQxC+XfYZ0hJoNdfRfDqUJ+tIeJwK4CggFlotI5LkpGhFpBHwITDDGOM5/sTHmfeB9sEb0LqpJuUhYvWrMv7sHd3+UyMNzNrIvO5cHr9aGaKoMAmtbfXma9Sje5iiCo3usE7/nTvqmr4Ut84r3qVrv18s+g9vqPXhLqTRBnwmElXgc6txWUgaw1hhTAOwRkZ1YwR8vIrWAhcBTxpg1LqhZ2aB2NX9m3hrHE59v4tXvd5J+7Iw2RFOu4eMLQa2snw43Fm/PPeZc+VNi2WfCB1bLZgAfPwhqDY06Q+trodUfoIqeR/otpQn6eCBCRJpjBfwoYMx5+3wBjAami0gQ1lROqogEAAuAWcaYeSiPFuDnw8vDO9O0XjVe+34X+3NyeWecNkRTFaRqXQjvaf2c4yiyevqcm/M/uAV2LoaNn1r34W1xldXbp/V1eh/eEkq7vHIg8BrW/Ps0Y8xzIvIMkGCM+VKs7/AvAwMoXkY5W0TGAdOBrSUON9EYk3Sh99KTsZ5hfmIGT3y+ifD61Zk+SRuiKRsVFUL6GuvG69u/cp7wdd6Ht81Aq7dPUCu7q6xwesGUqhCrdh/hrg+1IZpyI8ZYUzzbF1oN3c7dhzeoTXEXz8bR1sliL6NBryqMNkRTbi1nnzXS37EQ0laCKYKajZwj/UEQ3gv8Auyu0iU06FWFOnwyjztmJrA58zh/G9yeiT2b212SUr925ijs+s4a6acshYIzUKUWRPS3Qr9VfwisZXeVZaZBrypcbn4RD87ewJLkQ9zaszlPDdKGaMqNFeRC6o9W6O/4Fs5kg48/tOjjvA/vQGvtvgfRoFeXRZHD8NzCbUxbuYdr2ofw+ihtiKY8gKPIuiPX9q+tn2Np1vbQWGfoD4Lg1raWWBoa9Oqymr5yD898nUynJrWZOiFWG6Ipz2EMHN5WfDL3QJK1vX5E8cncJl3d8mSuBr267LQhmvIKxzOsqZ3tX0PaCnAUQo2Q4mWbzXu5zdW5GvTKFlZDtATO5Bfy5HVtGdutGT46b688Ve4x61aM27+GXd9b3TkDakLEH6zQj+hvtXiwiQa9ss3+nFwen7+Jn3cdoXuL+rx4cyfC6unFVcrDFeTBnp+KT+aezrJO5jbvVXwyt1bjy1qSBr2ylTGGOfHpPLtwGw5jeOK6tozT0b3yFo4iyEgoPpl7NNXa3qRr8RRPcJsK776pQa/cwv6cXJ74fDPLd2bRrXk9Xry5E83qV7e7LKVcxxjI2uEM/YWwf721vV7L4pO5oTFWIzcX06BXbsMYw2cJGfzz62QKHYbHB7RhfPdwHd0r73RiP+z4xgr9Pcutk7nVG0Cb66zgb97HZTda0aBXbufA8Vye/HwzP+7IIi7cGt2HB+noXnmx3BxI+d55MncJ5J8C/+q/PJlbtW6ZD69Br9ySMYZ5iRk883UyBUUO/nxtWyb20NG9qgQKz1oj/O0LrRH/qUNWf/22g2HEzDIdUoNeubWDx/N48vNNLNuRRWx4XV68uTPNdXSvKguHAzITrZG+jy9c/bcyHUaDXrk9Ywyfr8/kH19t5Wyhg8eubcOkns21X45SpXSxoHe/63hVpSQi3NQ1lCWP9OHKVkE8u3AbI95bze6sU3aXppTH06BXbiWkViBTJ8Tw6sjOpBw+xcDXf2bK8lSKHO71zVMpT6JBr9yOiHBjdChLHu5Nr4hgnvtmGze/u4qUwzq6V6osNOiV22pQK5Ap47vy2sgoUrNOM/CNn3nvp906ulfqEmnQK7cmItwQ3YQlj/TmqtbBPP/tdm56ZxUph0/aXZpSHkODXnmEBjUDee+Wrrw+Koq07NMMfGMF7/60m8Iih92lKeX2NOiVxxARhkY1YcnDfejXpgEvfLudm95dza5DOrpX6mI06JXHCa5ZhXfGdeHN0dHsyz7NoDdW8PaPKTq6V+oCNOiVRxIRru/cmCWP9OHqdg14cdEOhr2zih0HdXSv1Pk06JVHC6pRhXfGdWXymC5kHMvl+jdXMHmZju6VKkmDXnmFQZ0aseTh3vTvEMJLi3dw49ur2H7whN1lKeUWNOiV16hfowqTx3Th7bFd2J9jje7fXLqLAh3dq0pOg155nYGRjVjySB8GdGzEy0t2csPklWw7oKN7VXlp0CuvVK96AG+OjubdcV04dCKPIW+t4PXvdXSvKicNeuXVBnRsxHcP92FgZCNe/X4nQ99aydb9x+0uS6nLSoNeeb161QN4fVQ0793SlcMnzzL0rZW8umQn+YU6uleVgwa9qjSu7dCQJQ/35vrOjXl96S6GTl7Jlkwd3Svvp0GvKpW61QN4dWQUU8bHcOTUWW6YvJJXvtuho3vl1UoV9CIyQER2iEiKiDxxgX1GiEiyiGwVkU9KbJ8gIrucPxNcVbhS5dG/fQhLHu7NkM6NeeOHFIa8tUJH98pr/e49Y0XEF9gJ9AcygHhgtDEmucQ+EcBcoJ8x5piINDDGHBaRekACEAMYIBHoaow5dqH303vGqsvt++RD/N+CzWSfzueePi154OpWVPHztbsspS5Jee8ZGwekGGNSjTH5wGxg6Hn73AFMPhfgxpjDzu3XAkuMMUedzy0BBpTlQyhVUf7QPoQlD/fhhqgmvLUshSFvrmRTRo7dZSnlMqUJ+iZAeonHGc5tJbUGWovIShFZIyIDLuG1iMidIpIgIglZWVmlr14pF6ldzZ+XR3Rm2sQYcnLzufHtVby0eDtnC4vsLk2pcnPVyVg/IAK4ChgNTBGROqV9sTHmfWNMjDEmJjg42EUlKXXp+rUN4buH+zAsugmTl+1m8Bsr2JieY3dZSpVLaYI+Ewgr8TjUua2kDOBLY0yBMWYP1px+RClfq5RbqV3Vn5eGd2b6pFhO5hVy49sr+fei7eQV6OheeabSBH08ECEizUUkABgFfHnePl9gjeYRkSCsqZxUYDFwjYjUFZG6wDXObUq5vb5tGvDdI70Z3jWMd37czeA3V7Buz1G7y1Lqkv1u0BtjCoH7sQJ6GzDXGLNVRJ4RkSHO3RYD2SKSDCwDHjPGZBtjjgL/xPpjEQ8849ymlEeoFejPv2/uxMxb4zhztpAR763m9pnxeoMT5VF+d3nl5abLK5W7ys0vYvqqPbzz425OnS1kWHQoj1zTmiZ1qtpdmlIXXV6pQa/UJco5k8/bP+5mxqo0AMZf0Yz7+raibvUAewtTlZoGvVIVIDMnl9eW7GT++gyqB/hx91UtmdQznGoBfnaXpiohDXqlKtDOQyd5cdEOvt92iAY1q/DQHyIYEROGv6+2klKXT3mvjFVKXUTrkJpMnRDDvLu707ReNZ5asIVrX13ON5sP4G4DKVU5adAr5SIx4fX47O7uTBkfg6+PcO/H67lh8kpW7T5id2mqktOgV8qFRIT+7UNY9MfevHRzJ7JOnmXMlLWMn7ZO72ylbKNz9EpVoLyCIj5cvZe3lqVwPLeAG6Ia8+g1bQirV83u0pSX0ZOxStnseG4B7/20m2kr91DkMIzt1oz7+7UiqEYVu0tTXkKDXik3cehEHq99v4u5CekE+vlwR+8W3N6rBTWq6JJMVT4a9Eq5md1Zp/jP4h18u+UgQTUCeKBfBKPjmhLgp6fNVNno8kql3EzL4Bq8M64rC+7tQcvgGvz9y6384ZWf+G9SJg6Hew2+lOfToFfKRtFN6zL7ziuYPimWagG+PDQ7ievfWsHynVm6Bl+5jAa9UjYTEfq2acA3D/bi1ZGdOZ5bwPhp6xg7da3e9ES5hAa9Um7Cx0e4MTqUpY/24e/Xt2f7wZMMnbyS+z5ez54jp+0uT3kwPRmrlJs6mVfAlOWpTF2xh/xCByNjw3jo6gga1Aq0uzTlhnTVjVIeLOvkWd78YRefrN2Hv68Pt13ZnDv7tKBWoL/dpSk3okGvlBdIO3Kal5fs5KuN+6lbzZ/7+rbilu7NqOLna3dpyg3o8kqlvEB4UHXeHB3NV/dfSYfGtXl24Tb6/ecn5idmUKRLMtVFaNAr5WEiQ2vz0e3d+Oi2btSrHsCjn21k0Bs/88P2Q7okU/0mDXqlPNSVEUH8976evDk6mtyCIm6dkcDI99ewft8xu0tTbkaDXikP5uMjXN+5MUse7sM/h3YgNes0w95exZ2zEkg5fNLu8pSb0JOxSnmR02cL+WDFHt5fnsqZ/EKGdw3jj/0jaFS7qt2lqQqmq26UqmSyT53lrWUpfLRmLz4iTOwZzr19WlG7mi7J9FYa9EpVUulHz/DKkp18kZRJzSp+3Nu3FRN7hBPor0syvY0GvVKVXPL+E7y4eDs/7siiYa1AHu4fwbAuofj76mk6b6FBr5QCYPXubF5YtJ2N6TmE1KrC2G7NGB3XlOCaeqcrT6dBr5T6H2MMP+7IYvqqNJbvzCLA14dBnRoxoUc4UWF17C5PldHFgl7vX6ZUJSMi9G3bgL5tG7A76xSzVqUxLzGDBRsy6RxWh0k9whkY2UjvduVFdESvlOJkXgHzEzOYtXovqUdOE1SjCmO6NWVst6aEaLdMj6BTN0qpUnE4DD+nHGHmqjSW7TiMrwjXRTZiYo9mdGlaFxGxu0R1ATp1o5QqFR8foU/rYPq0DibtyGlmrd7LZwnpfLVxPx2b1GJC93Cu79xYl2d6GB3RK6Uu6vTZQj7fkMnMVWmkHD5FveoBjI4LY9wVzfSKWzeiUzdKqXIzxrBqdzbTV6axdPshfES4tkMIE3s0JzZcp3XsVu6pGxEZALwO+AJTjTEvnPf8ROAlINO56S1jzFTncy8Cg7AaqC0BHjLu9tdFKfW7RISerYLo2SqI9KNn+HDNXmav28c3mw/SrlEtJvZoxtCoJjqt44Z+d0QvIr7ATqA/kAHEA6ONMckl9pkIxBhj7j/vtT2w/gD0dm5aATxpjPnxQu+nI3qlPEdufhFfJFnTOtsPnqRONX9GxoZxyxXNCK1bze7yKpXyjujjgBRjTKrzYLOBoUDyRV9lMUAgEAAI4A8cKk3RSin3VzXAl9FxTRkVG8baPUeZsTKNKctTmbI8lT+0C2Fiz3C6t6iv0zo2K03QNwHSSzzOALr9xn43iUhvrNH/w8aYdGPMahFZBhzACvq3jDHbylu0Usq9iAhXtKjPFS3qk5mTy0fOaZ3vkg/RJqQm43s048boJlQL0IV+dnDVpW9fAeHGmE5Y8/AzAUSkFdAOCMX6g9FPRHqd/2IRuVNEEkQkISsry0UlKaXs0KROVR4f0JbVT17Nizd1wtdHeGrBFq7411Ke/TqZfdln7C6x0inNHH134GljzLXOx08CGGOev8D+vsBRY0xtEXkMCDTG/NP53N+APGPMixd6P52jV8q7GGNI2HuMGavSWLTlIA5j6NemARN6hNMrIkindVykvHP08UCEiDTHWlUzChhz3hs0MsYccD4cApybntkH3CEiz2NN3fQBXrvkT6CU8lgiQmx4PWLD63HweB4fr93LJ2v3sXTaOloGV2dCj3CGdQmlRhWd1qkopVpHLyIDsQLaF5hmjHlORJ4BEowxXzqDfAhQCBwF7jHGbHeO7t/GWnVjgEXGmEcu9l46olfK++UVFLFw0wFmrk5jU8Zxalbx46auoUzoEU7zoOp2l+eR9IIppZRbMsawIT2HmavS+GbzAQqKDFe1CWZCj3D6RATj46PTOqWlQa+UcnuHT+Txybp9fLx2H1knz9I8qDq3XNGMm2NCqRWo97r9PRr0SimPkV/o4NstB5ixKo0N+3KoHuDLsC6hTOjRjFYNatpdntvSoFdKeaRNGTnMWJXG1xsPkF/koFdEEBO6h9O3bQN8dVrnFzTolVIe7cips3y6dh8frd3LoRNnCatXlfFXhDMiJoza1XRaBzTolVJeoqDIweKtB5m5Ko34tGNU9fflhugmjI4LI7JJ7Uq9Jl+DXinldbZkHmfW6jT+m7Sfs4UO2jWqxajYMG6IalIpR/ka9Eopr3U8t4AvkzKZk5DOlswTBPj5MLBjQ0bEhnFF8/qVZommBr1SqlLYknmcOfHpfJGUycm8QprVr8aImDBu7hrq9Tc516BXSlUquflFLNp6gNnr0lm75yi+PkLfNsGMjG1K3zbB+Pm6qp+j+9CgV0pVWqlZp5ibkMG8xAyOnDpLg5pVuLlrKCNiwgj3onYLGvRKqUqvoMjBD9sPMzc+nWU7DuMw0L1FfUbGhjGgY0OPvwWiBr1SSpVw8Hge8xLTmZOQTvrRXGoF+nFjdBNGxjalfeNadpdXJhr0Sin1GxwOw5rUbGbHp7Noy0Hyixx0Cq3NiJgwhkQ19qgeOxr0Sin1O46dzueLpEzmxKez/eBJAv19GBTZmFFxYcQ0q+v2F2Np0CulVCkZY9iUcZzZ8el8mZTJ6fwiWgRXZ2RMGMO6hBJcs4rdJf4mDXqllCqD02cLWbj5AHPj00nYeww/H+EP7UIYGRdG74hgt2qspkGvlFLllHL4JHPi05m/PpOjp/NpVDuQ4V1DGR4TRli9anaXp0GvlFKukl/o4Ptth5gdn87Pu7IAuLJVECNjw+jfPoQqfvYs09SgV0qpCpCZk8tnCel8lpBBZk4udav5c2N0KKPiwmgdcnlvkqJBr5RSFajIYViRcoS58el8l3yQgiJDdNM6jIoNY3CnxlSv4lfhNWjQK6XUZZJ96iwLNmQyOz6dlMOnqB7gy+BOjRkZF0Z0WJ0KW6apQa+UUpeZMYb1+44xJz6drzYeILegiNYhNRgZ25Qbo5tQr3qAS99Pg14ppWx0Mq+ArzcdYHZ8OhvTcwjw9aF/hxBGxYbRs2WQS3rma9ArpZSb2H7wBHPi01mwIZOcMwWE1q36v575jetULfNxNeiVUsrN5BUU8V3yIebE72NlSjY+Atd1bMRbY6LLNI9/saCv+FPBSimlfiXQ35chnRszpHNj9mWf4bPEdBzGVMjJWg16pZSyWdP61Xj0mjYVdnzvu5+WUkqpX9CgV0opL6dBr5RSXk6DXimlvJwGvVJKeTkNeqWU8nIa9Eop5eU06JVSysu5XQsEEckC9pbjEEHAEReVYydv+Rygn8Vdectn8ZbPAeX7LM2MMcG/9YTbBX15iUjChfo9eBJv+Rygn8Vdectn8ZbPARX3WXTqRimlvJwGvVJKeTlvDPr37S7ARbzlc4B+FnflLZ/FWz4HVNBn8bo5eqWUUr/kjSN6pZRSJWjQK6WUl/OaoBeRASKyQ0RSROQJu+spKxGZJiKHRWSL3bWUl4iEicgyEUkWka0i8pDdNZWFiASKyDoR2ej8HP+wu6byEhFfEdkgIl/bXUt5iEiaiGwWkSQR8eh7kIpIHRGZJyLbRWSbiHR32bG9YY5eRHyBnUB/IAOIB0YbY5JtLawMRKQ3cAqYZYzpaHc95SEijYBGxpj1IlITSARu8LT/XcS6t1t1Y8wpEfEHVgAPGWPW2FxamYnII0AMUMsYM9juespKRNKAGGOMx18wJSIzgZ+NMVNFJACoZozJccWxvWVEHwekGGNSjTH5wGxgqM01lYkxZjlw1O46XMEYc8AYs975+0lgG9DE3qounbGccj70d/547AhJREKBQcBUu2tRFhGpDfQGPgAwxuS7KuTBe4K+CZBe4nEGHhgo3kxEwoFoYK3NpZSJc6ojCTgMLDHGeOTncHoN+DPgsLkOVzDAdyKSKCJ32l1MOTQHsoDpzim1qSJS3VUH95agV25MRGoA84E/GmNO2F1PWRhjiowxUUAoECciHjmtJiKDgcPGmES7a3GRK40xXYDrgPucU5+eyA/oArxjjIkGTgMuO9foLUGfCYSVeBzq3KZs5pzTng98bIz53O56ysv5dXoZMMDmUsqqJzDEObc9G+gnIh/ZW1LZGWMynf88DCzAmsb1RBlARolvivOwgt8lvCXo44EIEWnuPIkxCvjS5poqPedJzA+AbcaYV+yup6xEJFhE6jh/r4p10n+7rUWVkTHmSWNMqDEmHOv/Jz8YY8bZXFaZiEh150l+nNMc1wAeuVrNGHMQSBeRNs5NVwMuW7Tg56oD2ckYUygi9wOLAV9gmjFmq81llYmIfApcBQSJSAbwd2PMB/ZWVWY9gVuAzc75bYD/M8Z8Y19JZdIImOlc3eUDzDXGePSyRC8RAiywxhP4AZ8YYxbZW1K5PAB87ByspgKTXHVgr1heqZRS6sK8ZepGKaXUBWjQK6WUl9OgV0opL6dBr5RSXk6DXimlvJwGvVJKeTkNeqWU8nL/D9DZNSt2hm4yAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#model.history.history\n",
+    "model_loss = pd.DataFrame(model.history.history)\n",
+    "# Create a plot to compare the performance on the training dataset to the loss on the validation dataset\n",
+    "model_loss.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "id": "242facac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Vorhersagen bestimmen\n",
+    "predictions = (model.predict(X_test) > 0.5).astype(\"int32\")\n",
+    "prediction = (model.predict(X_train) > 0.5).astype(\"int32\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "id": "b889ba90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trainingsdaten:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.70      0.72      0.71       534\n",
+      "           1       0.76      0.75      0.76       653\n",
+      "\n",
+      "    accuracy                           0.74      1187\n",
+      "   macro avg       0.73      0.73      0.73      1187\n",
+      "weighted avg       0.74      0.74      0.74      1187\n",
+      "\n",
+      "Testdaten:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.66      0.74      0.70       139\n",
+      "           1       0.74      0.66      0.70       158\n",
+      "\n",
+      "    accuracy                           0.70       297\n",
+      "   macro avg       0.70      0.70      0.70       297\n",
+      "weighted avg       0.70      0.70      0.70       297\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Klassifikations Report\n",
+    "print(\"Trainingsdaten:\")\n",
+    "print(classification_report(y_train,prediction))\n",
+    "print(\"Testdaten:\")\n",
+    "print(classification_report(y_test,predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "id": "71cf265c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 42.0, 'Predicted label')"
+      ]
+     },
+     "execution_count": 275,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Confusion-Matrix Testdaten\n",
+    "cm = confusion_matrix(y_test,predictions)\n",
+    "df_cm = pd.DataFrame(cm, index=['worse wine','better wine'], columns=['worse wine', 'better wine'],)\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": 276,
+   "id": "ca4355c3",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True Negatives: 103\n",
+      "False Positives: 36\n",
+      "False Negatives: 54\n",
+      "True Positives: 104\n",
+      "Recall: 0.6582278481012658\n",
+      "Precision: 0.7428571428571429\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Confusion Matrix\n",
+    "tn, fp, fn, tp = cm.ravel()\n",
+    "recall = tp/(fn+tp)\n",
+    "precision = tp/(tp+fp)\n",
+    "print(\"True Negatives: \" + str(tn))\n",
+    "print(\"False Positives: \" + str(fp))\n",
+    "print(\"False Negatives: \" + str(fn))\n",
+    "print(\"True Positives: \" + str(tp))\n",
+    "print(\"Recall: \" + str(recall))\n",
+    "print(\"Precision: \" + str(precision))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b52f5597",
+   "metadata": {
+    "editable": true,
+    "include": false,
+    "paragraph": "Deployment",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Deployment\n",
+    "Um dieses trainierte ML-Modell zu nutzen, muss eine API erstellt werden.  \n",
+    "Das Ziel ist, dass Sie mit einer API-Anfrage über das Internet auf das ML-Modell zugreifen können. (Oder Ihr Intranet)  \n",
+    "Der Code für den Einsatz ist hier zu finden. https://github.com/Dustin-dusTir/ml-services-api  \n",
+    "eventuell läuft dort noch eine Live-Version der API und des Frontends."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daa5810d",
+   "metadata": {
+    "editable": true,
+    "include": false,
+    "paragraph": "Deployment",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Wenn wir eine API für ein ML-Modell erstellen, wollen wir nicht jedes Mal ein neues Modell trainieren, wenn wir den API-Server bereitstellen oder neu bereitstellen wollen.\n",
+    "Deshalb müssen wir das ML-Modell exportieren und speichern.\n",
+    "\n",
+    "Für dieses spezielle Modell verwenden wir die Tensorflow-Funktionalität, um das Tensorflow-Modell zu exportieren.\n",
+    "Und wir verwenden das Pickle-Modul, um den Datenskalierer auch auf der Festplatte zu speichern."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2b935c7",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# save the ml model\n",
+    "model.save(filepath='wine_NN_model_12epochs')\n",
+    "\n",
+    "# load the model again\n",
+    "new_model = tf.keras.models.load_model('wine_NN_model_12epochs')\n",
+    "\n",
+    "# make sure, that the model has been saved and loaded correctly\n",
+    "sum((model.predict(X_test) == new_model.predict(X_test)) == False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc34b725",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save the data scaler with pickle\n",
+    "pickle.dump(scaler, open('scaler.sav', 'wb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b4db306-b5b3-45a2-b3f7-ea2cdd1a36b2",
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Business",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Eine Weinmanufaktur möchte sicherstellen, dass sie stets hochwertigen und schmackhaften Wein produziert. Bisher werden alle neuen Chargen von professionellen Weinverkostern getestet und auf einer Skala von 1 bis 10 bewertet. Dieser Prozess ist jedoch teuer und subjektiv. Das Ziel der Manufaktur ist es, ein maschinelles Lernmodell zu entwickeln, das automatisch neue Weinchargen basierend auf deren chemischen Eigenschaften kategorisiert. So sollen gute und schlechte Weine effizient unterschieden werden können."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "308d3a59-95dc-40ff-99b2-020492a9045c",
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Daten",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Der Datensatz, genannt \"Wine Quality Data Set\", wurde von der UCI-Website heruntergeladen und enthält reale Daten aus einer Weinmanufaktur in Nordportugal, gesammelt im Jahr 2009. Der Datensatz umfasst sowohl rote als auch weiße Weine, wobei der Datensatz der Rotweine 12 Spalten und 1600 Einträge umfasst. Die Spalten sind nach den Inhaltsstoffen und der Qualitätsbewertung des Weins benannt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8d7cf97-95ee-43b6-849a-7e943e085794",
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Datenvorbereitung",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Bei der Datenvorbereitung wurden Duplikate entfernt und Ausreißer anhand des 98. Perzentils entfernt. Fehlende Daten waren nicht vorhanden. Da die Daten unausgeglichen waren, wurde die Zielvariable in einen binären Klassifikator umgewandelt. Die Merkmale wurden mit dem StandardScaler von sklearn skaliert."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7700a90e-758b-4f31-a7bb-8443117c3084",
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Datenmodell",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Für die Modellierung wurden verschiedene Algorithmen verwendet, darunter Lineare Regression, Logistische Regression und ein neuronales Netzwerk mit Tensorflow. Es wurden keine speziellen Hyperparameter verwendet. Das Ziel war es, vorherzusagen, ob ein Wein über oder unter der durchschnittlichen Qualität liegt. Die Daten wurden in 80% Trainings- und 20% Testdaten aufgeteilt."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb03b5fe-16cf-40d4-a100-3b9cb2438aa3",
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Evaluation",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Die Leistung des Modells wurde anhand von verschiedenen Metriken, insbesondere für das neuronale Netzwerk, bewertet. Diese Metriken helfen dabei, die Genauigkeit und Zuverlässigkeit des Modells bei der Vorhersage der Weinqualität zu bestimmen."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6525529a-c38a-49f3-a41d-bc7c30711a59",
+   "metadata": {
+    "editable": true,
+    "include": true,
+    "paragraph": "Umsetzung",
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "Das ML-Modell kann in der Weinmanufaktur eingesetzt werden, wo der Winzer eine Probe entnimmt und deren chemische Komponenten analysiert. Diese Daten können dann in den ML-Algorithmus eingespeist werden, um sofortiges und kostengünstiges Feedback zur Weinqualität zu erhalten."
+   ]
+  }
+ ],
+ "metadata": {
+  "branche": "Wine",
+  "dataSource": "https://archive.ics.uci.edu/datasets.php",
+  "funktion": "Geschmacksbewertung",
+  "interpreter": {
+   "hash": "aab7ff84f4433dd8b68de441cd3c658d57659112bcb62d3bd6aa325045009f13"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  },
+  "skipNotebookInDeployment": false,
+  "title": "Vorhersage der Weinqualität mittels maschinellem Lernen",
+  "teaser": "Eine Weinmanufaktur möchte die Qualität und den Geschmack ihrer Weine durch den Einsatz eines maschinellen Lernmodells vorhersagen, um den teuren und subjektiven Prozess der professionellen Verkostung zu ersetzen.",
+  "repoLink": ""
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
-- 
GitLab