diff --git a/CRM/Customer Churn Prediction/notebook.ipynb b/CRM/Customer Churn Prediction/notebook.ipynb
index 021312256f4fb05a05ab69955f381c780086c4c7..8e89194b4bb4f2fa15509978d5152b2563fdc15a 100644
--- a/CRM/Customer Churn Prediction/notebook.ipynb
+++ b/CRM/Customer Churn Prediction/notebook.ipynb
@@ -1,4076 +1,4318 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": false,
- "paragraph": "Business",
- "slideshow": {
- "slide_type": ""
+ "metadata": {
+ "branche": "Telekommunikation",
+ "dataSource": "https://storage.googleapis.com/ml-service-repository-datastorage/Customer_Churn_Prediction_data.csv",
+ "funktion": "Marketing",
+ "interpreter": {
+ "hash": "07673f924689cda9590af2124148040e2ce6fe7ee39bbd541ae69d9c32a71079"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.9"
+ },
+ "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/blob/main/CRM/Customer%20Churn%20Prediction/notebook.ipynb?ref_type=heads",
+ "skipNotebookInDeployment": false,
+ "teaser": "Ein wichtiges Ziel f\u00fcr jedes Unternehmen liegt in der Erhaltung wertvoller Kundenbeziehungen. Problematisch ist die Abwanderung von Kunden zu Wettbewerbern (= Churn). Der Datensatz f\u00fcr diese Demo wurde auf der Kaggle Data Science Plattform ver\u00f6ffentlicht. Die Zeilen des Datensatzes stellen jeweils einen Kunden dar. Die Spalten beschreiben die Merkmale der Kunden. Auf Basis der Merkmale wird versucht zu klassifizieren, ob ein Kunde das Unternehmen verl\u00e4sst oder nicht. Verschiedene Schritte zur Datenvorbereitung werden umgesetzt. Als Datenmodell wird eine logisitische Regression verwendet. Das finale Datenmodell erreicht eine Genauigkeit von 76% und einen Recall von 73%. Fast drei Viertel aller abwanderungswilligen Kunden werden durch dieses Modell erkannt.",
+ "title": "Vorhersage der Abwanderung von Kunden"
},
- "tags": []
- },
- "source": [
- "# 1. Business Understanding"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "include": true,
- "paragraph": "Business"
- },
- "source": [
- "Ein wichtiges Ziel für jedes Unternehmen liegt in der Erhaltung wertvoller Kundenbeziehungen. Für Unternehmen ist daher eine Einschätzung der Qualität der Kundenbeziehung wesentlich. Problematisch ist in diesem Sinne die Abwanderung von Kunden zu Wettbewerbern (= Churn). Soweit sich das Risiko der Abwanderung eines Kunden vorab einschätzen lässt, können Gegenmaßnahmen eingeleitet werden. Die Abschätzung der Wahrscheinlichkeit, mit der ein Kunde abwandert oder zu einem Wettbewerber wechselt ist in diesem Sinne wesentlich. Darüber hinaus stellt sich die Frage, anhand welcher Merkmale abwanderungswillige Kunden zu erkennen sind. Mit dieser Demo kann die Abwanderung eines Kunden vorhergesagt werden, so dass frühzeitig entsprechende Maßnahmen ergriffen werden können.\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": false,
- "paragraph": "Daten",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# 2. Daten und Datenverständnis"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": true,
- "paragraph": "Daten"
- },
- "source": [
- "Der Datensatz für diese Demo wurde auf der [Kaggle Data Science Plattform](https://www.kaggle.com/) veröffentlicht. Die Daten stammen von einem Unternehmen aus der Telekommunikationsbranche und wurde von IBM zur Verfügung gestellt. Die Zeilen des Datensatzes stellen jeweils einen Kunden dar. Die Spalten beschreiben die Merkmale der Kunden. Auf Basis der Merkmale wird versucht zu klassifizieren, ob ein Kunde das Unternehmen verlässt oder nicht. Daher enthalten die historischen Daten auch die Zielvariable „Churn“ (= Abwanderung), die Auskunft darüber gibt, ob ein Kunde abgewandert ist. Die Kundenmerkmale sind in drei numerische Variablen (z.B. monatlicher Rechnungsbetrag, Alter der Kundenbeziehung, etc.) und 18 kategoriale Variablen (z.B. Geschlecht, diverse Servicekategorien, etc.) unterteilt."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "include": true,
- "paragraph": "Datenvorbereitung"
- },
- "source": [
- "Zunächst wird der Typ der Daten nach dem Einlesen in das Notebook überprüft. Einlesefehler werden entsprechend korrigiert. Danach werden die einzelnen Kundenmerkmale einer deskriptiven Analyse unterzogen. Damit lässt sich der Zusammenhang zur Zielvariable Kundenabwanderung visuell darstellen. Auf der Grundlage einer Korrelationsanalyse werden die Zusammenhänge zwischen Kundenmerkmalen und der Zielvariable untersucht. Kundenmerkmale die keinen Mehrwert bieten werden entfernt (= Dimensionsreduktion). Die Ausgewogenheit des Datensatzes in Bezug auf die Zielvariable wird grafisch dargestellt. Die Kunden mit Abwanderung machen etwa 27 % des gesamten Datensatzes aus, während loyale Kunden etwa 73% ausmachen. Daher liegt ein unausgewogener Datensatz vor. Anschließend werden die kategorialen Werte umgewandelt (= Bildung von Dummy Variablen). Die einzelnen Kundenmerkmale werden auf Multikollinearität und statistische Signifikanz geprüft. Schließlich werden alle Kundenmerkmale auf ein gemeinsames Messniveau gebracht (= Standardisierung). Durch ein Undersampling wird die Unausgewogenheit des Datensatzes ausgeglichen. Abschließend werden Trainings- und Testdaten gebildet."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 2.1. Import von relevanten Modulen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 141,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "import statsmodels.api as sm\n",
- "import warnings\n",
- "import imblearn\n",
- "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
- "from sklearn.preprocessing import StandardScaler\n",
- "from sklearn.linear_model import LogisticRegression\n",
- "from imblearn.under_sampling import InstanceHardnessThreshold\n",
- "from sklearn import metrics\n",
- "from sklearn.metrics import classification_report, confusion_matrix\n",
- "from sklearn.model_selection import train_test_split\n",
- "sns.set()\n",
- "# remove warnings\n",
- "warnings.filterwarnings('ignore')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 2.2. Daten einlesen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 142,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>customerID</th>\n",
- " <th>gender</th>\n",
- " <th>SeniorCitizen</th>\n",
- " <th>Partner</th>\n",
- " <th>Dependents</th>\n",
- " <th>tenure</th>\n",
- " <th>PhoneService</th>\n",
- " <th>MultipleLines</th>\n",
- " <th>InternetService</th>\n",
- " <th>OnlineSecurity</th>\n",
- " <th>...</th>\n",
- " <th>DeviceProtection</th>\n",
- " <th>TechSupport</th>\n",
- " <th>StreamingTV</th>\n",
- " <th>StreamingMovies</th>\n",
- " <th>Contract</th>\n",
- " <th>PaperlessBilling</th>\n",
- " <th>PaymentMethod</th>\n",
- " <th>MonthlyCharges</th>\n",
- " <th>TotalCharges</th>\n",
- " <th>Churn</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>7590-VHVEG</td>\n",
- " <td>Female</td>\n",
- " <td>0</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>1</td>\n",
- " <td>No</td>\n",
- " <td>No phone service</td>\n",
- " <td>DSL</td>\n",
- " <td>No</td>\n",
- " <td>...</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>Month-to-month</td>\n",
- " <td>Yes</td>\n",
- " <td>Electronic check</td>\n",
- " <td>29.85</td>\n",
- " <td>29.85</td>\n",
- " <td>No</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>5575-GNVDE</td>\n",
- " <td>Male</td>\n",
- " <td>0</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>34</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>DSL</td>\n",
- " <td>Yes</td>\n",
- " <td>...</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>One year</td>\n",
- " <td>No</td>\n",
- " <td>Mailed check</td>\n",
- " <td>56.95</td>\n",
- " <td>1889.5</td>\n",
- " <td>No</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>3668-QPYBK</td>\n",
- " <td>Male</td>\n",
- " <td>0</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>2</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>DSL</td>\n",
- " <td>Yes</td>\n",
- " <td>...</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>Month-to-month</td>\n",
- " <td>Yes</td>\n",
- " <td>Mailed check</td>\n",
- " <td>53.85</td>\n",
- " <td>108.15</td>\n",
- " <td>Yes</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>7795-CFOCW</td>\n",
- " <td>Male</td>\n",
- " <td>0</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>45</td>\n",
- " <td>No</td>\n",
- " <td>No phone service</td>\n",
- " <td>DSL</td>\n",
- " <td>Yes</td>\n",
- " <td>...</td>\n",
- " <td>Yes</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>One year</td>\n",
- " <td>No</td>\n",
- " <td>Bank transfer (automatic)</td>\n",
- " <td>42.30</td>\n",
- " <td>1840.75</td>\n",
- " <td>No</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>9237-HQITU</td>\n",
- " <td>Female</td>\n",
- " <td>0</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>2</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>Fiber optic</td>\n",
- " <td>No</td>\n",
- " <td>...</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>Month-to-month</td>\n",
- " <td>Yes</td>\n",
- " <td>Electronic check</td>\n",
- " <td>70.70</td>\n",
- " <td>151.65</td>\n",
- " <td>Yes</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 21 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " customerID gender SeniorCitizen Partner Dependents tenure PhoneService \\\n",
- "0 7590-VHVEG Female 0 Yes No 1 No \n",
- "1 5575-GNVDE Male 0 No No 34 Yes \n",
- "2 3668-QPYBK Male 0 No No 2 Yes \n",
- "3 7795-CFOCW Male 0 No No 45 No \n",
- "4 9237-HQITU Female 0 No No 2 Yes \n",
- "\n",
- " MultipleLines InternetService OnlineSecurity ... DeviceProtection \\\n",
- "0 No phone service DSL No ... No \n",
- "1 No DSL Yes ... Yes \n",
- "2 No DSL Yes ... No \n",
- "3 No phone service DSL Yes ... Yes \n",
- "4 No Fiber optic No ... No \n",
- "\n",
- " TechSupport StreamingTV StreamingMovies Contract PaperlessBilling \\\n",
- "0 No No No Month-to-month Yes \n",
- "1 No No No One year No \n",
- "2 No No No Month-to-month Yes \n",
- "3 Yes No No One year No \n",
- "4 No No No Month-to-month Yes \n",
- "\n",
- " PaymentMethod MonthlyCharges TotalCharges Churn \n",
- "0 Electronic check 29.85 29.85 No \n",
- "1 Mailed check 56.95 1889.5 No \n",
- "2 Mailed check 53.85 108.15 Yes \n",
- "3 Bank transfer (automatic) 42.30 1840.75 No \n",
- "4 Electronic check 70.70 151.65 Yes \n",
- "\n",
- "[5 rows x 21 columns]"
- ]
- },
- "execution_count": 142,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_raw = pd.read_csv(\"https://storage.googleapis.com/ml-service-repository-datastorage/Customer_Churn_Prediction_data.csv\")\n",
- "data_raw.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 143,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'pandas.core.frame.DataFrame'>\n",
- "RangeIndex: 7043 entries, 0 to 7042\n",
- "Data columns (total 21 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 customerID 7043 non-null object \n",
- " 1 gender 7043 non-null object \n",
- " 2 SeniorCitizen 7043 non-null int64 \n",
- " 3 Partner 7043 non-null object \n",
- " 4 Dependents 7043 non-null object \n",
- " 5 tenure 7043 non-null int64 \n",
- " 6 PhoneService 7043 non-null object \n",
- " 7 MultipleLines 7043 non-null object \n",
- " 8 InternetService 7043 non-null object \n",
- " 9 OnlineSecurity 7043 non-null object \n",
- " 10 OnlineBackup 7043 non-null object \n",
- " 11 DeviceProtection 7043 non-null object \n",
- " 12 TechSupport 7043 non-null object \n",
- " 13 StreamingTV 7043 non-null object \n",
- " 14 StreamingMovies 7043 non-null object \n",
- " 15 Contract 7043 non-null object \n",
- " 16 PaperlessBilling 7043 non-null object \n",
- " 17 PaymentMethod 7043 non-null object \n",
- " 18 MonthlyCharges 7043 non-null float64\n",
- " 19 TotalCharges 7043 non-null object \n",
- " 20 Churn 7043 non-null object \n",
- "dtypes: float64(1), int64(2), object(18)\n",
- "memory usage: 1.1+ MB\n"
- ]
- }
- ],
- "source": [
- "data_raw.info()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenvorbereitung"
- },
- "source": [
- "Der Datensatz besteht aus 7.043 Zeilen und 21 Attributen:\n",
- "\n",
- "- Zu prognostizierendes Attribut: Abwanderung\n",
- "- Numerische Attribute: Vertragsdauer, Monatsgebühren und Gesamtgebühren.\n",
- "- Kategorische Attribute: CustomerID, Gender, SeniorCitizen, Partner, Angehörige, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod.\n",
- "Es wurden nicht alle Datentypen korrekt eingelesen:\n",
- "\n",
- "- TotalCharges muss ein numerischer Wert sein -> in Float umwandeln\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 144,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>customerID</th>\n",
- " <th>gender</th>\n",
- " <th>SeniorCitizen</th>\n",
- " <th>Partner</th>\n",
- " <th>Dependents</th>\n",
- " <th>tenure</th>\n",
- " <th>PhoneService</th>\n",
- " <th>MultipleLines</th>\n",
- " <th>InternetService</th>\n",
- " <th>OnlineSecurity</th>\n",
- " <th>...</th>\n",
- " <th>DeviceProtection</th>\n",
- " <th>TechSupport</th>\n",
- " <th>StreamingTV</th>\n",
- " <th>StreamingMovies</th>\n",
- " <th>Contract</th>\n",
- " <th>PaperlessBilling</th>\n",
- " <th>PaymentMethod</th>\n",
- " <th>MonthlyCharges</th>\n",
- " <th>TotalCharges</th>\n",
- " <th>Churn</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>0 rows × 21 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- "Empty DataFrame\n",
- "Columns: [customerID, gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges, Churn]\n",
- "Index: []\n",
- "\n",
- "[0 rows x 21 columns]"
- ]
- },
- "execution_count": 144,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# test for duplicates\n",
- "data_raw[data_raw.duplicated(keep=False)]"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Keine Duplikate im Datensatz"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 2.3. Datenbereinigung"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Hier sollten die ersten Lesefehler korrigiert werden, bevor die eigentliche Datenaufbereitung erfolgt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 145,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'pandas.core.frame.DataFrame'>\n",
- "RangeIndex: 7043 entries, 0 to 7042\n",
- "Data columns (total 21 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 customerID 7043 non-null object \n",
- " 1 gender 7043 non-null object \n",
- " 2 SeniorCitizen 7043 non-null int64 \n",
- " 3 Partner 7043 non-null object \n",
- " 4 Dependents 7043 non-null object \n",
- " 5 tenure 7043 non-null int64 \n",
- " 6 PhoneService 7043 non-null object \n",
- " 7 MultipleLines 7043 non-null object \n",
- " 8 InternetService 7043 non-null object \n",
- " 9 OnlineSecurity 7043 non-null object \n",
- " 10 OnlineBackup 7043 non-null object \n",
- " 11 DeviceProtection 7043 non-null object \n",
- " 12 TechSupport 7043 non-null object \n",
- " 13 StreamingTV 7043 non-null object \n",
- " 14 StreamingMovies 7043 non-null object \n",
- " 15 Contract 7043 non-null object \n",
- " 16 PaperlessBilling 7043 non-null object \n",
- " 17 PaymentMethod 7043 non-null object \n",
- " 18 MonthlyCharges 7043 non-null float64\n",
- " 19 TotalCharges 7032 non-null float64\n",
- " 20 Churn 7043 non-null object \n",
- "dtypes: float64(2), int64(2), object(17)\n",
- "memory usage: 1.1+ MB\n"
- ]
- }
- ],
- "source": [
- "# convert total charges\n",
- "data_raw['TotalCharges'] = pd.to_numeric(data_raw['TotalCharges'], errors='coerce')\n",
- "data_raw.info()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Die Konvertierung der TotalCharges hat zu Nullwerten geführt. Diese Nullwerte müssen korrigiert werden."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 146,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Remove zero values\n",
- "# axis = 0 rows / axis = 1 columns\n",
- "data_no_mv = data_raw.dropna(axis=0)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 2.4. Deskriptive Analytik"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "In diesem Teil des Notebooks soll das Datenverständnis mit Hilfe der deskriptiven Analytik berücksichtigt werden. Nach dem Entfernen der Nullwerte besteht der Datensatz aus 7032 Zeilen, von denen jeweils eine einen Kunden beschreibt, und 21 Spalten, die die Attribute des Kunden definieren. Mit Hilfe dieser Daten soll versucht werden, zu klassifizieren, ob ein Kunde abwandert oder nicht. Zu diesem Zweck enthalten die historischen Daten die Zielvariable \"Churn\", die Auskunft darüber gibt, ob ein Kunde abgewandert ist.\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 2.4.1. Kontinuierliche Merkmale"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Zunächst werden die Verteilungen der kontinuierlichen Merkmale einzeln untersucht und in einem zweiten Schritt die kategorialen Merkmale in Zusammenhang mit der Zielvariablen gesetzt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 147,
- "metadata": {},
- "outputs": [],
- "source": [
- "# load continous features\n",
- "numeric_data = data_no_mv.select_dtypes(include=[np.number])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Besitz"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 148,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<seaborn.axisgrid.FacetGrid at 0x23e942418e0>"
- ]
- },
- "execution_count": 148,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 360x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.displot(numeric_data[\"tenure\"])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "- Keine Normalverteilung erkennbar.\n",
- "- Keine Ausreißer erkennbar.\n",
- "- Kunden sind potentiell gleichmäßig über die einzelnen Monate verteilt, aber eine große Anzahl von Kunden ist noch nicht lange im Unternehmen.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 149,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='tenure', ylabel='Density'>"
- ]
- },
- "execution_count": 149,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"tenure\"],\n",
- " bins=10,\n",
- " color='orange',\n",
- " label='Non-Churn',\n",
- " kde=True)\n",
- "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"tenure\"],\n",
- " bins=10,\n",
- " color='blue',\n",
- " label='Churn',\n",
- " kde=True)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Kunden, die noch nicht lange bei dem Unternehmen sind, werden eher abwandern als langjährige Kunden."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Monatliche Kosten"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 150,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='MonthlyCharges', ylabel='Density'>"
- ]
- },
- "execution_count": 150,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(numeric_data[\"MonthlyCharges\"])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "- Es ist keine Normalverteilung erkennbar.\n",
- "- Die meisten Kunden befinden sich im vorderen Teil der Verteilung und zahlen relativ niedrige monatliche Gebühren.\n",
- "- Dennoch verläuft die Kurve gleichmäßig mit einem erneuten Anstieg nach hinten und dementsprechend können keine Ausreißer identifiziert werden.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 151,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='MonthlyCharges', ylabel='Density'>"
- ]
- },
- "execution_count": 151,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"MonthlyCharges\"],\n",
- " bins=10,\n",
- " color='orange',\n",
- " label='Non-Churn',\n",
- " kde=True)\n",
- "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"MonthlyCharges\"],\n",
- " bins=10,\n",
- " color='blue',\n",
- " label='Churn',\n",
- " kde=True)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "- Kunden mit niedrigen monatlichen Gebühren sind eher abwanderungsbereit.\n",
- "- Der Abwanderungstrend zwischen Kunden, die abwandern, und Kunden, die nicht abwandern, gleicht sich an, wenn die monatlichen Gebühren steigen.\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Gesamtkosten"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 152,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
- ]
- },
- "execution_count": 152,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(numeric_data[\"TotalCharges\"])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "- Die Kurve flacht nach hinten hin extrem stark ab.\n",
- "- Es sind Ähnlichkeiten mit der Exponentialverteilung zu erkennen. -> Test der logarithmischen Transformation zur Erreichung einer Normalverteilung.\n",
- "- Es ist fraglich, ob es Ausreißer im hinteren Teil gibt. -> Boxplot\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 153,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Boxplot für TotalCharges erstellen, um sicherzustellen, dass keine Ausreißer vorhanden sind.\n",
- "plt.boxplot(numeric_data[\"TotalCharges\"])\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "- Boxplot zeigt keine Ausreißer.\n",
- "- Dies bedeutet, dass auch bei den Gesamtkosten keine Ausreißer festgestellt werden können.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 154,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
- ]
- },
- "execution_count": 154,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# logarithmic transformation\n",
- "log_charges = np.log(data_no_mv[\"TotalCharges\"])\n",
- "sns.distplot(log_charges)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "- Auch die Transformation mit Hilfe des Logarithmus führt nicht zu einer Normalverteilung.\n",
- "- Vor weiteren Transformationen sollte zunächst die Korrelation mit anderen Variablen untersucht werden.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 155,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
- ]
- },
- "execution_count": 155,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"TotalCharges\"],\n",
- " bins=10,\n",
- " color='orange',\n",
- " label='Non-Churn',\n",
- " kde=True)\n",
- "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"TotalCharges\"],\n",
- " bins=10,\n",
- " color='blue',\n",
- " label='Churn',\n",
- " kde=True)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "\n",
- "Die Verteilung ist über die gesamte Bandbreite der Kosten sowohl bei den abwandernden als auch bei den nicht abwandernden Kunden nahezu identisch.\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Korrelationsanalyse"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 156,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 156,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# correlation between continous features\n",
- "feature_corr = numeric_data.drop(\"SeniorCitizen\", axis=1).corr()\n",
- "sns.heatmap(feature_corr, annot=True, cmap='coolwarm')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Die Korrelationsmatrix zeigt, dass die Attribute \"Tenure\" und \"TotalCharges\" eine kritische positive Korrelation von über 0,8 aufweisen. Diese Beziehung wird später im Zusammenhang mit der Multikollinearität erneut untersucht und muss entfernt werden."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Streudiagramme mit kontinuierlichen Merkmalen und Ziel"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 157,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='tenure', ylabel='MonthlyCharges'>"
- ]
- },
- "execution_count": 157,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.scatterplot(data=data_no_mv, x=\"tenure\", y=\"MonthlyCharges\", hue=\"Churn\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Das Streudiagramm deutet darauf hin, dass Kunden im oberen linken Bereich, d. h. Kunden mit hohen monatlichen Kosten und kurzer Betriebszugehörigkeit, am ehesten abwandern."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 158,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='tenure', ylabel='TotalCharges'>"
- ]
- },
- "execution_count": 158,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.scatterplot(data=data_no_mv, x=\"tenure\", y=\"TotalCharges\", hue=\"Churn\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Es besteht eine rein logische, lineare Beziehung zwischen der Dauer der Betriebszugehörigkeit und den in Rechnung gestellten Gesamtkosten. Je länger eine Person Kunde ist, desto mehr monatliche Beträge musste sie bereits zahlen."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 2.4.2. Kategorische Merkmale"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Abwanderung (Ziel)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Zunächst wird die Verteilung der Zielvariablen Churn untersucht."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 159,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 1.0, 'Abwanderung im Vergleich zur Nicht-Abwanderung')"
- ]
- },
- "execution_count": 159,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# produce pie chart for churn\n",
- "# generate procentage relationship\n",
- "churn_rate = data_no_mv.Churn.value_counts() / len(data_no_mv.Churn)\n",
- "\n",
- "# Plot\n",
- "labels = 'Keine Abwanderung', 'Abwanderung'\n",
- "fig, ax = plt.subplots()\n",
- "ax.pie(churn_rate, labels=labels, autopct='%.f%%') \n",
- "ax.set_title('Abwanderung im Vergleich zur Nicht-Abwanderung')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "- Die Abwanderungen machen etwa 27 % des gesamten Datensatzes aus, während die Nicht-Abwanderungen etwa 73 % ausmachen.\n",
- "- Dies ist ein unausgewogener Datensatz und eine andere Metrik muss in der Bewertungsphase verwendet werden.\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Geschlecht"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 160,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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79rFq1Sqys7PJzs6mX79+ACQlJTFz5kw++OADXC4XGRkZAKSlpREfH09ubi49evRg2bJlRt+XiIh4wCVD48SJE5w4cYJHHnmEkydPupfLy8uZNGnSJTvOyMggNTUVq9UKwJkzZyguLiY5OZnY2FiWLFmC0+nk6NGjVFdXExERAUBcXBy5ubnU1taSn5/PgAED6rWLiIj3mC/14tSpU/n4448BiIqKOreR2ez+Y34xs2fPrrdcXl5O7969SU1NpW3btkyYMIHMzEy6deuGxWJxr2exWCgtLaWiooKAgADMZnO9dhER8Z5LhsaKFSuAb85FzJ0796p2dPPNN7N06VL38pgxY8jKyqJr166YTCZ3u8vlwmQyuf//vh8uGxEYGHDlRct5LJa23i5B5II0NpvGJUPjO3PnzuXo0aOcPHkSl8vlbg8PDze8o/3791NUVOQ+QnG5XJjNZoKCgrDZbO71ysvLsVqtdOjQgcrKShwOB76+vthsNvdUV0McO2bH6XRdfsWL0ECsz2ar9HYJ8i2Nzfo0NhuHj4/pkh+2DYXGkiVLWLFiBYGBge42k8nEtm3bDBficrmYM2cOvXv3pnXr1qxbt45hw4YREhKCv78/BQUF9OrVi+zsbKKjo/Hz8yMyMpKcnBxiY2PJysoiOjra8P5ERKTxGQqNrKwsNm/eTKdOna54R2FhYTz66KOMGjWKuro6+vfvz5AhQwBIT08nJSUFu91OeHg4iYmJAKSmpjJ9+nSWL19OcHAwCxcuvOL9i4jI1TMUGsHBwVccGB9++KH754SEBBISEs5bJywsjMzMzPPaQ0JCWLly5RXtV0REGp+h0OjTpw/z58/n3nvvpWXLlu72hpzTEBGRa5+h0Fi/fj1AvfskGnpOQ0RErn2GQuP7U0wiInLjMhQab7755gXbf/vb3zZqMSIi0rwZCo3PP//c/fPZs2fJz8+nT58+HitKRESaJ8M3931faWkpM2bM8EhBIiLSfF3Ro9E7derE0aNHG7sWERFp5hp8TsPlcrFv3756d4eLiMiNocHnNOCbm/2mTZvmkYJERKT5atA5jaNHj1JXV0doaKhHixIRkebJUGgcPnyYxx9/nLKyMpxOJ+3bt+fVV1+la9eunq5PRESaEUMnwmfNmsX48ePJz8+noKCAiRMnkpaW5unaRESkmTEUGseOHWPYsGHu5eHDh1NRUeGxokREpHkyFBoOh4MTJ064l48fP+6pekREpBkzdE5j9OjRjBgxgkGDBmEymcjJyWHs2LGerk1ERJoZQ0caMTExANTW1vLll19SWlpKv379PFqYiIg0P4aONKZPn05CQgKJiYnU1NTwzjvvkJyczGuvvebp+kREpBkxdKRRUVHh/gpWf39/xo0bh81m82hhIiLS/Bg+EV5aWupeLi8vx+VyeawoERFpngxNT40bN44HHniAu+++G5PJRF5enh4jIiJyAzIUGg8++CA9evRg165d+Pr68vDDD/PTn/7U07WJiEgzYyg0AMLCwggLC/NkLSIi0sxd0fdpiIjIjUmhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMcyjoWG32xkyZAhHjhwBIC8vj9jYWPr378+iRYvc6xUWFhIXF8eAAQOYMWMGdXV1ABQXF5OQkMDAgQOZOHEiVVVVnixXREQuw2OhsWfPHkaNGkVRUREA1dXVJCcns2zZMnJycti3bx/bt28HICkpiZkzZ/LBBx/gcrnIyMgAIC0tjfj4eHJzc+nRowfLli3zVLkiImKAx0IjIyOD1NRUrFYrAHv37iU0NJSbb74Zs9lMbGwsubm5HD16lOrqaiIiIgCIi4sjNzeX2tpa8vPzGTBgQL12ERHxHsMPLGyo2bNn11suKyvDYrG4l61WK6Wlpee1WywWSktLqaioICAgALPZXK+9oQIDA67wHciFWCxtvV2CyAVpbDYNj4XGDzmdTkwmk3vZ5XJhMpku2v7d/9/3w2Ujjh2z43Re+RdGaSDWZ7NVersE+ZbGZn0am43Dx8d0yQ/bTXb1VFBQUL2viLXZbFit1vPay8vLsVqtdOjQgcrKShwOR731RUTEe5osNG677TYOHTrE4cOHcTgcbNy4kejoaEJCQvD396egoACA7OxsoqOj8fPzIzIykpycHACysrKIjo5uqnJFROQCmmx6yt/fn3nz5vHkk09SU1NDTEwMAwcOBCA9PZ2UlBTsdjvh4eEkJiYCkJqayvTp01m+fDnBwcEsXLiwqcoVEZEL8HhofPjhh+6f+/Tpw7vvvnveOmFhYWRmZp7XHhISwsqVKz1anxjnrKvVPPq36s7WUHHyrLfLEGlyTXakIdc+H7MfBfPHe7uMZqHXtNcBhUZzoQ8053j6A41CQ0SuefpAc46nP9Do2VMiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMczsjZ2OGTOG48ePYzZ/s/tZs2ZRVVXF3LlzqampYdCgQUyZMgWAwsJCZsyYQVVVFZGRkaSlpbm3ExGRptXkf31dLhdFRUX87W9/c//xr66uZuDAgaxcuZLg4GAmTJjA9u3biYmJISkpiRdeeIGIiAiSk5PJyMggPj6+qcsWERG8MD118OBBAH73u99x//33s2rVKvbu3UtoaCg333wzZrOZ2NhYcnNzOXr0KNXV1URERAAQFxdHbm5uU5csIiLfavLQOHXqFH369GHp0qW89dZbrF27luLiYiwWi3sdq9VKaWkpZWVl9dotFgulpaVNXbKIiHyryaenevbsSc+ePd3LDz74IEuWLKFXr17uNpfLhclkwul0YjKZzmtviMDAgKsvWuQCLJa23i5B5II8OTabPDQ++eQTamtr6dOnD/BNEISEhGCz2dzr2Gw2rFYrQUFB9drLy8uxWq0N2t+xY3acTtcV16s/DHIxNlulV/evsSkXczVj08fHdMkP200+PVVZWcn8+fOpqanBbrezYcMGnn76aQ4dOsThw4dxOBxs3LiR6OhoQkJC8Pf3p6CgAIDs7Gyio6ObumQREflWkx9p3HPPPezZs4cHHngAp9NJfHw8PXv2ZN68eTz55JPU1NQQExPDwIEDAUhPTyclJQW73U54eDiJiYlNXbKIiHzLKzc8TJ48mcmTJ9dr69OnD+++++5564aFhZGZmdlElYmIyKXojnARETFMoSEiIoYpNERExDCFhoiIGKbQEBERwxQaIiJimEJDREQMU2iIiIhhCg0RETFMoSEiIoYpNERExDCFhoiIGKbQEBERwxQaIiJimEJDREQMU2iIiIhhCg0RETFMoSEiIoYpNERExDCFhoiIGKbQEBERwxQaIiJimEJDREQMU2iIiIhhCg0RETFMoSEiIoYpNERExDCFhoiIGKbQEBERwxQaIiJimEJDREQMuyZC47333uO+++6jf//+rF692tvliIjcsMzeLuBySktLWbRoEevXr6dFixaMHDmSqKgofvKTn3i7NBGRG06zD428vDx69+5Nu3btABgwYAC5ublMmjTJ0PY+PqarrqFj+zZX3cf1osVNgd4uodlojLF1tTQ2z9HYPOdqxubltjW5XC7XFffeBF599VVOnz7NlClTAPjrX//K3r17ef75571cmYjIjafZn9NwOp2YTOeSz+Vy1VsWEZGm0+xDIygoCJvN5l622WxYrVYvViQicuNq9qFx5513snPnTo4fP86ZM2fYvHkz0dHR3i5LROSG1OxPhHfq1IkpU6aQmJhIbW0tDz74ILfeequ3yxIRuSE1+xPhIiLSfDT76SkREWk+FBoiImKYQkNERAxTaIiIiGEKjRvAkSNH6NGjB0OHDq33r6SkxCP76tu3b6P3KzeeI0eO0L17d2bOnFmvvbCwkO7du7N+/fqLbtu3b1+OHDni6RJvSM3+kltpHFarlezsbG+XIdIg7dq146OPPsLhcODr6wtATk4OHTp08HJlNy6Fxg2svLycmTNn8vXXX2MymZg6dSp33nknL7/8MsXFxRQVFXH8+HEmTpzIzp072bNnD2FhYSxatAiHw8Fzzz3HgQMHKC8vp3v37ixcuNBQ/yJGtWnThrCwMPLz8+nduzcAH3/8sXscrVq1iuzsbM6cOYOfnx8LFiygS5cu7u0dDgfz589n9+7dOBwO4uLiGDdunDfeynVDoXGDKCsrY+jQoe7l2NhYPvvsM4YPH869995LWVkZ8fHxZGVlAfD555+zbt06/vnPfzJ27Fjee+89fvzjH3Pfffexf/9+Kisr8fPzY926dTidTsaOHcv27dsJDw9372P27NkX7D8gIKCp375cwwYNGsQHH3xA79692bt3L927d8flcmG32/nwww9ZuXIlLVu2ZPHixaxevZpnn33WvW1GRgYAGzZs4OzZszz88MP06NGDyMhIb72da55C4wZxoempqKgoDh48yJIlSwCoq6vjq6++AuBXv/oVZrOZzp07Y7FY3N9f0qlTJ06ePElUVBTt2rVj9erVHDx4kKKiIk6fPl2v/7y8vAv2/7Of/czTb1euI3379uWll17C6XSyadMmBg0aRE5ODgEBASxYsID333+foqIiPvroo/PG1s6dOyksLGTXrl0AnD59mv379ys0roJC4wbmdDp5++233d9VUlZWRmBgIFu3bsXPz8+9ntl8/jDZtm0bS5YsITExkbi4OCoqKvjhwwUu1r9IQ3w3RVVQUMCuXbuYOnUqOTk5lJSUMGLECEaPHk10dDQdO3aksLCw3rYOh4OkpCT69+8PwPHjx2nTRt9BcjV09dQNrHfv3qxZswaAL774gtjYWM6cOWNo2507dzJo0CCGDx/OTTfdxD/+8Q8cDkej9S/yfYMGDWLBggX06NHD/SGmdevWhIaGMm7cOH7xi1+wdevWC47BjIwMamtrqaqqIj4+nn//+99eeAfXDx1p3MBSUlKYOXMmsbGxAMyfP9/w+YaHHnqIP/zhD7z//vv4+fnxy1/+8rxLHK+mf5Hvu+eee5gxYwa///3v3W1+fn44nU7uu+8+XC4Xt99+OwcOHKi33ciRIzl8+DDDhg2jrq6OuLg4oqKimrr864oeWCgiIoZpekpERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGSDORm5vLmDFjvF2GyCUpNERExDDd3CfSQH/+85/JzMykTZs2REZGsm3bNnJzc0lPTyc/Px+Hw8HPf/5zUlJSCAgIoG/fvgwbNoydO3dSUlLC0KFDmTx5MgCLFy/mvffeo127doSGhrr3cfbs2Uv2d+utt7J//36efvpp+vXr56XfhNyIdKQh0gAfffQR69evJzMzk/Xr11NVVQV8EyS+vr6sX7+ed999F6vVSnp6unu706dPs2bNGtauXcsbb7zBV199xdatW9m8eTNZWVmsXbsWu93uXv9y/XXr1o1NmzYpMKTJ6UhDpAG2b9/OwIEDuemmmwBISEhg165d/P3vf6eyspK8vDwAamtr6z2c8d577wW+eUpwYGAgJ0+eZOfOnfTr18/9aJXhw4ezcuVKgMv2p6e0ircoNEQawGw213ua73ffJud0OklOTiYmJgaAqqoqampq3Ov5+/u7fzaZTO4+LtSXkf5at27dmG9LxDBNT4k0QExMDJs3b6ayshKAzMxMAO666y5Wr17N2bNncTqdPPvss+d9k+EPRUdHk5uby6lTp3A6nfW+7+RK+hNpCgoNkQbo06cPv/nNbxgxYgRxcXFUVlbSqlUrHn/8cUJCQhg2bJj7qavTp0+/ZF8xMTEMHz6c4cOH89BDD9G2bVv3a1fSn0hT0FNuRRrg008/5V//+heJiYkAvPnmm+zZs4eXXnrJu4WJNBGFhkgD2O12kpOTOXjwICaTieDgYJ5//nk6derk7dJEmoRCQ0REDNM5DRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImLY/wLqjLpwL4cdqQAAAABJRU5ErkJggg==",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"gender\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Die Abwanderungsrate zwischen Männern und Frauen ist ungefähr gleich hoch."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Senioren"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 161,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"SeniorCitizen\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Bei Kunden, die als Senioren eingestuft werden, ist die Wahrscheinlichkeit höher, dass sie abwandern."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Partner"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 162,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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UcOzYMVwul7s9MjLyguvZ7XbGjRvHyy+/zI033sisWbMoLCwkKCgIgGnTpjF48GD2799PSkoK1dXVREdHk56ejtlsprS0lMTERA4fPkyXLl3IyMigdevWl/FyRUTkchgKjRUrVrB69Wo6dOjgbjOZTHzwwQfnXWfv3r2kpqZSVFTkbtu3bx9r167FarU2WDYxMZFnn32Wnj17Mnv2bLKysoiLiyM9PZ24uDhGjBjBypUryczMJDExsZEvUUREmoqhT09lZ2fz/vvvs3PnTvfXhQIDICsri7S0NHdAnDx5ktLSUmbPnk1sbCwrVqzA6XRSUlJCTU0NPXv2BGD06NHk5eVRV1dHQUGBe+zkdLuIiHiPoTONsLAwOnbs2KgNL1iwoMF0RUUFffv2JS0tjZCQEKZMmcLGjRvp1q0bFovFvZzFYqGsrIzKykqCg4Mxm80N2hurQ4fgRq8j52exhHi7BJFz0rHZPAyFRr9+/Vi6dCmDBg0iMDDQ3X6xMY0zderUiZUrV7qnJ06cSHZ2NhEREZhMJne7y+XCZDK5v5/p59NGHD5sx+l0XXzB89CB2JDNVuXtEuRHOjYb0rHZNHx8TBd8s20oNDZv3gzQoHvoYmMaP/fll19SVFTk7m5yuVyYzWZCQ0Ox2Wzu5SoqKrBarbRv356qqiocDge+vr7YbLazxkJERKR5GQqNnTt3XvaOXC4XCxcupG/fvrRq1YoNGzYwatQowsPDCQgIoLCwkN69e5OTk0NMTAx+fn5ER0eTm5tLbGws2dnZxMTEXHYdIiJy6QyFxhtvvHHO9gceeMDwjnr06MEjjzzC+PHjqa+vZ8iQIYwcORKAjIwMUlNTsdvtREZGkpCQAEBaWhrJycmsWrWKsLAwli1bZnh/IiLS9AyFxldffeX++dSpUxQUFNCvXz9DOzjzLCU+Pp74+PizlunRowcbN248qz08PJw1a9YY2o+IiHie4Yv7zlRWVkZKSopHChIRkZbrkm6N3rFjR0pKSpq6FhERaeEaPabhcrnYt29fg6vDRUTk2tDoMQ344WK/pKQkjxQkIiItV6PGNEpKSqivr6dz584eLUpERFomQ6FRXFzMY489Rnl5OU6nk3bt2vHKK68QERHh6fpERKQFMTQQPm/ePCZPnkxBQQGFhYVMnTqV9PR0T9cmIiItjKHQOHz4MKNGjXJPjxkzhsrKSo8VJSIiLZOh0HA4HBw9etQ9feTIEU/VIyIiLZihMY0JEyZw//33c/fdd2MymcjNzeW3v/2tp2sTEZEWxtCZxsCBAwGoq6vjm2++oaysjMGDB3u0MBERaXkMnWkkJycTHx9PQkICtbW1vP3228yePZtXX33V0/WJiEgLYuhMo7Ky0n3n2YCAACZNmtTgGRgiInJtMDwQfuajVisqKnC5Lv1peCIicmUy1D01adIk7rvvPm6//XZMJhO7d+/WbURERK5BhkJj7NixREVFsWfPHnx9fXnooYfo3r27p2sTEZEWxlBowA8PSurRo4cnaxERkRbOcGiIiLRUzvo6LJYQb5fRItSfqqXy2CmPbV+hISJXPB+zH4VLJ3u7jBahd9JrgOdC45Ke3CciItcmhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBjm0dCw2+2MHDmSgwcPArB7925iY2MZMmQIL7zwgnu5/fv3M3r0aIYOHUpKSgr19fUAlJaWEh8fz7Bhw5g6dSrV1dWeLFdERC7CY6Gxd+9exo8fT1FREQA1NTXMnj2bzMxMcnNz2bdvH7t27QIgMTGRuXPnsn37dlwuF1lZWQCkp6cTFxdHXl4eUVFRZGZmeqpcERExwGOhkZWVRVpaGlarFYDPPvuMzp0706lTJ8xmM7GxseTl5VFSUkJNTQ09e/YEYPTo0eTl5VFXV0dBQQFDhw5t0C4iIt7jsbvcLliwoMF0eXk5FovFPW21WikrKzur3WKxUFZWRmVlJcHBwZjN5gbtjdWhQ/AlvgI5F91+WqTl8+TfabPdGt3pdGIymdzTLpcLk8l03vbT38/082kjDh+243Re+vPM9U+yIZutytslyI90bMr5XM7fqY+P6YJvtpvt01OhoaHYbDb3tM1mw2q1ntVeUVGB1Wqlffv2VFVV4XA4GiwvIiLe02yhccstt3DgwAGKi4txOBxs3bqVmJgYwsPDCQgIoLCwEICcnBxiYmLw8/MjOjqa3NxcALKzs4mJiWmuckVE5ByarXsqICCAxYsXM336dGpraxk4cCDDhg0DICMjg9TUVOx2O5GRkSQkJACQlpZGcnIyq1atIiwsjGXLljVXuSIicg4eD42dO3e6f+7Xrx/vvvvuWcv06NGDjRs3ntUeHh7OmjVrPFqfiIgYpyvCRUTEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGEKDRERMUyhISIihik0RETEMIWGiIgYptAQERHDPP6McLl6OOvrsFhCvF1Gi1B/qpbKY6e8XYZIs1NoiGE+Zj8Kl072dhktQu+k1wCFhlx71D0lIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExzCsX902cOJEjR45gNv+w+3nz5lFdXc2iRYuora3l7rvvZsaMGQDs37+flJQUqquriY6OJj093b2eiIg0r2b/7+tyuSgqKuIvf/mL+59/TU0Nw4YNY82aNYSFhTFlyhR27drFwIEDSUxM5Nlnn6Vnz57Mnj2brKws4uLimrtsERHBC91T3377LQAPPvgg99xzD2vXruWzzz6jc+fOdOrUCbPZTGxsLHl5eZSUlFBTU0PPnj0BGD16NHl5ec1dsoiI/KjZzzSOHz9Ov379mDNnDnV1dSQkJDB58mQsFot7GavVSllZGeXl5Q3aLRYLZWVljdpfhw7BTVa7yJl080ZpqTx5bDZ7aPTq1YtevXq5p8eOHcuKFSvo3bu3u83lcmEymXA6nZhMprPaG+PwYTtOp+uS69U/Bjkfm63Kq/vXsSnncznHpo+P6YJvtpu9e+rTTz/l448/dk+7XC7Cw8Ox2WzuNpvNhtVqJTQ0tEF7RUUFVqu1WesVEZGfNHtoVFVVsXTpUmpra7Hb7WzZsoWnnnqKAwcOUFxcjMPhYOvWrcTExBAeHk5AQACFhYUA5OTkEBMT09wli4jIj5q9e+rOO+9k79693HfffTidTuLi4ujVqxeLFy9m+vTp1NbWMnDgQIYNGwZARkYGqamp2O12IiMjSUhIaO6SRUTkR1654OHJJ5/kySefbNDWr18/3n333bOW7dGjBxs3bmymykRE5EJ0RbiIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTKEhIiKGKTRERMQwhYaIiBim0BAREcMUGiIiYphCQ0REDFNoiIiIYVdEaPz5z39m+PDhDBkyhHXr1nm7HBGRa5bZ2wVcTFlZGS+88AKbN2/G39+fcePG0adPH37xi194uzQRkWtOiw+N3bt307dvX9q2bQvA0KFDycvLY9q0aYbW9/ExXXYN17drfdnbuFr4t+ng7RJajKY4ti6Xjs2f6Nj8yeUcmxdb1+RyuVyXvPVm8Morr3DixAlmzJgBwJ/+9Cc+++wz5s+f7+XKRESuPS1+TMPpdGIy/ZR8LperwbSIiDSfFh8aoaGh2Gw297TNZsNqtXqxIhGRa1eLD43+/fvz8ccfc+TIEU6ePMn7779PTEyMt8sSEbkmtfiB8I4dOzJjxgwSEhKoq6tj7Nix3Hzzzd4uS0TkmtTiB8JFRKTlaPHdUyIi0nIoNERExDCFhoiIGKbQEBERwxQaAsC8efP43e9+16AtPz+fQYMGYbfbvVSVSEMHDx7kpptu4qOPPmrQftddd3Hw4EEvVXVtUWgIADNnzmTfvn188MEHAJw4cYJnnnmGhQsXEhwc7OXqRH7i5+fHnDlz9GbGSxQaAkDr1q159tlnmT9/PidOnGDFihXcddddBAUFMX78eEaNGsWDDz7I999/D8Abb7zBPffcw3333cfcuXO9XL1cS6xWK/3792fJkiVnzXv55ZcZPnw4sbGxLF68GIfD4YUKr24KDXHr378/AwYMYNasWXz00UdMnz6d1NRUnn/+ebZs2cIDDzzAnDlzcDgcvPLKK2zatInNmzdTV1dHWVmZt8uXa0hycjL5+fkNuqk+/PBDdu7cyaZNm9iyZQvFxcW88847Xqzy6tTirwiX5pWcnMwdd9zBypUrOXToEN9//z1Tp051z7fb7fj6+tKrVy/Gjh3LoEGDeOCBB+jYsaMXq5ZrTXBwMPPnz2fOnDm8++67AOzZs4cRI0YQFBQEwJgxY8jOziY+Pt6bpV51FBrSQHBwMG3atCE8PBy73c6NN95ITk4OAA6Hg4qKCgAyMzP5+9//zocffsjkyZPJyMjgtttu82bpco0ZMGBAg24qp9N51jL19fXNXdZVT91Tcl5du3bl2LFjfPrppwBs2rSJp59+miNHjjB8+HC6d+/OE088wa9//Wu+/PJLL1cr16LT3VTl5eX07duX9957j5qaGurr69m0aRN9+/b1dolXHZ1pyHn5+/uzfPlyFixYQG1tLcHBwSxZsoT27dtz//33M3bsWIKCgujSpQtjxozxdrlyDTrdTfXQQw9xxx13cPz4ccaMGUN9fT0DBgxgwoQJ3i7xqqMbFoqIiGHqnhIREcMUGiIiYphCQ0REDFNoiIiIYQoNERExTB+5FWmEgwcPMnjwYLp37+5uc7lcJCQkMHbsWMPb+etf/8revXt54oknPFGmiMcoNEQaKTAw0H2VPEBZWRkjR44kKiqKHj16GNrG559/zrFjxzxVoojHKDRELlPHjh3p3Lkz+/fv5/XXX6e4uJijR4/SunVrMjIy6Nq1KxMnTuS6667j22+/Zfjw4bzzzjs4HA5CQkLo3LkzO3bswMfHh+LiYgIDA1myZAkRERFUVVWxYMECvvrqK+rq6ujXrx9JSUmYzWaioqIYNGgQ//znP8nIyOBXv/qVt38Vcg3QmIbIZfrb3/7Gd999h8lkok2bNmzYsIHt27cTFRXFunXr3Mu1adOG3Nxcpk2bxrhx4xg+fDgzZswAoKCggDlz5rB161ZuueUW/vjHPwKwcOFCIiMj2bx5M9nZ2VRWVvLGG28AUFdXx5133sn27dsVGNJsdKYh0kg1NTXce++9wA83cWzXrh3PPfccAwcOpFu3bqxZs4bi4mI++eQTevXq5V4vOjr6vNuMjIwkNDQUgF/+8pfs2LED+GHs4/PPP2fjxo3ufZ/pQtsU8QSFhkgj/XxM47T169eTlZVFfHw8sbGxtG3btsEjSFu1anXBbZ5mMpk4fXcfp9PJ8uXLiYiIAOD48eOYTCZD2xTxBHVPiTSR/Px8Ro0axW9+8xu6dOnCzp07z/vkOF9fX0O37R4wYABvvvkmLpeLU6dOMXXqVNauXdvUpYsYptAQaSIPPvggGzZsIDY2lvj4eCIjI/nuu+/OuWzfvn3Jz89n/vz5F9xmSkoKJ06cIDY2ltjYWLp3787kyZM9Ub6IIbrLrYiIGKYzDRERMUyhISIihik0RETEMIWGiIgYptAQERHDFBoiImKYQkNERAxTaIiIiGH/H/L5ypVCM+aPAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"Partner\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Kunden, die keinen Partner haben, sind eher bereit, abzuwandern."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Angehörige"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 163,
- "metadata": {},
- "outputs": [
- {
- "data": {
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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"Dependents\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Kunden, die Verwandte haben, sind eher bereit, abzuwandern."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Mehrere Anschlüsse"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 164,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"MultipleLines\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Bei Kunden, die mehrere Anschlüsse haben, ist die Wahrscheinlichkeit einer Abwanderung geringer."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Internet Service"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 165,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"InternetService\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Wenn ein Kunde einen Glasfaseranschluss hat, ist es wahrscheinlicher, dass er ausfällt als ein Kunde mit DSL."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Online-Sicherheit"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 166,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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D8fHxISwsjC1btgCQkpJCeHh4TZUrIiLnUWNnGq1ateKxxx5j2LBhVFRU0LNnT/r27QtAUlISU6dOpaioiDZt2jBixAgAEhISiIuL49VXXyU4OJiFCxfWVLkiInIeFqM+9vt7lqo0T0VPWlMDFVWf1bMfwMvq4+kyqqSirPSXYTOrV108fmvnx6h5SmqdSzVPqZfbOszL6kPG/NGeLqNK7pz0OhpnWaTuUjciIiJimkJDRERMU2iIiIhpuqYhIh7X6MoGNPCrOzd1lJSWU3iixNNleIRCQ0Q8roGfT526+23t/BgKuTxDQ81TIiJimkJDRERMU2iIiIhpCg0RETFNoSEiIqYpNERExDSFhoiImKbQEBER0xQaIiJimkJDRERMc2toFBUV0bdvXw4dOgTA+vXr6du3L5GRkUyePJmystPjKixZsoRu3brRv39/+vfvz5o1p7sTyM7OJiYmhl69ejF27FiKi4vdWa6IiFyC20Jj9+7dDBs2jMzMTAAOHDjAypUrWbduHZs3b8bpdLJ27VoA9uzZw8KFC0lNTSU1NZWYmBgAEhMTiY6OJi0tjbZt27Js2TJ3lSsiIia4LTSSk5NJSEggMDAQAF9fXxISEggICMBisXDLLbeQnZ0NnA6N5cuXExkZyYwZMygtLaW8vJydO3cSEREBQFRUFGlpae4qV0RETHBbaMyaNYuwsDDXdEhICJ06dQLg2LFjrFmzhu7du1NcXEzr1q2JjY3l3Xff5cSJEyxbtozjx48TEBCA1Xq6I16bzUZOTo67yhURERNqvGv0nJwcRo8ezcCBA2nfvj0AK1ascD0+atQo4uPjiY6OxmKxVFr319NmXGyAdPEMm62Rp0uoNfRa1F2X67Gr0dDYv38/o0ePZvjw4YwaNQo4fbE7PT2dQYMGAWAYBlarlaZNm1JYWIjD4cDb2xu73e5q6qqKo0eLcDqNSy53ub4BPMFuL6z2bdbV4+eO16IuqovHr74eOy8vy0W/bNfYLbdFRUU88sgjPPXUU67AAGjQoAELFizg4MGDGIbBmjVr6NGjBz4+PoSFhbFlyxYAUlJSCA8Pr6lyRUTkPGosNDZu3EheXh5vvvmm69baRYsW0bRpU2bMmMHYsWPp1asXhmHw8MMPA5CQkEBycjK9e/dm165dPP300zVVroiInIfbm6c++eQTAEaOHMnIkSPPu0xERITrLqmzhYSEsGrVKneWJyIiVaBfhIuIiGmmQuN8t7r+9NNP1V6MiIjUbhcNjfz8fPLz83n00UcpKChwTefl5TF+/PiaqlFERGqJi17TePbZZ/niiy8AXL+pALBaree9BiEiIvXbRUNj5cqVAEyePJk5c+bUSEEiIlJ7mbp7as6cORw+fJiCggIM4z8/lGvTpo3bChMRkdrHVGgsXryYlStX0qxZM9c8i8XCxx9/7LbCRESk9jEVGikpKXz44Yc0b97c3fWIiEgtZuqW2+DgYAWGiIiYO9Po2LEj8+fPp3v37jRo0MA1X9c0REQuL6ZC45133gGoNAiSrmmIiFx+TIXGmf6jRETk8mYqNN58883zzj/TG62IiFweTIXGDz/84Pq7rKyMnTt30rFjR7cVJSIitZPpH/edLScnhylTprilIBERqb1+U9fozZs35/Dhw9Vdi4iI1HJVvqZhGAZ79uyp9OtwERG5PJg60/jhhx9c//34448EBweTlJR0yfWKioro27cvhw4dAiA9PZ3IyEh69uzJSy+95Fpu7969REVFERERwZQpU6ioqAAgOzubmJgYevXqxdixYykuLv4tz1FERKqJqdCYM2cOc+bMYfz48Tz++OPExsYSFBR00XV2797NsGHDyMzMBKCkpIT4+HiWLVvGli1b2LNnD5999hkAsbGx/OUvf+GDDz7AMAySk5MBSExMJDo6mrS0NNq2bcuyZct+x1MVEZHfy1RoZGVl0adPHwYMGEBUVBR//vOf2b9//0XXSU5OJiEhgcDAQAC++eYbrrvuOlq0aIHVaiUyMpK0tDQOHz5MSUkJoaGhAERFRZGWlkZ5eTk7d+50jdtxZr6IiHiOqWsaM2bMYPTo0dx///0AbNq0icTERN56660LrjNr1qxK07m5udhsNtd0YGAgOTk558y32Wzk5ORw/PhxAgICsFqtleaLiIjnmAqNo0ePugIDYODAgfztb3+r0o6cTicWi8U1bRgGFovlgvPP/Hu2X0+b0axZQJXXEfey2Rp5uoRaQ69F3XW5HjtToeFwOMjPz6dx48YAHDt2rMo7CgoKwm63u6btdjuBgYHnzM/LyyMwMJCmTZtSWFiIw+HA29vbtXxVHT1ahNNpXHK5y/UN4Al2e2G1b7OuHj93vBZ1UV08fvX12Hl5WS76ZdvUNY0HH3yQIUOG8PLLL7No0SKGDRvGsGHDqlTI7bffzoEDB8jKysLhcPDee+8RHh5OSEgIfn5+ZGRkAJCamkp4eDg+Pj6EhYWxZcsW4PSYHuHh4VXap4iIVC9TodGlSxcAysvL2b9/Pzk5OfTo0aNKO/Lz82Pu3Lk8+eST9O7dmxtuuIFevXoBkJSUxJw5c+jVqxcnT55kxIgRACQkJJCcnEzv3r3ZtWsXTz/9dJX2KSIi1ctU81RcXBwxMTGMGDGC0tJS3n77beLj41mxYsUl1z27h9yOHTuyefPmc5Zp1aoVGzduPGd+SEgIq1atMlOiiIjUAFNnGsePH3d9+/fz82PkyJGVrkOIiMjlwVRoOByOSre75uXlYRiXvrgsIiL1i6nmqZEjRzJgwADuueceLBYL6enpTJo0yd21iYhILWMqNAYNGkTbtm3Zvn073t7ePPLII9xyyy3urk1ERGoZU6EBpy9Wt2rVyp21iIhILfebxtMQEZHLk0JDRERMU2iIiIhpCg0RETFNoSEiIqYpNERExDSFhoiImKbQEBER0xQaIiJimkJDRERMU2iIiIhppvueqi4bNmxg9erVrulDhw7Rv39/Tp06RUZGBv7+/gCMHz+eHj16sHfvXqZMmUJxcTFhYWEkJiZitdZ42SIiggdCY/DgwQwePBiAH3/8kXHjxjF+/HgeeughVq9eTWBgYKXlY2NjmTlzJqGhocTHx5OcnEx0dHRNly0iIni4eWr69Ok888wz+Pv7k52dTXx8PJGRkSxevBin08nhw4cpKSkhNDQUgKioKNLS0jxZsojIZc1joZGenk5JSQn33XcfeXl5dOjQgdmzZ5OcnMyuXbvYuHEjubm52Gw21zo2m63SCIIiIlKzPHZxYN26dTz88MMAtGjRgqVLl7oeGz58OCkpKdx4441YLBbXfMMwKk2b0axZQPUULNXGZmvk6RJqDb0Wddfleuw8EhplZWXs3LmTuXPnArBv3z4yMzOJiIgAToeD1WolKCgIu93uWi8vL++cax6XcvRoEU7npcczv1zfAJ5gtxdW+zbr6vFzx2tRF9XF41dfj52Xl+WiX7Y90jy1b98+rr/+eq644grgdEjMnj2bgoICysvLWb9+PT169CAkJAQ/Pz8yMjIASE1NJTw83BMli4gIHjrTOHjwIEFBQa7pVq1a8dhjjzFs2DAqKiro2bMnffv2BSApKYmpU6dSVFREmzZtGDFihCdKFhERPBQavXv3pnfv3pXmxcTEEBMTc86yrVq1YuPGjTVVmoiIXIR+ES4iIqYpNERExDSFhoiImKbQEBER0xQaIiJimkJDRERMU2iIiIhpCg0RETFNoSEiIqYpNERExDSFhoiImKbQEBER0xQaIiJimkJDRERMU2iIiIhpCg0RETFNoSEiIqZ5ZOS+4cOHc+zYMazW07ufMWMGxcXFzJkzh9LSUu677z6eeeYZAPbu3cuUKVMoLi4mLCyMxMRE13oiIlKzavzT1zAMMjMz+de//uX68C8pKaFXr16sWrWK4OBgxowZw2effUaXLl2IjY1l5syZhIaGEh8fT3JyMtHR0TVdtoiI4IHmqX//+98AjBo1in79+rF69Wq++eYbrrvuOlq0aIHVaiUyMpK0tDQOHz5MSUkJoaGhAERFRZGWllbTJYuIyC9q/EzjxIkTdOzYkWnTplFeXs6IESMYPXo0NpvNtUxgYCA5OTnk5uZWmm+z2cjJyanS/po1C6i22qV62GyNPF1CraHXou66XI9djYdGu3btaNeunWt60KBBLF68mDvvvNM1zzAMLBYLTqcTi8VyzvyqOHq0CKfTuORyl+sbwBPs9sJq32ZdPX7ueC3qorp4/OrrsfPyslz0y3aNN0/t2rWLL7/80jVtGAYhISHY7XbXPLvdTmBgIEFBQZXm5+XlERgYWKP1iojIf9R4aBQWFjJ//nxKS0spKiri3XffZeLEiRw4cICsrCwcDgfvvfce4eHhhISE4OfnR0ZGBgCpqamEh4fXdMkiIvKLGm+e6tatG7t372bAgAE4nU6io6Np164dc+fO5cknn6S0tJQuXbrQq1cvAJKSkpg6dSpFRUW0adOGESNG1HTJIiLyC4/84OHpp5/m6aefrjSvY8eObN68+ZxlW7VqxcaNG2uoMhERuRj9IlxERExTaIiIiGkKDRERMU2hISIipik0RETENIWGiIiYptAQERHTFBoiImKaQkNERExTaIiIiGkKDRERMU2hISIipik0RETENIWGiIiYptAQERHTPDKehoiAs6K8To2NXVFWyvGCMk+XIR7mkdBYsmQJW7duBaBLly5MmjSJyZMnk5GRgb+/PwDjx4+nR48e7N27lylTplBcXExYWBiJiYlYrco6qfu8rD5kzB/t6TJMu3PS64BC43JX45++6enpbNu2jXfffReLxcLo0aP56KOP2LNnD6tXryYwMLDS8rGxscycOZPQ0FDi4+NJTk4mOjq6pssWERE8cE3DZrMRFxeHr68vPj4+3HjjjWRnZ5OdnU18fDyRkZEsXrwYp9PJ4cOHKSkpITQ0FICoqCjS0tJqumQREflFjZ9p3Hzzza6/MzMz2bp1K2vWrGHHjh0kJCTQqFEjxowZw8aNG7n55pux2Wyu5W02Gzk5OTVdsoiI/MJjFwd+/PFHxowZw6RJk7jhhhtYunSp67Hhw4eTkpLCjTfeiMVicc03DKPStBnNmgVUW81SPerSxV+pTMfuPy7X18IjoZGRkcGECROIj4+nT58+7Nu3j8zMTCIiIoDT4WC1WgkKCsJut7vWy8vLO+eax6UcPVqE02lccrnL9Q3gCXZ7YbVvU8evZrjj2EHdPH7uei08zcvLctEv2zV+TePIkSOMGzeOpKQk+vTpA5wOidmzZ1NQUEB5eTnr16+nR48ehISE4OfnR0ZGBgCpqamEh4fXdMkiIvKLGj/TWLlyJaWlpcydO9c1b+jQoTz22GMMGzaMiooKevbsSd++fQFISkpi6tSpFBUV0aZNG0aMGFHTJYuIyC9qPDSmTp3K1KlTz/tYTEzMOfNatWrFxo0b3V2WiIiYoG5ERETENIWGiIiYptAQERHTFBoiImKaQkNERExTaIiIiGkKDRERMU2hISIipik0RETENA2BJyJSRXVtqF6ovuF6FRoiIlVU14bqheobrlfNUyIiYppCQ0RETFNoiIiIaQoNERExTaEhIiKmKTRERMS0OhEa//jHP+jduzc9e/ZkzZo1ni5HROSyVet/p5GTk8NLL73EO++8g6+vL0OHDqV9+/bcdNNNni5NROSyU+tDIz09nQ4dOtC4cWMAIiIiSEtLY/z48abW9/KymN7X1U0a/pYSPcr3ymaeLqHKqnJMqkLHz/3cdeyg7h2/unbswNzxu9QyFsMwjOoqyB2WL1/OyZMneeaZZwDYsGED33zzDS+88IKHKxMRufzU+msaTqcTi+U/yWcYRqVpERGpObU+NIKCgrDb7a5pu91OYGCgBysSEbl81frQuPvuu/nyyy85duwYp06d4sMPPyQ8PNzTZYmIXJZq/YXw5s2b88wzzzBixAjKy8sZNGgQt912m6fLEhG5LNX6C+EiIlJ71PrmKRERqT0UGiIiYppCQ0RETFNoiIiIaQqNOuDQoUP813/9F1988UWl+ffeey+HDh3yUFVyMTNmzGDChAmV5m3bto3u3btTVFTkoapqn+p4b+fk5PDoo49edJlvvvmGBQsW/OY6zSgsLGTcuHFu3ccZH3/8MYsWLaqRff2aQqOO8PHxYdq0afrAqSOeffZZ9uzZw8cffwzAyZMnmT59OrNnzyYgIMDD1dUuv/e93bx5c1asWHHRZX766SeOHj36m7ZvVkFBAXv37nXrPs7o3r07Tz31VI3s69dq/e805LTAwEDuvvtu5s2bd06/W6+99hqbN2/G29ubTp06ERsbi7e3t4cqFYCGDRsyc+ZM4uPj6dixI4sXL+bee+/F39+fYcOGUVJSQpMmTUhMTKRFixa8+eabvPvuu3h5eXHbbbcxY8YMTz+FGnOx9zZc+v196NAhRowYwSeffEJcXBwBAQF8++235OTkMG7cOHr06MHixYs5efIkr776Ko899hjz589nx44dOBwOoqKiGDlyJF999RULFizA6XRy8803c+2115KTk0NWVhaHDx9m8ODBjB07FofDcd71Z86cSW5uLuPGjWPp0qWu+oqKipg4cSJ5eXkAjBs3ju7du5OVlcX06dPJz8+nQYMGTJs2jVtvvZW4uDjy8/PJysri2WefZePGjbz22msArFq1iqysLG699VZ27NjB3LlzSU9PZ+7cuRiGwTXXXMOLL76Iv7//eWusFobUegcPHjS6detmFBYWGl27djW2bdtmGIZhdOvWzVizZo0xePBg4+TJk0Z5ebnx+OOPG6tXr/ZwxXLGlClTjAkTJhh9+/Y1Tpw4YURGRhqHDx82DMMwPv/8c+Ohhx4yKioqjPbt2xtlZWWGw+Ew4uLijJ9//tnDldeMi723Dx48aHz66aeXfH+f2YZhGMbzzz9vjBs3znA6ncb3339v3HXXXYZhGMamTZuM559/3jAMw1i7dq0xe/ZswzAMo7S01HjwwQeNnTt3Gtu3bzfuvPNO48SJE4ZhGMbixYuNQYMGGaWlpUZeXp4RGhpqFBQUXHD9s+s42zvvvGNMnz7dMAzD+O6774y5c+cahmEYQ4YMMb799lvDMAzjxx9/NHr27Ol6DmdqLSsrMzp16mTk5+e71tm9e7fr+ZSWlhodO3Y0vvvuO8MwDCMpKcl46623LlhjddCZRh0SEBDACy+8wLRp09i8eTMA27dvp0+fPvj7+wMwcOBAUlJSiImJ8WSp8ou4uDi6du3K0qVLOXLkCAcPHmTs2LGux4uKivD29qZdu3YMGjSI7t278/DDD9O8eXMPVl3zzvfeht/2/u7UqRMWi4VbbrmF/Pz8cx7/8ssv2bt3L9u3bwdONx3u27ePm266iZYtW9KoUSPXsu3bt8fX15dmzZrRuHFjCgsLL7h+UFDQeetp164dCxcuJCcnh65duzJu3DiKi4vZs2cPkydPdi138uRJjh8/DuDq9cLHx4cePXrw4Ycf0qlTJ/Lz87ntttv46aefANi3bx/NmzendevWwOlmUYAJEyact8awsLALvm5mKTTqmM6dO7tO5eF0L8C/VlFRUdNlyQUEBARw5ZVXEhISQlFREddeey2pqakAOBwOV5PFsmXL+Prrr/n8888ZPXo0SUlJ3HXXXZ4svcb9+r0Nv+397efnB3DB3rAdDgexsbH07NkTgGPHjtGwYUO+/vprGjRocN5tndmeYRgXXP/sjlXPdv3117N161b++7//m3/961+88cYbbNiwAV9fX9d7AeDnn392jRt0dh39+/dn0aJFFBQUEBkZWWnbPj4+lZ5nYWEhxcXFF6yxOuhCeB0UFxfHtm3byM3NpUOHDrz//vuUlJRQUVHBpk2b6NChg6dLlPO44YYbKCgoYNeuXQBs2rSJ5557jmPHjtG7d29uueUWnnrqKTp16sS+ffs8XK1nnP3eBqrt/e3t7e0Kmw4dOpCcnEx5eTnFxcVER0fz9ddfm97Whda3Wq3nDbTVq1fzyiuvcN9995GQkMCxY8cwDIPrr7/eFRpffPHFBc+eQkNDyc3NJTU1lX79+lV6rGXLlhw9etR15vH666/z9ttv/+7neDE606iDzpzKP/LII3Tt2pUTJ04wcOBAKioq6Ny5Mw8++KCnS5Tz8PX1ZdGiRcyaNYvS0lICAgKYN28eTZs2ZciQIQwaNAh/f39atmzJwIEDPV2uR5z93gbo1q0be/fu/d3v79tuu40lS5aQlJTEU089RVZWFvfffz8VFRVERUXRvn17vvrqK1PbGjp06HnXLy8v55prrmH48OGsWrXKtfyAAQOYOHEikZGReHt7Exsby5VXXsmCBQuYPn06r7/+Oj4+Prz00ksXPDu677772LZtGy1atKg038/PjwULFjBp0iTKy8v5wx/+wPz58/H19T1vjdVBHRaKiIhpap4SERHTFBoiImKaQkNERExTaIiIiGkKDRERMU2hIZeFt99+m379+tG7d2/69OlDbGws2dnZF13nq6++om/fvgAsWrSIlJSU31XDihUr6N+/P/369aNv377MmzePsrKy37VNM3Jychg6dCgABw8e5Mknn3T7PqX+0u80pN6bN28e33//PcuXLyc4OBin08nmzZsZMmQIGzZsuGD3D2f7vT2Kbt26lX/+85+sX7+eBg0aUFpayoQJE1iyZAkTJ078Xdu+lObNm7Nu3ToAsrOzOXDggFv3J/WbzjSkXvv5559Zt24dL7/8MsHBwQB4eXkxYMAAIiIiWL58Offeey+vvPIK0dHRdOvWjZdffvmc7cTFxbFy5UoA/vjHP/LKK68wdOhQ7r33XtauXetabsOGDURFRTFgwABGjhzJ/v37AbDb7TgcDkpKSoDTP8qaNm0af/7znwEoKytj9uzZ3H///fTr14+4uDhXV+EHDhxg+PDh9OnTh8jISLZs2QKcHnPif//3f137PjN96NAhunTpwqhRo4iIiOB//ud/aNeuHQ6Hg6lTp/J///d/PPLII7z66quuvooAdu3axYABA6rplZf6SqEh9dru3bu54YYbuOqqq8557O677yYjIwM43aHb2rVrWbduHW+88QYHDx684DbLyspo0qQJ69atY/HixcyZM4fS0lJ27NhBSkoKa9asISUlhdGjRzN+/HgA7r//fq688ko6d+7MkCFDmDt3LkeOHHF1TPfXv/4Vb29v3nnnHTZv3kxgYCBJSUkATJw4kV69evH+++/z17/+lYULF15y7Imff/6ZJ554gg8++ACbzQac7kpj5syZ/OEPf2DlypU88MADfPrpp65O/ZKTk13NWCIXouYpqfcu1MFdWVmZq9uG7t27A6ebcpo1a0ZBQcFFt3lm+TZt2lBWVsbJkyf59NNPycrKqvTBe+LECfLz82ncuLErjLZv386OHTt47LHHiI6OJjY2lk8//ZTCwkLS09MBKC8vp1mzZuTn5/P9998zePBgAIKDg/nnP/95yedstVoJDQ296DLNmjWja9eupKamMmDAALZt20ZCQsIlty2XN4WG1GuhoaFkZWVht9td37jP+Oqrr2jXrh2ff/75eXszvZhf96RqGAZOp5P+/fsTGxsLnO6hNTc3l6uuuooVK1Zw5513cscdd9CiRQsGDx7Mrl27ePTRR4mNjcXpdBIfH0+XLl0AKC4uprS0FKvVWmk/AP/+97+55pprXPs94+yL6r6+vq51LyYmJobp06djtVrp2bNntfWEKvWXmqekXmvevDnDhw9n4sSJ5OTkuOZv2rSJDz/88JJjS1dF586def/99109tL799ts89NBDAJSUlPDiiy9WGt/hhx9+4NZbb3Wtu2bNGsrKynA6nUybNo2FCxcSEBBAmzZtXHduHTlyhGHDhlFYWEjTpk3Zs2cPcDoAL9Q199m8vb0pLy93Td9xxx14eXmxcuVKNU2JKTrTkHrv2WefZcOGDYwdO5aysjLKysr44x//yLp16wgJCam2/XTu3JlHH32UUaNGYbFYCAgIYMmSJVgsFp544gksFgtDhw7FYrHgdDpp27at66L7E088wbx587j//vtxOBy0bt2auLg4AF588UUSExNZtWoVFouFWbNmYbPZeO6555g+fTrr16+nTZs2tGnT5pI13nTTTfj5+TFo0CA2bNiAxWIhKiqKLVu20KpVq2p7LaT+Ui+3IpexiooKxo8f7/oNi8ilqHlK5DL1008/0bFjR5o0aUKvXr08XY7UETrTEBER03SmISIipik0RETENIWGiIiYptAQERHTFBoiImKaQkNEREz7/6Pi1z5eRJfbAAAAAElFTkSuQmCC",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"OnlineSecurity\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Kunden, die den Internet-Sicherheitsdienst nicht nutzen, werden eher abwandern."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Online Backup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 167,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"OnlineBackup\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Personen, die keine Online-Datensicherung nutzen, sind eher bereit, umzuziehen."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Geräteschutz\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 168,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"DeviceProtection\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Kunden, die keinen zusätzlichen Geräteschutz erworben haben, werden mit größerer Wahrscheinlichkeit migrieren."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Technischer Support"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 169,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"TechSupport\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Kunden, die keinen technischen Support in Anspruch nehmen, werden eher abwandern."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Streaming-TV/ Streaming-Filme"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 170,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "for col in [\"StreamingTV\", \"StreamingMovies\"]:\n",
- " sns.countplot(x=col, hue='Churn', data=data_no_mv)\n",
- " plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Die Hinzunahme von Film- und TV-Streaming-Angeboten hat kaum Auswirkungen auf die Abwanderungsrate."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Papierlose Abrechnung"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 171,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"PaperlessBilling\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Kunden, die ohne Rechnung bezahlen, werden eher abwandern."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Zahlungsmethode"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 172,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"PaymentMethod\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Kunden, die mit elektronischen Schecks bezahlen, wandern deutlich häufiger ab als Kunden, die eine andere Zahlungsmethode verwenden."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Vertrag"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 173,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "sns.countplot(x=\"Contract\", hue=\"Churn\", data=data_no_mv)\n",
- "plt.show()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Bei Kunden mit kurzfristigen Verträgen ist die Wahrscheinlichkeit größer, dass sie das Unternehmen verlassen, als bei Kunden mit längerfristigen Verträgen."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenvorbereitung"
- },
- "source": [
- "# 3. Aufbereitung der Daten"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenvorbereitung"
- },
- "source": [
- "## 3.1. Reduzieren der Kunden-ID"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 174,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Removing the Customer ID, it does not add value to the model\n",
- "data_prep = data_no_mv.drop(\"customerID\", axis = 1)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenvorbereitung"
- },
- "source": [
- "## 3.2. Umkodierung der kategorialen Variablen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 175,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Convert binary variables to 1 and 0 with Yes and No\n",
- "bin_var = [\"Partner\",\"Dependents\",\"PhoneService\",\"PaperlessBilling\",\"Churn\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 176,
- "metadata": {},
- "outputs": [],
- "source": [
- "def binaer_umwandeln(x):\n",
- " return x.map({'Yes':1,'No':0})"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 177,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>gender</th>\n",
- " <th>SeniorCitizen</th>\n",
- " <th>Partner</th>\n",
- " <th>Dependents</th>\n",
- " <th>tenure</th>\n",
- " <th>PhoneService</th>\n",
- " <th>MultipleLines</th>\n",
- " <th>InternetService</th>\n",
- " <th>OnlineSecurity</th>\n",
- " <th>OnlineBackup</th>\n",
- " <th>DeviceProtection</th>\n",
- " <th>TechSupport</th>\n",
- " <th>StreamingTV</th>\n",
- " <th>StreamingMovies</th>\n",
- " <th>Contract</th>\n",
- " <th>PaperlessBilling</th>\n",
- " <th>PaymentMethod</th>\n",
- " <th>MonthlyCharges</th>\n",
- " <th>TotalCharges</th>\n",
- " <th>Churn</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>Female</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>No phone service</td>\n",
- " <td>DSL</td>\n",
- " <td>No</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>Month-to-month</td>\n",
- " <td>1</td>\n",
- " <td>Electronic check</td>\n",
- " <td>29.85</td>\n",
- " <td>29.85</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>Male</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>34</td>\n",
- " <td>1</td>\n",
- " <td>No</td>\n",
- " <td>DSL</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>One year</td>\n",
- " <td>0</td>\n",
- " <td>Mailed check</td>\n",
- " <td>56.95</td>\n",
- " <td>1889.50</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Male</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>No</td>\n",
- " <td>DSL</td>\n",
- " <td>Yes</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>Month-to-month</td>\n",
- " <td>1</td>\n",
- " <td>Mailed check</td>\n",
- " <td>53.85</td>\n",
- " <td>108.15</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>Male</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>45</td>\n",
- " <td>0</td>\n",
- " <td>No phone service</td>\n",
- " <td>DSL</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>Yes</td>\n",
- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>One year</td>\n",
- " <td>0</td>\n",
- " <td>Bank transfer (automatic)</td>\n",
- " <td>42.30</td>\n",
- " <td>1840.75</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>Female</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>No</td>\n",
- " <td>Fiber optic</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>No</td>\n",
- " <td>Month-to-month</td>\n",
- " <td>1</td>\n",
- " <td>Electronic check</td>\n",
- " <td>70.70</td>\n",
- " <td>151.65</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " gender SeniorCitizen Partner Dependents tenure PhoneService \\\n",
- "0 Female 0 1 0 1 0 \n",
- "1 Male 0 0 0 34 1 \n",
- "2 Male 0 0 0 2 1 \n",
- "3 Male 0 0 0 45 0 \n",
- "4 Female 0 0 0 2 1 \n",
- "\n",
- " MultipleLines InternetService OnlineSecurity OnlineBackup \\\n",
- "0 No phone service DSL No Yes \n",
- "1 No DSL Yes No \n",
- "2 No DSL Yes Yes \n",
- "3 No phone service DSL Yes No \n",
- "4 No Fiber optic No No \n",
- "\n",
- " DeviceProtection TechSupport StreamingTV StreamingMovies Contract \\\n",
- "0 No No No No Month-to-month \n",
- "1 Yes No No No One year \n",
- "2 No No No No Month-to-month \n",
- "3 Yes Yes No No One year \n",
- "4 No No No No Month-to-month \n",
- "\n",
- " PaperlessBilling PaymentMethod MonthlyCharges TotalCharges \\\n",
- "0 1 Electronic check 29.85 29.85 \n",
- "1 0 Mailed check 56.95 1889.50 \n",
- "2 1 Mailed check 53.85 108.15 \n",
- "3 0 Bank transfer (automatic) 42.30 1840.75 \n",
- "4 1 Electronic check 70.70 151.65 \n",
- "\n",
- " Churn \n",
- "0 0 \n",
- "1 0 \n",
- "2 1 \n",
- "3 0 \n",
- "4 1 "
- ]
- },
- "execution_count": 177,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data_prep[bin_var]=data_prep[bin_var].apply(binaer_umwandeln)\n",
- "data_prep.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 178,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>SeniorCitizen</th>\n",
- " <th>Partner</th>\n",
- " <th>Dependents</th>\n",
- " <th>tenure</th>\n",
- " <th>PhoneService</th>\n",
- " <th>PaperlessBilling</th>\n",
- " <th>MonthlyCharges</th>\n",
- " <th>TotalCharges</th>\n",
- " <th>Churn</th>\n",
- " <th>gender_Male</th>\n",
- " <th>...</th>\n",
- " <th>TechSupport_Yes</th>\n",
- " <th>StreamingTV_No internet service</th>\n",
- " <th>StreamingTV_Yes</th>\n",
- " <th>StreamingMovies_No internet service</th>\n",
- " <th>StreamingMovies_Yes</th>\n",
- " <th>Contract_One year</th>\n",
- " <th>Contract_Two year</th>\n",
- " <th>PaymentMethod_Credit card (automatic)</th>\n",
- " <th>PaymentMethod_Electronic check</th>\n",
- " <th>PaymentMethod_Mailed check</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>29.85</td>\n",
- " <td>29.85</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>34</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>56.95</td>\n",
- " <td>1889.50</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>53.85</td>\n",
- " <td>108.15</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>45</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>42.30</td>\n",
- " <td>1840.75</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>...</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>70.70</td>\n",
- " <td>151.65</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 31 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " SeniorCitizen Partner Dependents tenure PhoneService PaperlessBilling \\\n",
- "0 0 1 0 1 0 1 \n",
- "1 0 0 0 34 1 0 \n",
- "2 0 0 0 2 1 1 \n",
- "3 0 0 0 45 0 0 \n",
- "4 0 0 0 2 1 1 \n",
- "\n",
- " MonthlyCharges TotalCharges Churn gender_Male ... TechSupport_Yes \\\n",
- "0 29.85 29.85 0 0 ... 0 \n",
- "1 56.95 1889.50 0 1 ... 0 \n",
- "2 53.85 108.15 1 1 ... 0 \n",
- "3 42.30 1840.75 0 1 ... 1 \n",
- "4 70.70 151.65 1 0 ... 0 \n",
- "\n",
- " StreamingTV_No internet service StreamingTV_Yes \\\n",
- "0 0 0 \n",
- "1 0 0 \n",
- "2 0 0 \n",
- "3 0 0 \n",
- "4 0 0 \n",
- "\n",
- " StreamingMovies_No internet service StreamingMovies_Yes \\\n",
- "0 0 0 \n",
- "1 0 0 \n",
- "2 0 0 \n",
- "3 0 0 \n",
- "4 0 0 \n",
- "\n",
- " Contract_One year Contract_Two year \\\n",
- "0 0 0 \n",
- "1 1 0 \n",
- "2 0 0 \n",
- "3 1 0 \n",
- "4 0 0 \n",
- "\n",
- " PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check \\\n",
- "0 0 1 \n",
- "1 0 0 \n",
- "2 0 0 \n",
- "3 0 0 \n",
- "4 0 1 \n",
- "\n",
- " PaymentMethod_Mailed check \n",
- "0 0 \n",
- "1 1 \n",
- "2 1 \n",
- "3 0 \n",
- "4 0 \n",
- "\n",
- "[5 rows x 31 columns]"
- ]
- },
- "execution_count": 178,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# create dummies\n",
- "data_enc = pd.get_dummies(data_prep, drop_first=True)\n",
- "data_enc.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 179,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Dropping of dummies that also contain No phone service and No Internet service\n",
- "dup_variables = [\"OnlineSecurity_No internet service\",\"OnlineBackup_No internet service\", \"TechSupport_No internet service\",\"StreamingTV_No internet service\",\"StreamingMovies_No internet service\", \"DeviceProtection_No internet service\",\"MultipleLines_No phone service\"]\n",
- "data_enc.drop(dup_variables, axis=1, inplace=True)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenvorbereitung"
- },
- "source": [
- "## 3.3. Test auf Multikollinearität"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenvorbereitung"
- },
- "source": [
- "Um ein korrektes Funktionieren der späteren Regression zu gewährleisten, darf keine Multikollinearität zwischen den Variablen bestehen. Das Vorhandensein einer solchen wird mit Hilfe der Bibliothek Statsmodel überprüft."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 180,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " feature VIF\n",
- "0 SeniorCitizen 1.376564\n",
- "1 Partner 2.824725\n",
- "2 Dependents 1.969391\n",
- "3 tenure 20.482153\n",
- "4 PhoneService 47.244378\n",
- "5 PaperlessBilling 2.956951\n",
- "6 MonthlyCharges 212.353073\n",
- "7 TotalCharges 21.374002\n",
- "8 gender_Male 2.021331\n",
- "9 MultipleLines_Yes 2.861614\n",
- "10 InternetService_Fiber optic 17.695260\n",
- "11 InternetService_No 8.234451\n",
- "12 OnlineSecurity_Yes 2.682712\n",
- "13 OnlineBackup_Yes 2.909898\n",
- "14 DeviceProtection_Yes 2.992570\n",
- "15 TechSupport_Yes 2.758343\n",
- "16 StreamingTV_Yes 4.928957\n",
- "17 StreamingMovies_Yes 5.090603\n",
- "18 Contract_One year 2.056188\n",
- "19 Contract_Two year 3.487502\n",
- "20 PaymentMethod_Credit card (automatic) 1.984196\n",
- "21 PaymentMethod_Electronic check 2.955994\n",
- "22 PaymentMethod_Mailed check 2.383290\n"
- ]
- }
- ],
- "source": [
- "# independent variables\n",
- "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
- " \n",
- "# VIF dataframe \n",
- "vif_data = pd.DataFrame() \n",
- "vif_data[\"feature\"] = vif_test.columns \n",
- " \n",
- "# VIF for each Feature \n",
- "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
- " for i in range(len(vif_test.columns))] \n",
- " \n",
- "print(vif_data)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "\"MonthlyCharges\" hat den höchsten VIF und wird aus dem Datensatz entfernt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 181,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_enc.drop(\"MonthlyCharges\", axis=1, inplace=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 182,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " feature VIF\n",
- "0 SeniorCitizen 1.366018\n",
- "1 Partner 2.817414\n",
- "2 Dependents 1.961947\n",
- "3 tenure 17.073930\n",
- "4 PhoneService 9.277446\n",
- "5 PaperlessBilling 2.796488\n",
- "6 TotalCharges 18.028499\n",
- "7 gender_Male 1.942509\n",
- "8 MultipleLines_Yes 2.514269\n",
- "9 InternetService_Fiber optic 4.186492\n",
- "10 InternetService_No 3.473225\n",
- "11 OnlineSecurity_Yes 1.986701\n",
- "12 OnlineBackup_Yes 2.182678\n",
- "13 DeviceProtection_Yes 2.299462\n",
- "14 TechSupport_Yes 2.099655\n",
- "15 StreamingTV_Yes 2.749724\n",
- "16 StreamingMovies_Yes 2.771330\n",
- "17 Contract_One year 2.056169\n",
- "18 Contract_Two year 3.468149\n",
- "19 PaymentMethod_Credit card (automatic) 1.820729\n",
- "20 PaymentMethod_Electronic check 2.535918\n",
- "21 PaymentMethod_Mailed check 1.982063\n"
- ]
- }
- ],
- "source": [
- "# the independent variables set \n",
- "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
- " \n",
- "# VIF dataframe \n",
- "vif_data = pd.DataFrame() \n",
- "vif_data[\"feature\"] = vif_test.columns \n",
- " \n",
- "# VIF for each Feature \n",
- "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
- " for i in range(len(vif_test.columns))] \n",
- " \n",
- "print(vif_data)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "\"TotalCharges\" hat den höchsten VIF und wird aus dem Datensatz entfernt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 183,
- "metadata": {},
- "outputs": [],
- "source": [
- "data_enc.drop(\"TotalCharges\", axis=1, inplace=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 184,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " feature VIF\n",
- "0 SeniorCitizen 1.363244\n",
- "1 Partner 2.816895\n",
- "2 Dependents 1.956413\n",
- "3 tenure 7.530356\n",
- "4 PhoneService 9.260839\n",
- "5 PaperlessBilling 2.757816\n",
- "6 gender_Male 1.931277\n",
- "7 MultipleLines_Yes 2.426699\n",
- "8 InternetService_Fiber optic 3.581328\n",
- "9 InternetService_No 3.321342\n",
- "10 OnlineSecurity_Yes 1.947904\n",
- "11 OnlineBackup_Yes 2.093763\n",
- "12 DeviceProtection_Yes 2.241375\n",
- "13 TechSupport_Yes 2.060410\n",
- "14 StreamingTV_Yes 2.636855\n",
- "15 StreamingMovies_Yes 2.661529\n",
- "16 Contract_One year 2.055971\n",
- "17 Contract_Two year 3.456061\n",
- "18 PaymentMethod_Credit card (automatic) 1.794059\n",
- "19 PaymentMethod_Electronic check 2.401970\n",
- "20 PaymentMethod_Mailed check 1.967082\n"
- ]
- }
- ],
- "source": [
- "# the independent variables set \n",
- "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
- " \n",
- "# VIF dataframe \n",
- "vif_data = pd.DataFrame() \n",
- "vif_data[\"feature\"] = vif_test.columns \n",
- " \n",
- "# calculating VIF for each feature \n",
- "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
- " for i in range(len(vif_test.columns))] \n",
- " \n",
- "print(vif_data)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Keine der Variablen hat jetzt einen VIF von mehr als 10."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 3.4. Merkmalsskalierung"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 185,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Separate target variable and predictors\n",
- "y = data_enc[\"Churn\"]\n",
- "X = data_enc.drop(labels = [\"Churn\"], axis = 1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 186,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
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- " .dataframe thead th {\n",
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- " <th>Contract_One year</th>\n",
- " <th>Contract_Two year</th>\n",
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- " <th>PaymentMethod_Electronic check</th>\n",
- " <th>PaymentMethod_Mailed check</th>\n",
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- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>-1.239504</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>...</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0.512486</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>-1.239504</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 21 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " SeniorCitizen Partner Dependents tenure PhoneService \\\n",
- "0 0 1 0 -1.280248 0 \n",
- "1 0 0 0 0.064303 1 \n",
- "2 0 0 0 -1.239504 1 \n",
- "3 0 0 0 0.512486 0 \n",
- "4 0 0 0 -1.239504 1 \n",
- "\n",
- " PaperlessBilling gender_Male MultipleLines_Yes \\\n",
- "0 1 0 0 \n",
- "1 0 1 0 \n",
- "2 1 1 0 \n",
- "3 0 1 0 \n",
- "4 1 0 0 \n",
- "\n",
- " InternetService_Fiber optic InternetService_No ... OnlineBackup_Yes \\\n",
- "0 0 0 ... 1 \n",
- "1 0 0 ... 0 \n",
- "2 0 0 ... 1 \n",
- "3 0 0 ... 0 \n",
- "4 1 0 ... 0 \n",
- "\n",
- " DeviceProtection_Yes TechSupport_Yes StreamingTV_Yes \\\n",
- "0 0 0 0 \n",
- "1 1 0 0 \n",
- "2 0 0 0 \n",
- "3 1 1 0 \n",
- "4 0 0 0 \n",
- "\n",
- " StreamingMovies_Yes Contract_One year Contract_Two year \\\n",
- "0 0 0 0 \n",
- "1 0 1 0 \n",
- "2 0 0 0 \n",
- "3 0 1 0 \n",
- "4 0 0 0 \n",
- "\n",
- " PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check \\\n",
- "0 0 1 \n",
- "1 0 0 \n",
- "2 0 0 \n",
- "3 0 0 \n",
- "4 0 1 \n",
- "\n",
- " PaymentMethod_Mailed check \n",
- "0 0 \n",
- "1 1 \n",
- "2 1 \n",
- "3 0 \n",
- "4 0 \n",
- "\n",
- "[5 rows x 21 columns]"
- ]
- },
- "execution_count": 186,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Scaling the variables\n",
- "num_features = ['tenure']\n",
- "\n",
- "scaler = StandardScaler()\n",
- "\n",
- "X[num_features] = scaler.fit_transform(X[num_features])\n",
- "X.head()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 3.5. Undersampling "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 187,
- "metadata": {},
- "outputs": [],
- "source": [
- "iht = InstanceHardnessThreshold(random_state=0,estimator=LogisticRegression (solver='lbfgs', multi_class='auto'))\n",
- " \n",
- "X_resampled, y_resampled = iht.fit_resample(X, y)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 3.6. Erstellen von Test- & Trainingsdaten"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 188,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Split dataset in train and test datasets\n",
- "# The default value of 80% to 20% is used.\n",
- "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, random_state=110)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenmodell"
- },
- "source": [
- "# 4. Modellierung und Auswertung"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "include": true,
- "paragraph": "Datenmodell"
- },
- "source": [
- "Als Datenmodell wird eine logisitische Regression verwendet. Dies bietet sich zur Klassifikation der Kunden auf eine binäre Zielvariable (= Kundenabwanderung) an."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 4.1. Logistische Regression"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenmodell"
- },
- "source": [
- "Zur Lösung des Problems wird die logistische Regression verwendet. Hierfür werden die beiden Bibliotheken Statsmodels und Scikit-Learn verwendet. Die komplette Auswertung des Modells findet erst im Unterkapitel zu Scikit-Learn statt."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenmodell"
- },
- "source": [
- "## Statistische Modelle"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenmodell"
- },
- "source": [
- "### Training und Vorhersage"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 189,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Optimization terminated successfully.\n",
- " Current function value: 0.082006\n",
- " Iterations 11\n",
- " Logit Regression Results \n",
- "==============================================================================\n",
- "Dep. Variable: Churn No. Observations: 2803\n",
- "Model: Logit Df Residuals: 2781\n",
- "Method: MLE Df Model: 21\n",
- "Date: Thu, 21 Oct 2021 Pseudo R-squ.: 0.8817\n",
- "Time: 15:00:28 Log-Likelihood: -229.86\n",
- "converged: True LL-Null: -1942.4\n",
- "Covariance Type: nonrobust LLR p-value: 0.000\n",
- "=========================================================================================================\n",
- " coef std err z P>|z| [0.025 0.975]\n",
- "---------------------------------------------------------------------------------------------------------\n",
- "const 5.1912 0.828 6.266 0.000 3.567 6.815\n",
- "SeniorCitizen 0.4609 0.457 1.008 0.313 -0.435 1.357\n",
- "Partner -0.4112 0.302 -1.362 0.173 -1.003 0.181\n",
- "Dependents -0.5746 0.294 -1.952 0.051 -1.151 0.002\n",
- "tenure -2.9281 0.309 -9.468 0.000 -3.534 -2.322\n",
- "PhoneService -1.2307 0.544 -2.261 0.024 -2.298 -0.164\n",
- "PaperlessBilling 1.2621 0.288 4.385 0.000 0.698 1.826\n",
- "gender_Male -0.1334 0.255 -0.524 0.600 -0.633 0.366\n",
- "MultipleLines_Yes 1.0865 0.336 3.231 0.001 0.427 1.746\n",
- "InternetService_Fiber optic 3.1681 0.400 7.916 0.000 2.384 3.952\n",
- "InternetService_No -2.8314 0.567 -4.992 0.000 -3.943 -1.720\n",
- "OnlineSecurity_Yes -1.7901 0.321 -5.581 0.000 -2.419 -1.161\n",
- "OnlineBackup_Yes -0.3203 0.309 -1.036 0.300 -0.926 0.286\n",
- "DeviceProtection_Yes 0.4336 0.331 1.312 0.190 -0.214 1.082\n",
- "TechSupport_Yes -0.8710 0.329 -2.648 0.008 -1.516 -0.226\n",
- "StreamingTV_Yes 1.1971 0.351 3.414 0.001 0.510 1.884\n",
- "StreamingMovies_Yes 1.4263 0.374 3.815 0.000 0.693 2.159\n",
- "Contract_One year -3.5720 0.488 -7.317 0.000 -4.529 -2.615\n",
- "Contract_Two year -6.5206 0.584 -11.164 0.000 -7.665 -5.376\n",
- "PaymentMethod_Credit card (automatic) -0.0720 0.313 -0.230 0.818 -0.686 0.542\n",
- "PaymentMethod_Electronic check 1.2794 0.406 3.154 0.002 0.484 2.075\n",
- "PaymentMethod_Mailed check -0.3240 0.398 -0.813 0.416 -1.105 0.457\n",
- "=========================================================================================================\n",
- "\n",
- "Possibly complete quasi-separation: A fraction 0.37 of observations can be\n",
- "perfectly predicted. This might indicate that there is complete\n",
- "quasi-separation. In this case some parameters will not be identified.\n"
- ]
- }
- ],
- "source": [
- "# add constant\n",
- "X_const = sm.add_constant(X_train)\n",
- "# create model\n",
- "log_reg = sm.Logit(y_train, X_const).fit() \n",
- "print(log_reg.summary())"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenmodell"
- },
- "source": [
- "Das trainierte Modell zeigt statistisch nicht-signifikante Variablen an. Dies ist gegeben, wenn der Wert P>|z| größer als 0,05 ist und es sich nicht um die Konstante handelt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 190,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Removing the statistically non-significant features (P>|z|> 0.05)\n",
- "insignificant_features = [\"Partner\", \"gender_Male\", \"OnlineBackup_Yes\", \"DeviceProtection_Yes\", \"PaymentMethod_Credit card (automatic)\",\"PaymentMethod_Mailed check\"]\n",
- "X_train.drop(insignificant_features, axis=1, inplace=True)\n",
- "X_test.drop(insignificant_features, axis=1, inplace=True)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Datenmodell"
- },
- "source": [
- "Nun kann ein zweites Modell erstellt werden:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 191,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Optimization terminated successfully.\n",
- " Current function value: 0.083077\n",
- " Iterations 11\n",
- " Logit Regression Results \n",
- "==============================================================================\n",
- "Dep. Variable: Churn No. Observations: 2803\n",
- "Model: Logit Df Residuals: 2787\n",
- "Method: MLE Df Model: 15\n",
- "Date: Thu, 21 Oct 2021 Pseudo R-squ.: 0.8801\n",
- "Time: 15:00:28 Log-Likelihood: -232.87\n",
- "converged: True LL-Null: -1942.4\n",
- "Covariance Type: nonrobust LLR p-value: 0.000\n",
- "==================================================================================================\n",
- " coef std err z P>|z| [0.025 0.975]\n",
- "--------------------------------------------------------------------------------------------------\n",
- "const 4.7119 0.718 6.566 0.000 3.305 6.118\n",
- "SeniorCitizen 0.3954 0.458 0.864 0.387 -0.501 1.292\n",
- "Dependents -0.7328 0.262 -2.797 0.005 -1.246 -0.219\n",
- "tenure -2.9242 0.297 -9.845 0.000 -3.506 -2.342\n",
- "PhoneService -1.2073 0.540 -2.235 0.025 -2.266 -0.149\n",
- "PaperlessBilling 1.2161 0.285 4.273 0.000 0.658 1.774\n",
- "MultipleLines_Yes 1.0989 0.331 3.320 0.001 0.450 1.748\n",
- "InternetService_Fiber optic 3.1159 0.391 7.966 0.000 2.349 3.883\n",
- "InternetService_No -2.8462 0.529 -5.381 0.000 -3.883 -1.809\n",
- "OnlineSecurity_Yes -1.7441 0.313 -5.576 0.000 -2.357 -1.131\n",
- "TechSupport_Yes -0.8357 0.325 -2.569 0.010 -1.473 -0.198\n",
- "StreamingTV_Yes 1.2193 0.348 3.508 0.000 0.538 1.901\n",
- "StreamingMovies_Yes 1.4394 0.368 3.908 0.000 0.717 2.161\n",
- "Contract_One year -3.4572 0.471 -7.337 0.000 -4.381 -2.534\n",
- "Contract_Two year -6.3299 0.557 -11.372 0.000 -7.421 -5.239\n",
- "PaymentMethod_Electronic check 1.3103 0.362 3.623 0.000 0.601 2.019\n",
- "==================================================================================================\n",
- "\n",
- "Possibly complete quasi-separation: A fraction 0.36 of observations can be\n",
- "perfectly predicted. This might indicate that there is complete\n",
- "quasi-separation. In this case some parameters will not be identified.\n"
- ]
- }
- ],
- "source": [
- "# new model\n",
- "X_const = sm.add_constant(X_train)\n",
- "log_reg2 = sm.Logit(y_train, X_const).fit() \n",
- "print(log_reg2.summary())"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Keine statistisch nicht signifikanten Variablen mehr. Das endgültige Modell wurde modelliert:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 192,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Optimization terminated successfully.\n",
- " Current function value: 0.083077\n",
- " Iterations 11\n",
- " Logit Regression Results \n",
- "==============================================================================\n",
- "Dep. Variable: Churn No. Observations: 2803\n",
- "Model: Logit Df Residuals: 2787\n",
- "Method: MLE Df Model: 15\n",
- "Date: Thu, 21 Oct 2021 Pseudo R-squ.: 0.8801\n",
- "Time: 15:00:28 Log-Likelihood: -232.87\n",
- "converged: True LL-Null: -1942.4\n",
- "Covariance Type: nonrobust LLR p-value: 0.000\n",
- "==================================================================================================\n",
- " coef std err z P>|z| [0.025 0.975]\n",
- "--------------------------------------------------------------------------------------------------\n",
- "const 4.7119 0.718 6.566 0.000 3.305 6.118\n",
- "SeniorCitizen 0.3954 0.458 0.864 0.387 -0.501 1.292\n",
- "Dependents -0.7328 0.262 -2.797 0.005 -1.246 -0.219\n",
- "tenure -2.9242 0.297 -9.845 0.000 -3.506 -2.342\n",
- "PhoneService -1.2073 0.540 -2.235 0.025 -2.266 -0.149\n",
- "PaperlessBilling 1.2161 0.285 4.273 0.000 0.658 1.774\n",
- "MultipleLines_Yes 1.0989 0.331 3.320 0.001 0.450 1.748\n",
- "InternetService_Fiber optic 3.1159 0.391 7.966 0.000 2.349 3.883\n",
- "InternetService_No -2.8462 0.529 -5.381 0.000 -3.883 -1.809\n",
- "OnlineSecurity_Yes -1.7441 0.313 -5.576 0.000 -2.357 -1.131\n",
- "TechSupport_Yes -0.8357 0.325 -2.569 0.010 -1.473 -0.198\n",
- "StreamingTV_Yes 1.2193 0.348 3.508 0.000 0.538 1.901\n",
- "StreamingMovies_Yes 1.4394 0.368 3.908 0.000 0.717 2.161\n",
- "Contract_One year -3.4572 0.471 -7.337 0.000 -4.381 -2.534\n",
- "Contract_Two year -6.3299 0.557 -11.372 0.000 -7.421 -5.239\n",
- "PaymentMethod_Electronic check 1.3103 0.362 3.623 0.000 0.601 2.019\n",
- "==================================================================================================\n",
- "\n",
- "Possibly complete quasi-separation: A fraction 0.36 of observations can be\n",
- "perfectly predicted. This might indicate that there is complete\n",
- "quasi-separation. In this case some parameters will not be identified.\n"
- ]
- }
- ],
- "source": [
- "# final model\n",
- "X_const = sm.add_constant(X_train)\n",
- "log_reg_final = sm.Logit(y_train, X_const).fit() \n",
- "print(log_reg_final.summary())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 193,
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "# prediction\n",
- "y_hat = log_reg_final.predict(sm.add_constant(X_test)) \n",
- "# Statsmodel only gives the probabilities, therefore rounding is required. \n",
- "prediction = list(map(round, y_hat))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 4.1. Auswertung"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Zur Evaluation sollen mehrere Metriken verwendet werden, die komfortabler mittels Scikit-Learn erzeugt werden können. Deshalb wird das identische Modell wie mit Statsmodels nochmals in Scikit-Learn erzeugt."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "include": true,
- "paragraph": "Evaluation"
- },
- "source": [
- "Für die Bewertung der Qualität einer Klassifikation werden Metriken wir Accuracy (= allgemeine Genauigkeit der Klassifikation), Precision (= Präzision der Vorhersage der Kundenabwanderung) und Recall (= Menge der abwanderungswilligen Kunden die korrekt klassifiziert wurden) genutzt. In einer ersten Modellstufe wird eine Accuracy von 80%, ein Recall von 53% sowie eine Precision von 68% erreicht. Insbesondere der Recall ist hier zu schlecht, da nur etwas mehr als die Hälfte der abwanderungswilligen Kunden erkannt werden. In einer zweiten Modellstufe wird eine Hyperparameteroptimierung vorgenommen. Durch eine Veränderung des Treshholds für die Klassifikation wird eine Accuracy von 76%, ein Recall von 73% und eine Precision von 53% erreicht. Fast drei Viertel aller abwanderungswilligen Kunden werden durch dieses Modell erkannt."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## Scikit-Learn"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "### Training und Vorhersage"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 194,
- "metadata": {},
- "outputs": [],
- "source": [
- "# C is needed to build the exact same model as with Statsmodels; source: https://www.kdnuggets.com/2016/06/regularization-logistic-regression.html\n",
- "logistic_model = LogisticRegression(random_state=0, C=1e8)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 195,
- "metadata": {},
- "outputs": [],
- "source": [
- "# prediction with testdata\n",
- "result = logistic_model.fit(X_train,y_train)\n",
- "prediction_test = logistic_model.predict(X_test)\n",
- "prediction_train = logistic_model.predict(X_train)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Evaluation"
- },
- "source": [
- "### Evaluation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 196,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy with testdata: 0.9882352941176471\n"
- ]
- }
- ],
- "source": [
- "# Accuracy Score \n",
- "acc = metrics.accuracy_score(y_test, prediction_test)\n",
- "print('Accuracy with testdata: {}'.format(acc))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Evaluation"
- },
- "source": [
- "Die Genauigkeit deutet auf ein überdurchschnittliches Modell hin. Allerdings handelt es sich um einen unausgewogenen Datensatz. Daher müssen weitere Metriken analysiert werden.\n",
- "\t\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 197,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "traindata:\n",
- " precision recall f1-score support\n",
- "\n",
- " 0 0.96 1.00 0.98 1374\n",
- " 1 1.00 0.96 0.98 1429\n",
- "\n",
- " accuracy 0.98 2803\n",
- " macro avg 0.98 0.98 0.98 2803\n",
- "weighted avg 0.98 0.98 0.98 2803\n",
- "\n",
- "testdata:\n",
- " precision recall f1-score support\n",
- "\n",
- " 0 0.98 1.00 0.99 495\n",
- " 1 1.00 0.98 0.99 440\n",
- "\n",
- " accuracy 0.99 935\n",
- " macro avg 0.99 0.99 0.99 935\n",
- "weighted avg 0.99 0.99 0.99 935\n",
- "\n"
- ]
- }
- ],
- "source": [
- "# classification report\n",
- "\n",
- "print(\"traindata:\")\n",
- "print(classification_report(y_train,prediction_train))\n",
- "print(\"testdata:\")\n",
- "print(classification_report(y_test,prediction_test))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Höhere Genauigkeit für das Training als für den Testdatensatz. Insgesamt sind die Werte für den Test- und den Trainingsdatensatz sehr ähnlich. Daher sollte nicht von einem Overfitting oder Underfitting ausgegangen werden."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 198,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0.5, 39.5, 'Predicted label')"
- ]
- },
- "execution_count": 198,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 720x504 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Confusion matrix testdata\n",
- "cm = confusion_matrix(y_test,prediction_test)\n",
- "df_cm = pd.DataFrame(cm, index=['No Churn','Churn'], columns=['No Churn', 'Churn'],)\n",
- "fig = plt.figure(figsize=[10,7])\n",
- "heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
- "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=14)\n",
- "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=14)\n",
- "plt.ylabel('True label')\n",
- "plt.xlabel('Predicted label')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 199,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "True Negatives: 493\n",
- "False Positives: 2\n",
- "False Negatives: 9\n",
- "True Positives: 431\n",
- "Recall: 0.9795454545454545\n",
- "Precision: 0.9953810623556582\n"
- ]
- }
- ],
- "source": [
- "# metrics from confusion matrix\n",
- "tn, fp, fn, tp = cm.ravel()\n",
- "recall = tp/(fn+tp)\n",
- "precision = tp/(tp+fp)\n",
- "print(\"True Negatives: \" + str(tn))\n",
- "print(\"False Positives: \" + str(fp))\n",
- "print(\"False Negatives: \" + str(fn))\n",
- "print(\"True Positives: \" + str(tp))\n",
- "print(\"Recall: \" + str(recall))\n",
- "print(\"Precision: \" + str(precision))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Präzision und Recall vermitteln ein viel realistischeres Bild des Modells. Es erreicht eine Präzision von rund 68 % und eine Wiederauffindbarkeit von 52 %. Der Recall ist für den Anwendungsfall eindeutig wichtiger und muss daher auf Kosten der Präzision verbessert werden."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 200,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# ROC-Kurve, AUC\n",
- "fig, ax = plt.subplots(figsize=(8,6))\n",
- "ax.set_title('ROC Kurve')\n",
- "plot = metrics.plot_roc_curve(logistic_model, X_test, y_test, ax=ax);\n",
- "ax.plot([0,1], [0,1], '--');"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Der AUC der ROC-Kurve ergibt einen guten Wert von 0,84. Daraus lässt sich schließen, dass durch die Optimierung des Schwellenwertes Optimierungspotenzial besteht."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 4.3. Interpretation"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Zunächst sollen jedoch die Ergebnisse für das Unternehmen veranschaulicht werden und es soll geklärt werden, welche Kunden zur Abwanderung führen und welche gegen eine Abwanderung sprechen."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 201,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "InternetService_Fiber optic 3.115901\n",
- "StreamingMovies_Yes 1.439381\n",
- "PaymentMethod_Electronic check 1.310265\n",
- "StreamingTV_Yes 1.219198\n",
- "PaperlessBilling 1.216093\n",
- "MultipleLines_Yes 1.098867\n",
- "SeniorCitizen 0.395488\n",
- "Dependents -0.732812\n",
- "TechSupport_Yes -0.835712\n",
- "PhoneService -1.207319\n",
- "OnlineSecurity_Yes -1.744166\n",
- "InternetService_No -2.846463\n",
- "tenure -2.924275\n",
- "Contract_One year -3.457173\n",
- "Contract_Two year -6.329852\n",
- "dtype: float64"
- ]
- },
- "execution_count": 201,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Read out regression coefficients and thus find out importance of individual attributes\n",
- "weights = pd.Series(logistic_model.coef_[0],\n",
- " index=X_train.columns.values)\n",
- "weights.sort_values(ascending = False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 202,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "AxesSubplot(0.125,0.125;0.775x0.755)\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Graphical representation of key features that lead to churn.\n",
- "weights = pd.Series(logistic_model.coef_[0],\n",
- " index=X_train.columns.values)\n",
- "print (weights.sort_values(ascending = False)[:7].plot(kind='bar'))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Die drei Hauptmerkmale, die zur Abwanderung führen, sind:\n",
- "\n",
- "- Der Glasfaserdienst (InternetService_Glasfaser),\n",
- "- Die Online-Zahlungen (PaperlessBilling) und\n",
- "- Das Abonnement des zusätzlichen Filmstreamingdienstes (StreamingMovies_Yes).\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 203,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "AxesSubplot(0.125,0.125;0.775x0.755)\n"
- ]
- },
- {
- "data": {
- "image/png": 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mzVCtWjX98UrLKSmYp6endPz4cWnfvn3SuHHjpDt37kgDBw4UHctAQZ7FixdL69evNzgmN4MHD5ZSUlIkd3d3SZIk6erVq5Krq6vYUM/RaDSSTqeTVq1aJa1atUqSJPl+P728vKSYmBj94/Pnz0uDBg0SmKiwiRMnSjt27JBcXV2lJ0+eSMuWLZPGjBkjOpYiRUdHF/mnsih6mEWn06FHjx749ddf0adPHzRq1AharVZ0LANarRb379/H4cOH4eDggNTUVDx58kR0rCLpdDpYWVnpH7do0UJgmqJptVrodDocOXIEPXv2RE5ODnJyckTHKlJ2drbBWfhbb70lu3/727dvw8vLC6ampqhatSp8fX1x9+5d0bEMxMTEYNy4cRgxYgR8fHzg7e2N999/X3SsQrp06YLatWvD1NQUJiYm0Ol0uHnzZqW9vqLLvEaNGti0aROio6Ph6OiILVu2oFatWqJjGRg9ejSGDBkCe3t7tGrVCt7e3hg/frzoWEVq2LAhjh49ChMTE2RmZiI0NBSvvvqq6FgGPDw80KNHD9jY2KBjx44YNGgQvLy8RMcqUt26dXH48GH948OHD8PCwkJcoCKYmZnh4cOHMDF5uvHBX3/9BVNTedXCF198AScnJ2i1WgwbNgwNGjSAk5OT6FiFzJo1C59//jkmTJiAZcuWYdy4cYiMjKy8AJX2HqAC3L17V1q9erV07tw5SZKeDmXcvXtXcKqipaenS5IkSXl5eYKTvNi9e/ckX19fqWvXrlKXLl2kiRMnSsnJyaJjFaLVavX/nZaWJjBJ8f744w9p8ODBUpcuXaQuXbpIgwYNkq5fvy46loFjx45J7u7uUpcuXaRx48ZJ3bp1k44ePSo6loGCYb+VK1dKJ0+elPLz8yVnZ2exoYrg6Ogo5ebmSrNnz5auXr0qxcbGSh9++GGlvb6iyzwgIKDQsenTpwtI8mIJCQlS3759JXt7e+nu3buSk5OTFBcXJzrWC126dEmSJEnKzMyUTp48KThNYbdv35Y++ugjqXfv3lJycrI0fPhw6datW6JjFevRo0fSw4cPRccoUkJCgpSWliYdPXpUOnz4sJSamio6UiFDhgyRHjx4IO3bt08KDQ2VJEmS+vTpIzhVYV5eXpIkSVJYWJgUEREhSZJUqdechO0B+jJmzpyJW7duIS4uDlevXtUf12q1yMzMFJissPnz52Pt2rWYMmUKGjRogHnz5mHu3LnYtWuX6GiFhISEID4+Hps2bUJOTg7WrVuHs2fPYuLEiaKj6c2ZMwejR49GSEgIrKys4OLighkzZmDbtm2io+mVNN84KCiokpKUzNfXF5GRkXBwcBAd5YU++ugj+Pr6YvXq1fD09MT+/ftha2srOlYhDRo0wPr169GtWzcsWbIEwNOpyZVFkWU+btw4/P333wgMDMSnn36qP25mZoY33nhDYLLCcnJyDDJ1794dwcHBAhO92K+//oq9e/cCAKytrfHNN99Ao9HIqswfPHiAHj16ICQkBCYmJhgyZIisihx4eiHseTdu3MDGjRtlNS0R+O8c7o4dO6J69er64++8847AVIacnZ3Rr18/mJiY4Mcff8Rff/2FNm3aiI5VSGBgIH777Td06NABffr0QUREBObNm1dpr6/IMn/ttdfw2muvYd++fcjKysLDhw8h/d90+ezsbFldZLKwsEBiYqL+AtO+fftkNYf3Wfn5+Xj8+LH+InJeXp7gRIVVr14dd+/e1X8/z549i6pVqwpOZUij0Rg83rJlC3bu3ImpU6fCx8dHUKqipaenIzo6GtHR0fpjcpvD/fxc+K1bt8puLjwA1K5dGx07dsSvv/6KDz74AA4ODmjcuHGlvb6ibxpav3491q9fb1DeJiYmOHLkiLhQ/yc5ORkNGjTAjRs34Ofnh4sXL6J69epo0qQJQkJC0KxZM9ERCwkLC8P27dv1076OHTuGYcOG4cMPPxSc7Okv6Zo1ayI2NhazZ8/GzZs38frrryMjIwMrV66U3RkvANy6dUs/5BIYGIgmTZoITqRMkyZNQvfu3bFt2zbs2rULa9euRUJCAjZs2CA6moGDBw8iNDQUOTk52LlzJ9zc3DB9+nS4u7tXToBKG52vAL169ZLtbIZu3bpJe/fu1T+W80WwZ8XGxkobN26UNm/erL8YKgdOTk7SmTNnJEmSpNzcXOnKlStSQkKC9OTJE8HJirZ582apW7du0ubNmyWdTic6zgt5e3tLw4cPL/RHTjQajSRJ/53VIkmVe2GxtDw8PKSHDx/qcyYnJ0v9+/evtNdX5DBLgUaNGsnurVaBsLAwzJ49Gz///DMCAgJQr1490ZFe6OjRo3B0dER4eDgAwNLSEsDTtSauXLkCDw8PceH+z9y5c+Hv7w8nJyf4+vqiZcuWoiO9kLe3N2JjYzFq1Ci88sor+usQBeTw/Szw7PWQ/Px8HDlyBK+88orARIUpYS48AJiamqJ27dr6x9bW1pWaU9Fl3rRpU3z44Yfo2rWrwbjpsxdFRWnVqhV27NiBb7/9FkOHDsW4ceNgY2Oj/7icLjBdvHgRjo6OBuOmz5JD+fTo0QP79u3DypUrMXjwYMyZM8fghiY53dzUuHFjNG7cGMnJyUhOTi70cTl8Pws8f7H23XffhaenJz777DNBiQqbOHEihg8fjqSkJIwfPx4xMTFYuHCh6FiFtGzZEt9++y3y8/ORkJCA7777rlIv1Cp6zHzNmjVFHpdDmRdISUnB7Nmz8ddff8Ha2hqA/C4wFShYcsDcXL6/43NycuDv748TJ07glVdegSRJsrlOUharV6+WxSyhO3fu6P9bkiRcu3YNCxYswM8//ywwlaHc3FxkZWUhNjYWWq0WHTt2xP/8z/+IjlVIdnY2QkNDcfLkSeh0OtjZ2WHChAkGZ+sVSdFlDjz9Bt68eROtWrXC48ePUbNmTdGR9LZu3YrQ0FCMGDEC//u//wszMzPRkYo1adIkxMTEwNHREW5ubujUqZPoSAaOHj2K+fPno0ePHpg+fXql/ZBUBI1Ggz179oiOYbDGiYmJCSwtLfHpp5/C3t5eYCpDDg4OcHR0hEajQYcOHUTHeaF//etfcHd3N1jfqDIpuszlvGTrkCFDYGJigoULF8pu7ntxsrKycPjwYURGRuLmzZvo16+fLN5yT5o0CfHx8Zg/fz66desmOs5L8/Dw0F+joOLl5OTg3//+N8LDw5GWlgYPDw+4ubkJK80XWbNmDSIiIvD6669Do9HAyckJVapUqbTXV3SZe3p6Yt26dRgzZgzCw8Nx7do1TJ48Gfv27RMdDZs2bcJHH31U7AWQS5cuoV27dpWYqnRu3bqFAwcO4ODBg7C0tERYWJjoSJg/fz6mTJlS7Duv1NRU2f2Av4hczsz//vtvfPvtt8jIyMCzVSCnu1Sf9fPPP2PBggXIzMxEt27dMGPGDNlN+Tx79iwiIiJw+vRp2NnZwdPTE2+++WaFv658B0dLQc5Lto4aNarEvzNr1ixZ/EAX+OabbxAREYHc3Fy4ublhw4YNaNiwoehYAIDZs2eX+HfGjh0rq++nEnz++efo3LkzOnfurJ8tIjc3btzAvn37EBERgVdffRVTp05Fnz59EBUVhTFjxuDQoUOiI+plZ2fj9u3buHXrFkxNTVG3bl0EBgbi//2//4cpU6ZU6GsrusyfX7J127ZtsprVUBK5vSkquPhVGWcRFUFu308lyM/Px4wZM0THKNbIkSMxcOBAbNq0yWBGmL29PU6cOCEwmaGpU6ciKioKPXv2xLhx49C5c2cATy/g9ujRg2VenICAAAQGBiIpKQlOTk6ws7NDQECA6FilJrczof/85z8IDAwUHaPc5Pb9LI5crqN06tQJv/zyC3r06CG7ZREKHDly5IX/tl988UUlp3mxgv55fiiwatWqOHDgQIW/vqLHzJVOLuOmBXx9fWFvb48OHToYLLqklHc7cvt+KmF/zR49euDevXsAnv4yLJjqmZCQIDgZlZUiz8zff//9Ys/ClDbnWC4uXLiACxcuGBxT4hxuuZg9eza6d++O2NhY1KxZE9bW1pg2bZqs1hT5/fffRUcgI1FkmW/duhWSJGHt2rVo3LgxBg4cCDMzM+zfvx+3b98WHa/U5Pam6JdffhEd4aXI7ftZsL/m9u3b9ftrurm5iY5lIDc3F5s2bcKff/6J2bNnIywsDGPHjpXdkEvB/SStW7dGTk6OrO4nkQv5LXBQCjY2Nnjttddw+fJljB8/Hg0bNoSVlRVGjRqFmJgY0fEMzJ8/v9CxggtOq1evruw4xcrIyMCsWbPg4+OD9PR0+Pv7y26zjy+//BKxsbFFfmzWrFmVnKZ4SlhTJCAgANnZ2bh06RLMzMxw48YNWY1DA0/vJ3F3d8f48eNx7949ODo6yvIdRU5ODhYvXoyBAwfC3d0dCxcuRHZ2dqW9viLPzJ916tQp/U0kv/32m2zusizNbkiVudZxaRQ1LDB16lRZDQt06NABS5cuxf379+Hu7m5wx13B7AG5UMKaIpcuXcKePXtw7Ngx1KhRA4sXL4arq6voWAaWLVuG7777DmPGjIGVlRW2bduGyZMny+LmwGcFBASgRo0a+n/j77//HnPnztXvOlTRFF3mCxYswIwZM5CamgpJkmBjY4PFixeLjgVAWbshFVDCsIBGo4FGo0FSUhIiIiIwdOhQtGjRAp6enrLbsb1nz56wtbXVrykSEBAguzVFTExMkJubq3/38ODBA9nNCpLz/STPunTpksENi3PmzEH//v0r7fUVXeZt27bF/v379f8DymmHoYLdkKysrIrcRkyOlDAsADy9Q3Xfvn04cOAAmjRpgt69eyMyMhKHDh2SzS9zAIiKisKKFSuwY8cO/PHHH/Dy8sKSJUvw9ttvi46m5+Pjg5EjRyI1NRWBgYH4+eefZbVQHaCc+0kkSUJmZqZ+CeHMzMxKHSlQ9NTE+Ph4fPXVV4VuRZbTioTDhg1DSEgIGjVqJDpKiY4fP46lS5ciKSkJnTp10g8LyGmz3w8++AD37t2Dh4cHNBqN/oc6Pz8fPXv2xMmTJwUn/C+NRoPg4GC0atUKAHD9+nVMnz4dP/74o+Bk/zVx4kR8/vnniIqKgk6nwzvvvIOgoCBs3rxZdDS9tLQ0BAYGGqxGOGvWLP0qpHLx448/Yv369frFy3755ReMHTsWgwcPrpTXV3SZu7q6wsvLCy1btjR4ayinM+F+/frhxo0bqF+/PqpVqyb7JVvv378v66VGDx06hD59+hgc+/vvvw3uDJSL/v374+DBgwbH3N3dC21WIcKnn36KhIQEpKSkwNraWn8ypNPp0KhRI2zfvl1wQmW6cuUKzpw5A51Ohy5duqB169aV9tqKHmapXr06vL29Rcco1saNG0VHKJXLly/D0tISVlZWsLS0xN69e5Geno5BgwaJjgYASEpKgiRJWLVqFdq3b68vH61WizFjxuCnn34SnLCw5s2bY8mSJXB3d4eJiQkiIiLQtGlT0bEAAIsWLUJ6ejoCAwMNZgGZm5ujfv36ApMVdvz4caxYsaLQO3C5nRC5urrCwcEBDg4OePvttyv92oOiz8xXrlwJS0tL9OjRA9WqVdMfl9N4miRJ2L59O6KiopCfnw87Ozt4e3vLaiw6PDwcq1atwsqVK2FhYQE3Nzf4+Pjg2rVraNu2LSZMmCA6Ivz9/REdHa0/kyxgbm4OBwcH2U2nA55O9VyxYgXOnj0Lc3NzdO7cGZMmTUKdOnVER1OUvn37ws/Pr9A7cLm9G0tNTcXx48dx7NgxxMfHo2PHjnB0dKy0i6CKLvNnF9YvILchjODgYNy4cQODBg2CJEnYvXs3bGxsMHPmTNHR9DQaDTZu3AhLS0usWbMGcXFx+Oqrr5CbmwuNRlMp60qU1oYNGzB27FjRMagSDR06FDt27BAdo1QkSUJcXBxOnTql30Kusq7jKHqYRQl3LJ44cQLh4eH6M3EHBwfZzePV6XT6TZyjo6P1ZxJyugtw586d8PLyQm5ubpHbBcppBkbBGjFt2rQxOJPkuifl06lTJwQFBeG9994zeAcup310AWDMmDH4448/0KZNG3Tp0gUbNmyo1D1AFV3mRS1k5O/vL6vdxbVaLfLz8/XFqNVqZXNjU4GCucbZ2dk4f/68/qaHBw8eQKvVCk73lJLeQBYs9hUXFyfr/VSVouCO3/j4eP0xOe6j++abbyI7Oxvp6elIS0vDvXv38PjxY4NF6yqSov9PU8Idi66urvDx8cGAAQMAAAcOHICLi4vgVIY8PT3h5eUF4Oka0Y0bN8apU6ewfPlyDBkyRHC6p4YOHQrg6cwVue6C8zxHR0f07dsXbm5ust67Uu62bt0qOkKpTJ48GQDw6NEjHDp0CAEBAbhz5w7i4uIq5fUVPWY+cOBA7N6922A/RTc3N1lsG/esY8eO4dSpUwCArl27ymredoHY2FikpqaiZ8+eqFKlCsLDw6HT6TBw4EDR0QwMGjQIW7ZsQa1atURHKVFGRgYOHTqEiIgIJCcnw8XFBW5ubnj99ddFR1OMM2fOYN26dbh48SJMTExga2uLCRMmyG7pBuDprJtTp04hKioKWq0WPXv2hL29faVlVfSZuVLuWMzNzUVubi7Mzc0rdYPXsig4c7x9+zauXbsGFxcXJCUlCU5VmKmpKRwdHdGsWTOD8VO5veUGgLp168LT0xOenp64ePEi5s6di3Xr1hkMF9CLnTp1CtOnT8e4cePwxRdfIC8vD+fPn4evry9CQkLQtWtX0REBPB1W02g02LZtGxwcHODj4yNku0VFn5kr4Y7FRYsWISYmBgMGDIBOp8OBAwfw/vvv45NPPhEdrZCDBw8iNDQUOTk52LlzJ9zc3DB9+nS4u7uLjqZ3+vTpIo/L6UaxAvfv30dkZCQOHjyIjIwM/Zm5nKbOypm3tzdmzpxZaBvDuLg4BAUFYdu2bYKSGZLLpiiKPjN/77330K5dO8TGxkKn08lyIaOjR4/iwIED+gthQ4cOhYeHhyzL/Ouvv8b27dvh7e2N+vXrY8+ePRg5cqSsylxui0AVx93dHc7OzvDz80P79u1Fx1GcrKysIvejtbW1RUZGhoBE8qboMs/MzERoaCiioqJgbm6u30i1sq4el4aVlRUyMzP1U//y8vJQr149wamKZmpqitq1a+sfW1tby27YatWqVfr/zs/Px+XLl9G5c2fZTVMDnl4fkePNTEqRnZ2N/Pz8QjOC8vPzkZ+fLyhVYVevXkWvXr0KHa/spTsUXebTpk1D8+bNERISAkmS8OOPP2LmzJlYunSp6Gh6lpaWcHNzQ69evWBubo7jx4/D0tIS/v7+ACCrmRktW7bU3+iQkJCA7777rlLnyZbG8zMbbt26Javv4bP+/PNPPHr0SBEXa+WoR48eCAkJgZ+fn/6YVqtFUFCQrIZSmzRpIosZdIoeM3dxcUFERESJx0QqaSxNo9FUUpKSZWdnIzQ01GB1ugkTJhicrctRv379ZLk2i6enJ27cuKGIi7VylJ2djU8++QRJSUmwtbWFVqtFXFwcWrRogTVr1sjmprZnZ9OJpOgz8xYtWuDs2bP6qT+JiYlo0qSJ4FSGNBoNEhISEBUVBTMzM3Tv3l22m1PUrFkTU6ZMwZQpU0RHeaGCdzQFrl+/rl9iVm6mTZsmOoKi1axZE1u2bMHp06f1UxN9fHwMpvqlpqYabFwhQmnWp6+MnIo+M3dzc8OVK1fQrFkzmJmZ4c8//0TdunVRvXp12azRsmnTJuzYsQO9evWCVqvFr7/+io8//lg2qxE+a/fu3QgODtZvayfH28+ffadjYmKCevXqoVu3brI5S3veuXPncOXKFQwaNAgXLlyQ5di+ksllJklJKiOnos/MQ0NDRUco0c6dO7F79279UMWECRPwwQcfyLLM161bh61bt8r2TBcAevfujb1792LYsGFITk7Gjh07ZDktEQA2b96Mw4cPIyUlBf369cOcOXMwePBgjB49WnQ01VDKuWhl5JTXVIUysrGxwX/+8x98//33sLS0xJkzZ2BjY6P/IwcWFhYGV+Nr1Kgh2wti1tbWsi5yAJg6dSpSUlIAALVq1YJOp8P06dMFpyranj17sHHjRtSoUQP16tXDrl27ZLXLkBooZapqZeRU9Jl5SEgI7t69i0uXLmHMmDH48ccfkZiYaHD1W7TmzZvDy8sLAwYMgLm5OX7++WfUrl1bv/KfnFb7a9euHSZNmoTu3bsbXLDz8PAQF+o5d+7cwVdffQUAqF27Nnx9fWU1D/5ZpqamBsM/1apVk90ia6Qeij4z//3337FkyRJUq1YNtWvXxjfffINjx46JjmXAxsYGvXv31q9K2L17d3Tq1El0rCJlZWWhVq1aiImJQXR0tP6PnJiYmODy5cv6x9evX5ftyoRdunRBcHAwcnJycPjwYYwbNw52dnaiY5FKyfOnoJSev6ElNzdXdje5yOnMuyRBQUHIy8vDn3/+Ca1Wi5YtW8quKGfMmIFRo0ahQYMGAJ4u07tkyRLBqYo2ffp0fP/992jdujXCw8Nhb2+vX/2RjINj5v+l6NksGzZswKVLl3Dx4kX4+Phg79696Nu3r6xulX9+gwLg6dj0b7/9JijRi8XFxWHSpEmwsLCATqfDvXv3sHbtWnTs2FF0NAO5ubm4cuUKzM3N0bx5c9nOZCmg1WqRkJCA119/XVZr7SvF+vXr8fHHHxscW7ZsGSZPnmwwNVk00TkVXeZXrlxBfHw8tmzZAhsbGwwaNEhWd4Y9Ly8vD4cPH0ZMTEyh+dJyMHToUPj7++vLOyYmBgsWLMCuXbsEJ/uvojYk8fPzQ926dUVH07tx4wZ8fX0xadIkvPvuuxg2bBjS0tKg0+mwdOlS2Q6zyU1ISAjS0tLwyy+/GGwRmZ+fj9jYWPz73/8WmO6/5JJTXu+hSyktLQ2TJk3CtWvX0KRJE5ibmyMqKgqPHz9Gp06dZLthbpUqVeDs7Ky/gCc32dnZBmfhb731Fp48eSIwUWFFbUgybdo0WdxOXWDBggUYPXo07O3tsWvXLmRnZ+PQoUO4desW/P39FbOfpWh9+vTB9evXERUVha5du+qHKszMzGSxyXgBueRUZJkXnN2EhYXp1wfPy8vDqlWrEBgYiEWLFglO+F/P3uYrSRKuXr0qu3HoAnXr1sXhw4fh5OQEADh8+DAsLCzEhnrO7du34eXlhe3bt6Nq1arw9fWFm5ub6FgGkpOT9TtLnTx5En379oW5uTmaNWuGrKwswemUo0OHDujQoQPs7e1x/vx59OrVC/fv38cvv/wiqzu95ZJTnq1SgvPnzyMyMtLgWJUqVTB58mTZTVN7fjZIvXr1sGLFCjFhSjB//nxMmzYNM2fOBAA0btwYixcvFpzKkBI2JCk4M5MkCdHR0Rg2bJj+cXZ2tshoirRkyRLodDr9yoTR0dGIjY1FQECA4GSGROdUZJk/Owf6WSYmJrL7wZbrin5Fadq0KX744QdkZ2dDp9PJcoGtSZMmYfjw4UhKSsL48eP1G5LISevWrbFhwwbk5uaiatWqePvtt5Gbm4tNmzbhrbfeEh1PceLi4rB//34AT1chXbJkCVxdXQWnKkx0TkWWeXF3U8nljrCcnBysWrUKzs7O6NChA4KCgvD999+jbdu2WLZsmX5qnZzExsZi06ZNePDggcFUKjmt8vfshiRarVaWG5LMnTsXS5cu1c8GMjU1xcKFC3H9+nUsX75cdDzF0el0SElJgbW1NYCn18zkdtIGiM+pyDIvbjH41NRUAYkKW7hwIczMzGBjY4PffvsN+/fvx549exAfH4+AgACsXbtWdMRCZsyYAW9vb7Ro0UI2vxSfdfnyZVhaWsLKygqWlpbYu3cv0tPTZbfOTZ06dTBv3jxcvXoVLVu2BADMmzcPwNMZQnL75SN3n3zyCTQajX4W0IULF2S56YfonIqcmvj3338X+3E5rMvi6uqqf8s1Z84cVKlSBbNnzwYA9O/fHwcPHhQZr0hyXoEuPDwcq1atwsqVK2FhYQE3Nzf4+Pjg2rVraNu2raxmN5w7dw46nQ6zZs1CYGCg/l1Ofn4+5s2bJ5spdUqSnJyMmJgYmJubo3379vqzX7kRmVORZ+ZyKOuSPPv2Kjo62mBt67y8PBGRXujOnTsAgDfffBNhYWHo1auXwRoictiAePPmzdi1axcsLS2xZs0adO3aFb6+vsjNzYVGo5FVmZ88eRKnT59GSkoKVq5cqT9ubm4OLy8vgcmUqWBj7EePHkGSJFy6dAm3b9+W3cV50TkVWeZKYGFhgdjYWGRnZyMlJQXvvvsugKfF3rBhQ8HpDHl7e+uHVaKjow3GyOWyLrxOp9PvoxodHY3+/fsDgCzv/pw4cSKAp+8m5LRImVJ9/vnnaNSoEWJiYuDk5IRff/1VlhtkC88pUYVITEyUnJ2dpS5duki7d++WJEmS1q5dK9nZ2UkXLlwQnM7Q3bt3pcmTJ0suLi7S7NmzpYyMDNGRCnF3d5eePHkiPXjwQGrXrp108+ZNSZIk6f79+1Lfvn0FpyvagwcPpJkzZ0rDhw+XHjx4IPn5+Unp6emiYylOwb/vokWLpJiYGOn+/fuSq6ur4FSFic4pv0vCKtG6dWscPHgQ0dHR+n0+BwwYgEOHDqFDhw6C0xn64osvYG1tjSlTpiAvL0+W0yk9PT3h5eWFkSNHwt7eHo0bN8apU6fw8ccfY8iQIaLjFWnOnDlo37490tPTDe5WpbIpWKqhWbNmSExMRL169QQnKpronBxmqWAZGRk4cOBAoel+clpNMTk5GRs3bgQAdO/eXZZDA8OGDUP79u2RmpqKnj17Aniae+jQoRg4cKDgdEVTwt2qSmBnZ4dJkybpV8y8dOkSqlevLjpWIaJzsswr2IQJE2BpaYmWLVvKcrofAP2SCAX//exjOSl4R3P79m1cu3YNLi4uSEpKEpzqxZRwt6oSjBgxAllZWbCxscGyZctw5swZWV3wLiA8Z6UN6PxDubi4iI5QIg8Pj2Ify8mBAwckFxcXqVevXtK9e/ekd999VwoPDxcdq0jHjh2T3N3dpS5dukjjxo2TunXrJh09elR0LMXp16+f6AilIjqnIueZK8mUKVMwcuRI2Nraio7yQra2tgZ3pCYnJ6NBgwaQJEk2s1kKaDQabN26Fd7e3ggPD0dKSgpGjhyJAwcOiI5WpPv37+vvVu3YsSNvGCoHX19f2Nvbo0OHDgbDFnKYMvss0Tk5zFJB3n//fZiYmODx48c4ePAgGjRoADMzM1kWpJJuYjE1NTVYM8ba2lq2QxeZmZmIjIxEeno6JElCQkICAHldL1GCCxcu4MKFCwbH5PYzBIjPyTKvIFu3bhUdodSUcBNWgZYtW+Lbb79Ffn4+EhIS8N1336FNmzaiYxXps88+Q506dWR9vUQJdu/eXWgp5tu3b4sJUwzROTnMUsGuXLmC0NBQLF++HNevX8ecOXMwf/58NG/eXHQ0RcrOzkZoaChOnjwJnU4HOzs7TJgwQZYrPD67pAOVXVJSEiRJwtixY/H111/rZ4NptVqMGTMGP/30k+CET8klJ8u8gg0ZMgQTJkyAvb09AODEiRNYs2YNtm/fLjgZVbTp06dj1KhRsn3nIHf+/v6Ijo42WIkQeLosgoODg2wW25JLTpZ5BSvq7MzDw8NgByIqvd27dyM4OBiZmZkAoL8GUTAeLScajQaJiYmoX78+qlWrJsvrJUqwYcMGjB07VnSMEonOyTKvYCNGjEC/fv30N4scPHgQP/30k/4mHSobJycnrFu3Dq1atRIdpUSxsbGoX79+oeNKukYhBxkZGYiIiNBfSC4gtwvJonPyAmgFCwoKwpdffonFixejatWq6Ny5MwIDA0XHUixra2tFFDnwdH3457c3pLL7/PPPFXEhWXROnplXkvT0dNltjqxEgYGBSE5ORvfu3Q22D5TjEgSi5x2rhVIuJIvOyTPzCpaQkABfX188fvwYO3fuhLe3N1asWIF27dqJjqZIWVlZqFWrFmJiYgyOy7HMRc87Vos333wTiYmJsr+QLDonz8wr2LBhwxAQEIApU6YgPDwcJ06cwPLly7Fr1y7R0YgUQSkXkkXn5Jl5BcvJycEbb7yhf9y9e3cEBwcLTKRMH3/8MdavX6+/s/Z5cvvBBp5eEFuyZAlu3ryJVatWITg4GP7+/njllVdER1OUNWvWiI5QKqJzsswryJ49e6DRaGBhYYHExER9Ae3bt0+/7jGV3vz58wEo687a2bNno3v37oiNjdWvZz516lRs2LBBdDRFefXVV7F9+3ZERUUhPz8fdnZ28Pb2Fh2rENE5WeYVZMuWLdBoNJg7dy78/Pxw9epVdOrUCU2bNsWSJUtEx1OckydPFvtxOU7343rmxrF48WLcuHEDgwYNgiRJ2L17N27duoWZM2eKjmZAdE6WeQVr0qQJtm/fjuzsbOh0Olnedq4E0dHRxX5cjhdAuZ65cZw4cQLh4eH6752DgwNcXV0FpypMdE6WeQW5evUqevXq9cKPy3GMV87kuJVdSSZOnIjhw4cjKSkJ48ePR0xMDBYuXCg6luJotVrk5+frN+/WarUwMzMTnKow0TlZ5hWkSZMmHButAMePH8eKFSuQkZFhcJedHH859uzZE7a2tvr1zAMCAnjxsxxcXV3h4+ODAQMGAAAOHDgAFxcXwakKE52TUxMrCNdfqRh9+/aFn59fobvs5Dhm7uXlhZ07d+of63Q6uLu7K+IGGLk5duwYTp06BUmSYGdnBwcHB9GRiiQyJ8/MK8jbb78tOoIq1atXD46OjqJjFMvHxwenT58GALRp00b/S8fMzAzvv/++yGiKk5GRAa1Wi549e6Jnz56Ijo5Gy5YtRccqRA45eTWmgsyZM0d0BFXq1KkTgoKC8Pvvv+PMmTP6P3KyZcsWJCYmwtvbG4mJiUhISEBCQgLi4uKwatUq0fEUIz4+HgMGDEBcXJz+2MmTJ+Hu7o7ExESByQzJJSeHWUhRhg8fbjC8UnCX3ZYtWwSmKlpeXh5OnDiB9PR0g+NynHkjRyNGjMD48ePRtWtXg+PHjx/Hxo0bERYWJibYc+SSk8MspBhnzpyBmZkZLl68CBMTE7Rv3x7jx4/HO++8IzpakaZOnYo7d+7gjTfeMPgFxDIvnczMzEIFCQDvvfceQkJCBCQqmlxyssxJEU6dOoXp06dj/PjxmDlzJvLy8nD+/HlMnjwZISEhRf4wiXb58mXZbG2mRPn5+dDpdIXm5ut0OuTl5QlKVZhccnLMnBRh7dq12LBhAz744AO0bNkSbdu2xbBhwxAaGirbceg33ngDKSkpomMo1jvvvFPkeifr1q2Dra2tgERFk0tOjpmTIhQ31dPFxQURERGVG6gURo8ejfPnz6NVq1b6G0kAyHJ8X46ysrIwduxY3L17F23atEG1atUQHx8PS0tLhIaGymZ/ALnkZJmTIvTp0wcHDx6EubnhyGB+fj5cXFxkOZxRMD3xeV26dKnkJMolSRKioqKQkJAAU1NT2NraonPnzqJjFSKHnCxzUoSAgABUrVoVfn5++mNarRYLFy5ElSpVDI4T/ROxzEkRsrOz8cknnyApKQm2trbQarWIi4tDixYtsGbNGoNhDNGevVHoWQXTKBMSEgSkIrVjmZOinD59Wj81sUOHDrJ8y00kAsuciEgFODWRiEgFWOZERCrAMiciUgGWORGRCrDMiYhU4P8DVHPOEbmLe10AAAAASUVORK5CYII=",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Most important features that keep customers from churning\n",
- "print(weights.sort_values(ascending = False)[-8:].plot(kind='bar'))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Die drei wichtigsten Merkmale, die Kunden von der Abwanderung abhalten, sind:\n",
- "\n",
- "- Die Verträge, die für zwei Jahre gekündigt werden können (Contract_Two year),\n",
- "- Die Zeit, die man Kunde eines Unternehmens ist (Tenure) und\n",
- "- kein Abonnement für den Internetdienst (InternetService_No).\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 4.4. Modell-Optimierung"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Die Recall-Rate ist als Zielmetrik zu niedrig und muss daher erhöht werden. Daher werden die Metriken bei verschiedenen Schwellenwerten der logistischen Regression analysiert."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 204,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "******** For a Threshold about 0.05 ******\n",
- "Accuracy: 0.8588235294117647\n",
- "True Negatives: 367\n",
- "False Positives: 128\n",
- "False Negatives: 4\n",
- "True Positives: 436\n",
- "Recall: 0.990909090909091\n",
- "Precision: 0.7730496453900709\n",
- "\n",
- "******** For a Threshold about 0.1 ******\n",
- "Accuracy: 0.9144385026737968\n",
- "True Negatives: 420\n",
- "False Positives: 75\n",
- "False Negatives: 5\n",
- "True Positives: 435\n",
- "Recall: 0.9886363636363636\n",
- "Precision: 0.8529411764705882\n",
- "\n",
- "******** For a Threshold about 0.15 ******\n",
- "Accuracy: 0.9422459893048128\n",
- "True Negatives: 446\n",
- "False Positives: 49\n",
- "False Negatives: 5\n",
- "True Positives: 435\n",
- "Recall: 0.9886363636363636\n",
- "Precision: 0.8987603305785123\n",
- "\n",
- "******** For a Threshold about 0.2 ******\n",
- "Accuracy: 0.9657754010695188\n",
- "True Negatives: 468\n",
- "False Positives: 27\n",
- "False Negatives: 5\n",
- "True Positives: 435\n",
- "Recall: 0.9886363636363636\n",
- "Precision: 0.9415584415584416\n",
- "\n",
- "******** For a Threshold about 0.25 ******\n",
- "Accuracy: 0.9786096256684492\n",
- "True Negatives: 481\n",
- "False Positives: 14\n",
- "False Negatives: 6\n",
- "True Positives: 434\n",
- "Recall: 0.9863636363636363\n",
- "Precision: 0.96875\n",
- "\n",
- "******** For a Threshold about 0.3 ******\n",
- "Accuracy: 0.9818181818181818\n",
- "True Negatives: 486\n",
- "False Positives: 9\n",
- "False Negatives: 8\n",
- "True Positives: 432\n",
- "Recall: 0.9818181818181818\n",
- "Precision: 0.9795918367346939\n",
- "\n",
- "******** For a Threshold about 0.35 ******\n",
- "Accuracy: 0.986096256684492\n",
- "True Negatives: 490\n",
- "False Positives: 5\n",
- "False Negatives: 8\n",
- "True Positives: 432\n",
- "Recall: 0.9818181818181818\n",
- "Precision: 0.988558352402746\n",
- "\n",
- "******** For a Threshold about 0.4 ******\n",
- "Accuracy: 0.9871657754010695\n",
- "True Negatives: 491\n",
- "False Positives: 4\n",
- "False Negatives: 8\n",
- "True Positives: 432\n",
- "Recall: 0.9818181818181818\n",
- "Precision: 0.9908256880733946\n",
- "\n",
- "******** For a Threshold about 0.45 ******\n",
- "Accuracy: 0.9893048128342246\n",
- "True Negatives: 493\n",
- "False Positives: 2\n",
- "False Negatives: 8\n",
- "True Positives: 432\n",
- "Recall: 0.9818181818181818\n",
- "Precision: 0.9953917050691244\n",
- "\n",
- "******** For a Threshold about 0.5 ******\n",
- "Accuracy: 0.9882352941176471\n",
- "True Negatives: 493\n",
- "False Positives: 2\n",
- "False Negatives: 9\n",
- "True Positives: 431\n",
- "Recall: 0.9795454545454545\n",
- "Precision: 0.9953810623556582\n",
- "\n",
- "******** For a Threshold about 0.55 ******\n",
- "Accuracy: 0.9882352941176471\n",
- "True Negatives: 493\n",
- "False Positives: 2\n",
- "False Negatives: 9\n",
- "True Positives: 431\n",
- "Recall: 0.9795454545454545\n",
- "Precision: 0.9953810623556582\n",
- "\n",
- "******** For a Threshold about 0.6 ******\n",
- "Accuracy: 0.9893048128342246\n",
- "True Negatives: 494\n",
- "False Positives: 1\n",
- "False Negatives: 9\n",
- "True Positives: 431\n",
- "Recall: 0.9795454545454545\n",
- "Precision: 0.9976851851851852\n",
- "\n",
- "******** For a Threshold about 0.65 ******\n",
- "Accuracy: 0.9893048128342246\n",
- "True Negatives: 494\n",
- "False Positives: 1\n",
- "False Negatives: 9\n",
- "True Positives: 431\n",
- "Recall: 0.9795454545454545\n",
- "Precision: 0.9976851851851852\n",
- "\n",
- "******** For a Threshold about 0.7 ******\n",
- "Accuracy: 0.9903743315508021\n",
- "True Negatives: 495\n",
- "False Positives: 0\n",
- "False Negatives: 9\n",
- "True Positives: 431\n",
- "Recall: 0.9795454545454545\n",
- "Precision: 1.0\n",
- "\n",
- "******** For a Threshold about 0.75 ******\n",
- "Accuracy: 0.9903743315508021\n",
- "True Negatives: 495\n",
- "False Positives: 0\n",
- "False Negatives: 9\n",
- "True Positives: 431\n",
- "Recall: 0.9795454545454545\n",
- "Precision: 1.0\n",
- "\n",
- "******** For a Threshold about 0.8 ******\n",
- "Accuracy: 0.9893048128342246\n",
- "True Negatives: 495\n",
- "False Positives: 0\n",
- "False Negatives: 10\n",
- "True Positives: 430\n",
- "Recall: 0.9772727272727273\n",
- "Precision: 1.0\n",
- "\n",
- "******** For a Threshold about 0.85 ******\n",
- "Accuracy: 0.9882352941176471\n",
- "True Negatives: 495\n",
- "False Positives: 0\n",
- "False Negatives: 11\n",
- "True Positives: 429\n",
- "Recall: 0.975\n",
- "Precision: 1.0\n",
- "\n",
- "******** For a Threshold about 0.9 ******\n",
- "Accuracy: 0.9871657754010695\n",
- "True Negatives: 495\n",
- "False Positives: 0\n",
- "False Negatives: 12\n",
- "True Positives: 428\n",
- "Recall: 0.9727272727272728\n",
- "Precision: 1.0\n",
- "\n",
- "******** For a Threshold about 0.95 ******\n",
- "Accuracy: 0.9807486631016042\n",
- "True Negatives: 495\n",
- "False Positives: 0\n",
- "False Negatives: 18\n",
- "True Positives: 422\n",
- "Recall: 0.9590909090909091\n",
- "Precision: 1.0\n",
- "\n",
- "******** For a Threshold about 0.99 ******\n",
- "Accuracy: 0.9497326203208556\n",
- "True Negatives: 495\n",
- "False Positives: 0\n",
- "False Negatives: 47\n",
- "True Positives: 393\n",
- "Recall: 0.8931818181818182\n",
- "Precision: 1.0\n"
- ]
- }
- ],
- "source": [
- "# Testing the metrics at different thresholds\n",
- "threshold_list = [0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,.7,.75,.8,.85,.9,.95,.99]\n",
- "pred_proba_df = y_hat\n",
- "for i in threshold_list:\n",
- " print ('\\n******** For a Threshold about {} ******'.format(i))\n",
- " # Round up if value is above threshold\n",
- " y_test_pred = pred_proba_df.apply(lambda x: 1 if x>i else 0)\n",
- " # read metrics\n",
- " test_accuracy = metrics.accuracy_score(y_test, y_test_pred)\n",
- " print(\"Accuracy: {}\".format(test_accuracy))\n",
- " # Confusion matrix\n",
- " c = confusion_matrix(y_test, y_test_pred)\n",
- " tn, fp, fn, tp = c.ravel()\n",
- " recall = tp/(fn+tp)\n",
- " precision = tp/(tp+fp)\n",
- " # print metrics\n",
- " print(\"True Negatives: \" + str(tn))\n",
- " print(\"False Positives: \" + str(fp))\n",
- " print(\"False Negatives: \" + str(fn))\n",
- " print(\"True Positives: \" + str(tp))\n",
- " print(\"Recall: \" + str(recall))\n",
- " print(\"Precision: \" + str(precision))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Ein Schwellenwert von 0,3 bietet ein besseres Ergebnis für die Anwendung. Er erhöht die Wiederauffindbarkeit auf ein zufriedenstellendes Niveau von 73,21 %, was zu Lasten der Präzision geht. Die Präzision ist jedoch vernachlässigbar."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Daraus ergeben sich die folgenden Werte:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 205,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " precision recall f1-score support\n",
- "\n",
- " 0 0.98 0.98 0.98 495\n",
- " 1 0.98 0.98 0.98 440\n",
- "\n",
- " accuracy 0.98 935\n",
- " macro avg 0.98 0.98 0.98 935\n",
- "weighted avg 0.98 0.98 0.98 935\n",
- "\n",
- "Confusion matrix for the new threshold:\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 720x504 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " \n",
- "Metrics for the new threshold:\n",
- "Accuracy: 0.9818181818181818\n",
- "True Negatives: 486\n",
- "False Positives: 9\n",
- "False Negatives: 8\n",
- "True Positives: 432\n",
- "Recall: 0.9818181818181818\n",
- "Precision: 0.9795918367346939\n"
- ]
- }
- ],
- "source": [
- "# Threshold about 0,3\n",
- "y_test_pred = pred_proba_df.apply(lambda x: 1 if x>0.30 else 0)\n",
- "test_accuracy = metrics.accuracy_score(y_test, y_test_pred)\n",
- "c = confusion_matrix(y_test, y_test_pred)\n",
- "# read values from confusion matrix\n",
- "tn, fp, fn, tp = c.ravel()\n",
- "recall = tp/(fn+tp)\n",
- "precision = tp/(tp+fp)\n",
- "print(classification_report(y_test,y_test_pred))\n",
- "# create confusion matrix\n",
- "print(\"Confusion matrix for the new threshold:\")\n",
- "df_cm = pd.DataFrame(c, index=['No Churn','Churn'], columns=['No Churn', 'Churn'],)\n",
- "fig = plt.figure(figsize=[10,7])\n",
- "heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
- "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=14)\n",
- "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=14)\n",
- "plt.ylabel('True label')\n",
- "plt.xlabel('Predicted label')\n",
- "plt.show()\n",
- "print(\" \")\n",
- "# print metrics\n",
- "print(\"Metrics for the new threshold:\")\n",
- "print(\"Accuracy: {}\".format(test_accuracy))\n",
- "print(\"True Negatives: \" + str(tn))\n",
- "print(\"False Positives: \" + str(fp))\n",
- "print(\"False Negatives: \" + str(fn))\n",
- "print(\"True Positives: \" + str(tp))\n",
- "print(\"Recall: \" + str(recall))\n",
- "print(\"Precision: \" + str(precision))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Evaluation"
- },
- "source": [
- "Erwartungsgemäß steigt die Rate der fälschlicherweise als abgewandert eingestuften Kunden. Im Gegenzug steigt aber auch die Anzahl der Kunden, die korrekt als Abwanderer vorhergesagt werden (True Positives). Wie in der Hausarbeit ausgeführt, ist dies essentiell, denn im Zweifelsfall würde ein Kunde fälschlicherweise vom Serviceteam angerufen werden und diesen Anruf sogar als guten Service wahrnehmen und längerfristig an das Unternehmen binden."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false,
- "paragraph": "Umsetzung"
- },
- "source": [
- "# 5. Deployment"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "include": true,
- "paragraph": "Umsetzung"
- },
- "source": [
- "Die Umsetzung bzw. Einbindung des Datenmodells bietet sich in CRM-Systemen an. Auf Basis von Kundenmerkmalen kann automatisiert eine Vorhersage über eine potentielle Kundenabwanderung erstellt werden. Auf diese Weise lassen sich gefährdete Kundenbeziehungen identifizieren, in Form von Dashboards visualisieren sowie teil-automatisiert bearbeiten."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 206,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Separate individual (scaled) customer\n",
- "customer_df = X_test.iloc[896]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 207,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "SeniorCitizen 0.000000\n",
- "Dependents 0.000000\n",
- "tenure -0.302393\n",
- "PhoneService 1.000000\n",
- "PaperlessBilling 0.000000\n",
- "MultipleLines_Yes 0.000000\n",
- "InternetService_Fiber optic 0.000000\n",
- "InternetService_No 1.000000\n",
- "OnlineSecurity_Yes 0.000000\n",
- "TechSupport_Yes 0.000000\n",
- "StreamingTV_Yes 0.000000\n",
- "StreamingMovies_Yes 0.000000\n",
- "Contract_One year 0.000000\n",
- "Contract_Two year 1.000000\n",
- "PaymentMethod_Electronic check 0.000000\n",
- "Name: 1544, dtype: float64"
- ]
- },
- "execution_count": 207,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Overview about the customer\n",
- "customer_df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 208,
- "metadata": {},
- "outputs": [],
- "source": [
- "# execute prediction\n",
- "cust_pred = logistic_model.predict([customer_df])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 209,
- "metadata": {},
- "outputs": [],
- "source": [
- "# evaluate results\n",
- "def check_prediction(pred):\n",
- " if pred[0] == 1:\n",
- " print(\"The customer will probably churn! Inform Customer Relationship Management!\")\n",
- " else:\n",
- " print(\"The customer probably will not churn.\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 210,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The customer probably will not churn.\n"
- ]
- }
- ],
- "source": [
- "check_prediction(cust_pred)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "# Zusammenfassung"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "Das Notebook hat gezeigt, wie eine logistische Regression verwendet werden kann, um die Abwanderung von Kunden im Telekommunikationssegment vorherzusagen."
- ]
- }
- ],
- "metadata": {
- "branche": "Telekommunikation",
- "dataSource": "https://storage.googleapis.com/ml-service-repository-datastorage/Customer_Churn_Prediction_data.csv",
- "funktion": "Marketing",
- "interpreter": {
- "hash": "07673f924689cda9590af2124148040e2ce6fe7ee39bbd541ae69d9c32a71079"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.9"
- },
- "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/blob/main/CRM/Customer%20Churn%20Prediction/notebook.ipynb?ref_type=heads",
- "skipNotebookInDeployment": false,
- "teaser": "Ein wichtiges Ziel für jedes Unternehmen liegt in der Erhaltung wertvoller Kundenbeziehungen. Problematisch ist die Abwanderung von Kunden zu Wettbewerbern (= Churn). Der Datensatz für diese Demo wurde auf der Kaggle Data Science Plattform veröffentlicht. Die Zeilen des Datensatzes stellen jeweils einen Kunden dar. Die Spalten beschreiben die Merkmale der Kunden. Auf Basis der Merkmale wird versucht zu klassifizieren, ob ein Kunde das Unternehmen verlässt oder nicht. Verschiedene Schritte zur Datenvorbereitung werden umgesetzt. Als Datenmodell wird eine logisitische Regression verwendet. Das finale Datenmodell erreicht eine Genauigkeit von 76% und einen Recall von 73%. Fast drei Viertel aller abwanderungswilligen Kunden werden durch dieses Modell erkannt.",
- "title": "Vorhersage der Abwanderung von Kunden"
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+ "nbformat": 4,
+ "nbformat_minor": 4,
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "include": true,
+ "paragraph": "Business"
+ },
+ "source": [
+ "Ein wichtiges Ziel f\u00fcr jedes Unternehmen liegt in der Erhaltung wertvoller Kundenbeziehungen. F\u00fcr Unternehmen ist daher eine Einsch\u00e4tzung der Qualit\u00e4t der Kundenbeziehung wesentlich. Problematisch ist in diesem Sinne die Abwanderung von Kunden zu Wettbewerbern (= Churn). Soweit sich das Risiko der Abwanderung eines Kunden vorab einsch\u00e4tzen l\u00e4sst, k\u00f6nnen Gegenma\u00dfnahmen eingeleitet werden. Die Absch\u00e4tzung der Wahrscheinlichkeit, mit der ein Kunde abwandert oder zu einem Wettbewerber wechselt ist in diesem Sinne wesentlich. Dar\u00fcber hinaus stellt sich die Frage, anhand welcher Merkmale abwanderungswillige Kunden zu erkennen sind. Mit dieser Demo kann die Abwanderung eines Kunden vorhergesagt werden, so dass fr\u00fchzeitig entsprechende Ma\u00dfnahmen ergriffen werden k\u00f6nnen.\n"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": true,
+ "paragraph": "Daten"
+ },
+ "source": [
+ "Der Datensatz f\u00fcr diese Demo wurde auf der [Kaggle Data Science Plattform](https://www.kaggle.com/) ver\u00f6ffentlicht. Die Daten stammen von einem Unternehmen aus der Telekommunikationsbranche und wurde von IBM zur Verf\u00fcgung gestellt. Die Zeilen des Datensatzes stellen jeweils einen Kunden dar. Die Spalten beschreiben die Merkmale der Kunden. Auf Basis der Merkmale wird versucht zu klassifizieren, ob ein Kunde das Unternehmen verl\u00e4sst oder nicht. Daher enthalten die historischen Daten auch die Zielvariable \u201eChurn\u201c (= Abwanderung), die Auskunft dar\u00fcber gibt, ob ein Kunde abgewandert ist. Die Kundenmerkmale sind in drei numerische Variablen (z.B. monatlicher Rechnungsbetrag, Alter der Kundenbeziehung, etc.) und 18 kategoriale Variablen (z.B. Geschlecht, diverse Servicekategorien, etc.) unterteilt."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "include": true,
+ "paragraph": "Datenvorbereitung"
+ },
+ "source": [
+ "Zun\u00e4chst wird der Typ der Daten nach dem Einlesen in das Notebook \u00fcberpr\u00fcft. Einlesefehler werden entsprechend korrigiert. Danach werden die einzelnen Kundenmerkmale einer deskriptiven Analyse unterzogen. Damit l\u00e4sst sich der Zusammenhang zur Zielvariable Kundenabwanderung visuell darstellen. Auf der Grundlage einer Korrelationsanalyse werden die Zusammenh\u00e4nge zwischen Kundenmerkmalen und der Zielvariable untersucht. Kundenmerkmale die keinen Mehrwert bieten werden entfernt (= Dimensionsreduktion). Die Ausgewogenheit des Datensatzes in Bezug auf die Zielvariable wird grafisch dargestellt. Die Kunden mit Abwanderung machen etwa 27 % des gesamten Datensatzes aus, w\u00e4hrend loyale Kunden etwa 73% ausmachen. Daher liegt ein unausgewogener Datensatz vor. Anschlie\u00dfend werden die kategorialen Werte umgewandelt (= Bildung von Dummy Variablen). Die einzelnen Kundenmerkmale werden auf Multikollinearit\u00e4t und statistische Signifikanz gepr\u00fcft. Schlie\u00dflich werden alle Kundenmerkmale auf ein gemeinsames Messniveau gebracht (= Standardisierung). Durch ein Undersampling wird die Unausgewogenheit des Datensatzes ausgeglichen. Abschlie\u00dfend werden Trainings- und Testdaten gebildet."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "include": true,
+ "paragraph": "Datenmodell"
+ },
+ "source": [
+ "Als Datenmodell wird eine logisitische Regression verwendet. Dies bietet sich zur Klassifikation der Kunden auf eine bin\u00e4re Zielvariable (= Kundenabwanderung) an."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "include": true,
+ "paragraph": "Evaluation"
+ },
+ "source": [
+ "F\u00fcr die Bewertung der Qualit\u00e4t einer Klassifikation werden Metriken wir Accuracy (= allgemeine Genauigkeit der Klassifikation), Precision (= Pr\u00e4zision der Vorhersage der Kundenabwanderung) und Recall (= Menge der abwanderungswilligen Kunden die korrekt klassifiziert wurden) genutzt. In einer ersten Modellstufe wird eine Accuracy von 80%, ein Recall von 53% sowie eine Precision von 68% erreicht. Insbesondere der Recall ist hier zu schlecht, da nur etwas mehr als die H\u00e4lfte der abwanderungswilligen Kunden erkannt werden. In einer zweiten Modellstufe wird eine Hyperparameteroptimierung vorgenommen. Durch eine Ver\u00e4nderung des Treshholds f\u00fcr die Klassifikation wird eine Accuracy von 76%, ein Recall von 73% und eine Precision von 53% erreicht. Fast drei Viertel aller abwanderungswilligen Kunden werden durch dieses Modell erkannt."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "include": true,
+ "paragraph": "Umsetzung"
+ },
+ "source": [
+ "Die Umsetzung bzw. Einbindung des Datenmodells bietet sich in CRM-Systemen an. Auf Basis von Kundenmerkmalen kann automatisiert eine Vorhersage \u00fcber eine potentielle Kundenabwanderung erstellt werden. Auf diese Weise lassen sich gef\u00e4hrdete Kundenbeziehungen identifizieren, in Form von Dashboards visualisieren sowie teil-automatisiert bearbeiten."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": false,
+ "paragraph": "Business",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ ]
+ },
+ "source": [
+ "# 1. Business Understanding"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": false,
+ "paragraph": "Daten",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ ]
+ },
+ "source": [
+ "# 2. Daten und Datenverst\u00e4ndnis"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 2.1. Import von relevanten Modulen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import statsmodels.api as sm\n",
+ "import warnings\n",
+ "import imblearn\n",
+ "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from imblearn.under_sampling import InstanceHardnessThreshold\n",
+ "from sklearn import metrics\n",
+ "from sklearn.metrics import classification_report, confusion_matrix\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "sns.set()\n",
+ "# remove warnings\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 2.2. Daten einlesen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>customerID</th>\n",
+ " <th>gender</th>\n",
+ " <th>SeniorCitizen</th>\n",
+ " <th>Partner</th>\n",
+ " <th>Dependents</th>\n",
+ " <th>tenure</th>\n",
+ " <th>PhoneService</th>\n",
+ " <th>MultipleLines</th>\n",
+ " <th>InternetService</th>\n",
+ " <th>OnlineSecurity</th>\n",
+ " <th>...</th>\n",
+ " <th>DeviceProtection</th>\n",
+ " <th>TechSupport</th>\n",
+ " <th>StreamingTV</th>\n",
+ " <th>StreamingMovies</th>\n",
+ " <th>Contract</th>\n",
+ " <th>PaperlessBilling</th>\n",
+ " <th>PaymentMethod</th>\n",
+ " <th>MonthlyCharges</th>\n",
+ " <th>TotalCharges</th>\n",
+ " <th>Churn</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>7590-VHVEG</td>\n",
+ " <td>Female</td>\n",
+ " <td>0</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>1</td>\n",
+ " <td>No</td>\n",
+ " <td>No phone service</td>\n",
+ " <td>DSL</td>\n",
+ " <td>No</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Month-to-month</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Electronic check</td>\n",
+ " <td>29.85</td>\n",
+ " <td>29.85</td>\n",
+ " <td>No</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>5575-GNVDE</td>\n",
+ " <td>Male</td>\n",
+ " <td>0</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>34</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>DSL</td>\n",
+ " <td>Yes</td>\n",
+ " <td>...</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>One year</td>\n",
+ " <td>No</td>\n",
+ " <td>Mailed check</td>\n",
+ " <td>56.95</td>\n",
+ " <td>1889.5</td>\n",
+ " <td>No</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3668-QPYBK</td>\n",
+ " <td>Male</td>\n",
+ " <td>0</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>2</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>DSL</td>\n",
+ " <td>Yes</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Month-to-month</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Mailed check</td>\n",
+ " <td>53.85</td>\n",
+ " <td>108.15</td>\n",
+ " <td>Yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>7795-CFOCW</td>\n",
+ " <td>Male</td>\n",
+ " <td>0</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>45</td>\n",
+ " <td>No</td>\n",
+ " <td>No phone service</td>\n",
+ " <td>DSL</td>\n",
+ " <td>Yes</td>\n",
+ " <td>...</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>One year</td>\n",
+ " <td>No</td>\n",
+ " <td>Bank transfer (automatic)</td>\n",
+ " <td>42.30</td>\n",
+ " <td>1840.75</td>\n",
+ " <td>No</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>9237-HQITU</td>\n",
+ " <td>Female</td>\n",
+ " <td>0</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>2</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>Fiber optic</td>\n",
+ " <td>No</td>\n",
+ " <td>...</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Month-to-month</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Electronic check</td>\n",
+ " <td>70.70</td>\n",
+ " <td>151.65</td>\n",
+ " <td>Yes</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows \u00d7 21 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " customerID gender SeniorCitizen Partner Dependents tenure PhoneService \\\n",
+ "0 7590-VHVEG Female 0 Yes No 1 No \n",
+ "1 5575-GNVDE Male 0 No No 34 Yes \n",
+ "2 3668-QPYBK Male 0 No No 2 Yes \n",
+ "3 7795-CFOCW Male 0 No No 45 No \n",
+ "4 9237-HQITU Female 0 No No 2 Yes \n",
+ "\n",
+ " MultipleLines InternetService OnlineSecurity ... DeviceProtection \\\n",
+ "0 No phone service DSL No ... No \n",
+ "1 No DSL Yes ... Yes \n",
+ "2 No DSL Yes ... No \n",
+ "3 No phone service DSL Yes ... Yes \n",
+ "4 No Fiber optic No ... No \n",
+ "\n",
+ " TechSupport StreamingTV StreamingMovies Contract PaperlessBilling \\\n",
+ "0 No No No Month-to-month Yes \n",
+ "1 No No No One year No \n",
+ "2 No No No Month-to-month Yes \n",
+ "3 Yes No No One year No \n",
+ "4 No No No Month-to-month Yes \n",
+ "\n",
+ " PaymentMethod MonthlyCharges TotalCharges Churn \n",
+ "0 Electronic check 29.85 29.85 No \n",
+ "1 Mailed check 56.95 1889.5 No \n",
+ "2 Mailed check 53.85 108.15 Yes \n",
+ "3 Bank transfer (automatic) 42.30 1840.75 No \n",
+ "4 Electronic check 70.70 151.65 Yes \n",
+ "\n",
+ "[5 rows x 21 columns]"
+ ]
+ },
+ "execution_count": 142,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_raw = pd.read_csv(\"https://storage.googleapis.com/ml-service-repository-datastorage/Customer_Churn_Prediction_data.csv\")\n",
+ "data_raw.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 143,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 7043 entries, 0 to 7042\n",
+ "Data columns (total 21 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 customerID 7043 non-null object \n",
+ " 1 gender 7043 non-null object \n",
+ " 2 SeniorCitizen 7043 non-null int64 \n",
+ " 3 Partner 7043 non-null object \n",
+ " 4 Dependents 7043 non-null object \n",
+ " 5 tenure 7043 non-null int64 \n",
+ " 6 PhoneService 7043 non-null object \n",
+ " 7 MultipleLines 7043 non-null object \n",
+ " 8 InternetService 7043 non-null object \n",
+ " 9 OnlineSecurity 7043 non-null object \n",
+ " 10 OnlineBackup 7043 non-null object \n",
+ " 11 DeviceProtection 7043 non-null object \n",
+ " 12 TechSupport 7043 non-null object \n",
+ " 13 StreamingTV 7043 non-null object \n",
+ " 14 StreamingMovies 7043 non-null object \n",
+ " 15 Contract 7043 non-null object \n",
+ " 16 PaperlessBilling 7043 non-null object \n",
+ " 17 PaymentMethod 7043 non-null object \n",
+ " 18 MonthlyCharges 7043 non-null float64\n",
+ " 19 TotalCharges 7043 non-null object \n",
+ " 20 Churn 7043 non-null object \n",
+ "dtypes: float64(1), int64(2), object(18)\n",
+ "memory usage: 1.1+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data_raw.info()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenvorbereitung"
+ },
+ "source": [
+ "Der Datensatz besteht aus 7.043 Zeilen und 21 Attributen:\n",
+ "\n",
+ "- Zu prognostizierendes Attribut: Abwanderung\n",
+ "- Numerische Attribute: Vertragsdauer, Monatsgeb\u00fchren und Gesamtgeb\u00fchren.\n",
+ "- Kategorische Attribute: CustomerID, Gender, SeniorCitizen, Partner, Angeh\u00f6rige, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod.\n",
+ "Es wurden nicht alle Datentypen korrekt eingelesen:\n",
+ "\n",
+ "- TotalCharges muss ein numerischer Wert sein -> in Float umwandeln\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 144,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>customerID</th>\n",
+ " <th>gender</th>\n",
+ " <th>SeniorCitizen</th>\n",
+ " <th>Partner</th>\n",
+ " <th>Dependents</th>\n",
+ " <th>tenure</th>\n",
+ " <th>PhoneService</th>\n",
+ " <th>MultipleLines</th>\n",
+ " <th>InternetService</th>\n",
+ " <th>OnlineSecurity</th>\n",
+ " <th>...</th>\n",
+ " <th>DeviceProtection</th>\n",
+ " <th>TechSupport</th>\n",
+ " <th>StreamingTV</th>\n",
+ " <th>StreamingMovies</th>\n",
+ " <th>Contract</th>\n",
+ " <th>PaperlessBilling</th>\n",
+ " <th>PaymentMethod</th>\n",
+ " <th>MonthlyCharges</th>\n",
+ " <th>TotalCharges</th>\n",
+ " <th>Churn</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>0 rows \u00d7 21 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "Empty DataFrame\n",
+ "Columns: [customerID, gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod, MonthlyCharges, TotalCharges, Churn]\n",
+ "Index: []\n",
+ "\n",
+ "[0 rows x 21 columns]"
+ ]
+ },
+ "execution_count": 144,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# test for duplicates\n",
+ "data_raw[data_raw.duplicated(keep=False)]"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Keine Duplikate im Datensatz"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 2.3. Datenbereinigung"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Hier sollten die ersten Lesefehler korrigiert werden, bevor die eigentliche Datenaufbereitung erfolgt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 145,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 7043 entries, 0 to 7042\n",
+ "Data columns (total 21 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 customerID 7043 non-null object \n",
+ " 1 gender 7043 non-null object \n",
+ " 2 SeniorCitizen 7043 non-null int64 \n",
+ " 3 Partner 7043 non-null object \n",
+ " 4 Dependents 7043 non-null object \n",
+ " 5 tenure 7043 non-null int64 \n",
+ " 6 PhoneService 7043 non-null object \n",
+ " 7 MultipleLines 7043 non-null object \n",
+ " 8 InternetService 7043 non-null object \n",
+ " 9 OnlineSecurity 7043 non-null object \n",
+ " 10 OnlineBackup 7043 non-null object \n",
+ " 11 DeviceProtection 7043 non-null object \n",
+ " 12 TechSupport 7043 non-null object \n",
+ " 13 StreamingTV 7043 non-null object \n",
+ " 14 StreamingMovies 7043 non-null object \n",
+ " 15 Contract 7043 non-null object \n",
+ " 16 PaperlessBilling 7043 non-null object \n",
+ " 17 PaymentMethod 7043 non-null object \n",
+ " 18 MonthlyCharges 7043 non-null float64\n",
+ " 19 TotalCharges 7032 non-null float64\n",
+ " 20 Churn 7043 non-null object \n",
+ "dtypes: float64(2), int64(2), object(17)\n",
+ "memory usage: 1.1+ MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "# convert total charges\n",
+ "data_raw['TotalCharges'] = pd.to_numeric(data_raw['TotalCharges'], errors='coerce')\n",
+ "data_raw.info()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Die Konvertierung der TotalCharges hat zu Nullwerten gef\u00fchrt. Diese Nullwerte m\u00fcssen korrigiert werden."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# Remove zero values\n",
+ "# axis = 0 rows / axis = 1 columns\n",
+ "data_no_mv = data_raw.dropna(axis=0)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 2.4. Deskriptive Analytik"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "In diesem Teil des Notebooks soll das Datenverst\u00e4ndnis mit Hilfe der deskriptiven Analytik ber\u00fccksichtigt werden. Nach dem Entfernen der Nullwerte besteht der Datensatz aus 7032 Zeilen, von denen jeweils eine einen Kunden beschreibt, und 21 Spalten, die die Attribute des Kunden definieren. Mit Hilfe dieser Daten soll versucht werden, zu klassifizieren, ob ein Kunde abwandert oder nicht. Zu diesem Zweck enthalten die historischen Daten die Zielvariable \"Churn\", die Auskunft dar\u00fcber gibt, ob ein Kunde abgewandert ist.\n"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 2.4.1. Kontinuierliche Merkmale"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Zun\u00e4chst werden die Verteilungen der kontinuierlichen Merkmale einzeln untersucht und in einem zweiten Schritt die kategorialen Merkmale in Zusammenhang mit der Zielvariablen gesetzt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# load continous features\n",
+ "numeric_data = data_no_mv.select_dtypes(include=[np.number])"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Besitz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.FacetGrid at 0x23e942418e0>"
+ ]
+ },
+ "execution_count": 148,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 360x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.displot(numeric_data[\"tenure\"])"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "- Keine Normalverteilung erkennbar.\n",
+ "- Keine Ausrei\u00dfer erkennbar.\n",
+ "- Kunden sind potentiell gleichm\u00e4\u00dfig \u00fcber die einzelnen Monate verteilt, aber eine gro\u00dfe Anzahl von Kunden ist noch nicht lange im Unternehmen.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='tenure', ylabel='Density'>"
+ ]
+ },
+ "execution_count": 149,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"tenure\"],\n",
+ " bins=10,\n",
+ " color='orange',\n",
+ " label='Non-Churn',\n",
+ " kde=True)\n",
+ "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"tenure\"],\n",
+ " bins=10,\n",
+ " color='blue',\n",
+ " label='Churn',\n",
+ " kde=True)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Kunden, die noch nicht lange bei dem Unternehmen sind, werden eher abwandern als langj\u00e4hrige Kunden."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Monatliche Kosten"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='MonthlyCharges', ylabel='Density'>"
+ ]
+ },
+ "execution_count": 150,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.distplot(numeric_data[\"MonthlyCharges\"])"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "- Es ist keine Normalverteilung erkennbar.\n",
+ "- Die meisten Kunden befinden sich im vorderen Teil der Verteilung und zahlen relativ niedrige monatliche Geb\u00fchren.\n",
+ "- Dennoch verl\u00e4uft die Kurve gleichm\u00e4\u00dfig mit einem erneuten Anstieg nach hinten und dementsprechend k\u00f6nnen keine Ausrei\u00dfer identifiziert werden.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 151,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='MonthlyCharges', ylabel='Density'>"
+ ]
+ },
+ "execution_count": 151,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"MonthlyCharges\"],\n",
+ " bins=10,\n",
+ " color='orange',\n",
+ " label='Non-Churn',\n",
+ " kde=True)\n",
+ "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"MonthlyCharges\"],\n",
+ " bins=10,\n",
+ " color='blue',\n",
+ " label='Churn',\n",
+ " kde=True)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "- Kunden mit niedrigen monatlichen Geb\u00fchren sind eher abwanderungsbereit.\n",
+ "- Der Abwanderungstrend zwischen Kunden, die abwandern, und Kunden, die nicht abwandern, gleicht sich an, wenn die monatlichen Geb\u00fchren steigen.\n"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Gesamtkosten"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 152,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
+ ]
+ },
+ "execution_count": 152,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.distplot(numeric_data[\"TotalCharges\"])"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "- Die Kurve flacht nach hinten hin extrem stark ab.\n",
+ "- Es sind \u00c4hnlichkeiten mit der Exponentialverteilung zu erkennen. -> Test der logarithmischen Transformation zur Erreichung einer Normalverteilung.\n",
+ "- Es ist fraglich, ob es Ausrei\u00dfer im hinteren Teil gibt. -> Boxplot\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 153,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Boxplot f\u00fcr TotalCharges erstellen, um sicherzustellen, dass keine Ausrei\u00dfer vorhanden sind.\n",
+ "plt.boxplot(numeric_data[\"TotalCharges\"])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "- Boxplot zeigt keine Ausrei\u00dfer.\n",
+ "- Dies bedeutet, dass auch bei den Gesamtkosten keine Ausrei\u00dfer festgestellt werden k\u00f6nnen.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 154,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
+ ]
+ },
+ "execution_count": 154,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# logarithmic transformation\n",
+ "log_charges = np.log(data_no_mv[\"TotalCharges\"])\n",
+ "sns.distplot(log_charges)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "- Auch die Transformation mit Hilfe des Logarithmus f\u00fchrt nicht zu einer Normalverteilung.\n",
+ "- Vor weiteren Transformationen sollte zun\u00e4chst die Korrelation mit anderen Variablen untersucht werden.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 155,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='TotalCharges', ylabel='Density'>"
+ ]
+ },
+ "execution_count": 155,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.distplot(data_no_mv[data_no_mv.Churn == 'No'][\"TotalCharges\"],\n",
+ " bins=10,\n",
+ " color='orange',\n",
+ " label='Non-Churn',\n",
+ " kde=True)\n",
+ "sns.distplot(data_no_mv[data_no_mv.Churn == 'Yes'][\"TotalCharges\"],\n",
+ " bins=10,\n",
+ " color='blue',\n",
+ " label='Churn',\n",
+ " kde=True)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "\n",
+ "Die Verteilung ist \u00fcber die gesamte Bandbreite der Kosten sowohl bei den abwandernden als auch bei den nicht abwandernden Kunden nahezu identisch.\n"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Korrelationsanalyse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:>"
+ ]
+ },
+ "execution_count": 156,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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RpAclhBDCIskiCVFas5+/9918xYMxYfPr5q7CQ+/GvLXmroJVqPogCpEhPiGEEBbJRlbxCSGEsEQyByWEEMIiyRCfEEIIS6SkByWEEMIiySo+IYQQlkjJIgkhhBAWSXpQQgghLJLMQQkhhLBIsopPCCGEJZJVfEIIISxTOc1BJSQk8OGHH2IwGAgPD2fgwIGmY4mJiYwfP96UzsjIoFq1anzzzTf3LE8ClBBCWJnyWMWXkpJCTEwMGzZswN7env79++Pn50f9+vUBaNy4MZs2bQIgNzeXvn37MnXq1BLLlAAlhBBWRpWhB6XT6dDpdMX2u7q64urqakrv3r0bf39/3NzcAOjatSubN28mIiKi2LlLliyhTZs2tG7dusRrS4ASQghrU4Y5qPj4eOLi4ortj4iIIDIy0pROTU3Fw8PDlPb09OTIkSPFzsvKymLdunUkJCT85bUlQAkhhLUpQw8qPDycnj17Ftt/Z+8JwGg0orkj8CmliqRv+frrr+ncuTM1a9b8y2tLgBJCCCtTllV8fx7Kuxdvb28OHjxoSqelpeHp6Vks3w8//MCwYcNKde3KsRheCCHEA6M02lJvpRUQEMCePXvIyMggNzeXrVu30qFDh6LXVYrjx4/TsmXLUpUpAUoIIayM0tiUeistLy8vRo0aRVhYGC+++CLBwcH4+voydOhQjh49ChQuLbezs8PBwaFUZcoQnxBCWJty+h1USEgIISEhRfYtW7bM9O+aNWuya9euUpcnAUoIIayM3ElCCCGERSrL0J05SYASQghrIz0oIYQQlshYhtV55iQBSgghrIwM8QkhhLBMMsQnhBDCEqlK8hNYCVBCCGFlZJm5EEIIiyRzUEIIISySrOITQghhkWSITwghhEVSSIASlVDb1jUYFlYXezsbfj+fzexFJ8nJLSiSJ+hZT17uVRulIC+/gIVLz3DyzA0AvvksgLSr+aa8azZcYtvO1Aptw8Oi+Yo5ZB09xdmYFeauSqWz5+AvLFv1L/R6PfUer8PYyOG4ODsXyXP2/EXeX7aS7OwcbGxsGP1/Q2lYvx4ALwz6Jx7uNUx5+78YQpdn21doG8rTQzMHlZSUxHPPPUe/fv2YNm2aaX9iYiIvvvgis2fPplevXmW66Lp163B2diY4OJjx48fzzDPPFCsjNjYWoMgjhf/s7NmzREdH88cffwDw5JNPMmnSJGrUqFGq80VRbq52TBzZkDfG/krSlVzeCK/LG6/W5b0Pz5jy1H7Uif97rR5D3vyF9Gs38W9Vg1kTn6L34H3UftSJrCw9r408ZMZWVH5VGtXjqUVTcHvGl6yjp8xdnUon87qOuYs+JG7ONHxqPcKS+M9YumoNo4b/05QnLz+ft6bOZGzEcPxbt+TnfQeYsSCW1R/EcDHpMq5Vq7B8YbQZW1G+KksPqlRh1M3Njf/85z8UFNz+Jv3dd99Ro0aNEs66t19++YWbN2/+rXNvSUlJISwsjJdeeomEhAS+/vprGjRoQERExH2Va83atKxO4ukskq7kAvDV95fp0tGrSB693sjc2FOkXyt8/06cyaKGmz22thqaNXKlwKhYPKcFnyxqxav9H8OmcnxRsyiPvTGQSyvWc+XLzeauSqV04L+HaVT/CXxqPQLAC8934YedP6OUuiPPEWp5e+HfuvDBef94pjVTx74JwPETJ7Gx0RA5YQqDo8YQ//kXFBQYK7wd5cmo0ZZ6M6dSDfG5uLjQqFEjDhw4gL+/PwC7du0iICAAgB9//JGFCxdiNBqpXbs206ZNw93dncDAQF544QV+/vlncnNzmTt3Ljqdjh07drB37148PDwA+Omnn1izZg3p6ekMHz6cfv36ma69fv169u7dy3vvvQcU9qwcHBzIycnB39+fwMBAADQaDUOHDsXHxweDwQDAkSNH6N+/PykpKfTq1YvIyEhu3LjBxIkTSUlJITU1lbZt2zJz5kz279/PvHnzMBqNNGjQgMmTJzN27FguXrxI7dq1SU5OJi4ujkceeYTo6Gj2799PQUEBvXr14tVXXyU5OZm33nqLnJzC4YLJkyfTokWLB/MuVRAvDwdS7xieS7uaTxUXW5ydtKZhvuTUfJJTb+eJHPIEP+9Px2BQaLUaDv6ayUfxZ7G11RD9TjOycwys//qPCm9LZXZ85HQA3Lv8w8w1qZxSr6bj4V7TlPZwr0l2Ti45ubmmYb6ky5ep4eZGdOxHnDl3gSouzgx/dSAABUYjrZo34/WwlzEYCpgwfQ7Ozk70faGHWdpTHirLEF+pa9mtWze2bNkCFH7wN2zYEDs7O9LT03nnnXdYvHgxCQkJPP3000WGAt3c3Pjiiy/o378/S5YsISAggMDAQKKiomjfvnBM9+bNm6xfv54lS5YQExNT5Lrdu3dnz5493LjxvzmOb74hNDSUxMREnnrqqSJ5tVotwcHB2NoWxt309HRWrVrFl19+yfLly7lx4wY//fQTjRs3Zu3atWzZsoUDBw5w/PhxAM6fP098fDxz585l8eLF1K1bl2+//ZYRI0Zw6lThUMu6desA+Oqrr/jiiy/Yvn07Bw8e5IsvvuDZZ59lw4YNREVFcehQ5Rvm0mg03PEl08RoLL7T0cGG6eOa4POIE3NjTwKQsDWZhUvPkJdv5EZ2AWs3JdGhrXt5V1uIIpRSaO6ySs3mju68wVDAvkP/JTjoOZYumE2v4OcZN20ON/V6goOeY+Trg3FydKRqFRf6hgbz894DFdmEcqfQlHozp1IHqMDAQP79739jNBr5/vvv6datGwBOTk74+vri4+MDQL9+/di7d6/pvFtBqEGDBmRmZt617Oeeew6NRkODBg24du1akWMuLi507NiRbdu2cfDgQWrXro2XlxcajQZ7e/sS69y+fXvs7e2pUaMG1atX5/r16wQHB/OPf/yDTz75hBkzZpCZmUlOTg4AdevWpWrVqkBhDzE0NBSAZs2a8eSTTwKwZ88eduzYQWhoKH379iU5OZmTJ0/Stm1bVqxYwejRo8nMzGTQoEGlfWktRkpaHu41br+m7jUd0GXpycsvOrzh5eHAR/NaUmBURE46zI3swt5V106ePPG4iymfBigw3CXiCVGOPD3cSc+4/TlyNT2DqlVccHJ0NO1zr1Gdx2o/SpOGDQBo59cGo9HIleQUtv74b34/f8GUVymFVls5fjdUWkqjKfVmTqUOULeG+Q4dOsTevXtNw3tGY9EPL6WUaYgNMD17/m7faG659ebfK0/v3r355ptvSEhIMC2maNq0KceOHSuSz2g0EhERwdWrVwFMPalbZSulWL16NdHR0dSoUYNBgwbxxBNPmMamHe/4D6zVaouMWd9SUFDAmDFj2LRpE5s2bWLt2rX06dOHVq1a8e2339KuXTu+++47hg8ffs/2Wqr9/73GUw1d8XnECYAXu9XiP/vSi+RxctISO6s5O3dfZeq8RG7evP3+16vjwpCBj2NjA/b2NvQOfpTt/5EVfKJitWnhy28nT5N0+QoAX2/exj+eaV0kzzOtWnIlJZWTZ84CcPj4b2g0Gry9PDl38RIr1qyjoMBIfv5NvvpuC4HtAyq8HeVJKU2pN3Mq0zLzbt268d5779G0aVPTh39eXh6HDx8mKSkJHx8f1q5di5+fX4nlaLXaIgsu/krr1q1JTk7mjz/+YNKkSUBhTy00NJSdO3fSsWNHlFJ88MEHpKen4+5+72GlXbt20a9fP0JCQjh69CgnTpzAaDQW6f4DtG3bloSEBBo1asTJkyc5ffo0Go0Gf39/1q1bR6dOnbh58yYvv/wy7777Ljt37sTLy4vw8HD8/Pzo2bNnqdtnKTKv65n1/klmTGiCra2GP5LzmLHgBA3rV2F8ZENeG3mI3j1q4eXhSIe27kWG70ZOPsyKzy/w/4bVJz62Nba2Gn78+SoJW5PN2CJhjaq7VWNc1BtMmbsAvcFALW9vJr45ghOnf2fe4iUsXxhNzepuzJj4Fgs/Wk5ufh72dnZMGz8aB3t7wvv34f0lKxgc9RaGggKe/Yc/PboEmrtZD9RDebPYTp06MWnSJEaOHGna5+7uzrRp04iIiECv11OrVi1mzpxZYjkBAQEsWLDANJxWGl26dCEzM9M0rOfh4cGyZcuIjo5m/vz5FBQU0KRJExYvXlxiOeHh4UydOpWlS5dSpUoVWrZsSVJSEnXq1CmSb8SIEUyYMIGQkBDq1KmDu7s7jo6O9O/fnwsXLtCzZ08MBgO9evXCz8+POnXqMHr0aDZs2IBWq2Xu3Lmlbpsl2Xsog72HMorsO3nmhmnp+KdfXOLTLy7d8/zZi2RZ9INyZMgEc1eh0vJv3dK0Qu+WPy8db/5UEz6cX/yzytHBgXFRb5R7Hc3JWE4BKiEhgQ8//BCDwUB4eDgDBw4scvzs2bNMmTKF69ev4+HhwYIFC6hWrdo9y9Oou41jWRClFHq9ntdee42JEycWWxhRXjZt2oSPjw+tWrXi8uXLDBo0iB9++KFYT+tBaBey84GXKYqasPl1c1fhoff00bXmroJVeKRRi/su48TvSaXO2+gJn1LlS0lJYcCAAWzYsAF7e3v69+/PggULqF+/PlD4Wf78888zadIkOnTowPz581FKMWbMmHuWafF3kkhLS6NHjx707du3woITQL169ZgyZYpp+G/atGnlEpyEEKKilWV1nk6nQ6fTFdvv6uqKq6urKb179278/f1xc3MDoGvXrmzevNn029Tjx4/j7OxMhw4dABg+fPhdy72TxQcoT09PDhyo+CWezZo1Y8OGDRV+XSGEKG9lWfwQHx9PXFxcsf0RERFF7tSTmppq+m0rFH52HzlyxJS+ePEi7u7uTJw4kcTEROrVq8fbb79d4rUtPkAJIYR4sMrSgwoPD7/roq87e09QuIr6zpXYf/49msFgYP/+/Xz66ac0a9aMhQsXMmfOHObMmXPPa0uAEkIIK1OWAPXnobx78fb25uDBg6Z0Wloanp6eprSHhwePPfYYzZo1AyA4OJioqKgSy5RJFSGEsDJGZVPqrbQCAgLYs2cPGRkZ5ObmsnXrVtN8E0DLli3JyMjgxIkTAOzYseMv1xVID0oIIayMsRxuYeTl5cWoUaMICwtDr9fTp08ffH19GTp0KFFRUTRr1ozFixczefJkcnNz8fb2Jjq65DvGW/wyc2sgy8zLnywzL3+yzLxiPIhl5v89fbXUeVs2MN/9NKUHJYQQVsbctzAqLQlQQghhZcx9l/LSkgAlhBBWpiyLH8xJApQQQlgZGeITQghhkSrLA+wlQAkhhJWRHpQQQgiLJIskhBBCWCTpQQkhhLBIBRKghBBCWCIZ4hNCCGGRZIhPCCGERaosd2CVACWEEFamPO5mXh4kQAkhhJWRIT4hhBAWySgBSgghhCUyyhyUEEIISyRDfEIIISySrOITQghhkWQVnxBCCItkNFaOAFU5HqsohBDigTGq0m9lkZCQQPfu3QkKCuKzzz4rdjwuLo5OnToRGhpKaGjoXfPcSXpQQghhZcpjDiolJYWYmBg2bNiAvb09/fv3x8/Pj/r165vyHDt2jAULFtCyZctSlSkBSgghrExZbhar0+nQ6XTF9ru6uuLq6mpK7969G39/f9zc3ADo2rUrmzdvJiIiwpTn2LFjLFmyhD/++IM2bdowbtw4HBwc7nltCVAW4Pugb8xdhYfejXlrzV2Fh94vzfqZuwpWoYf+5H2XUZahu/j4eOLi4ortj4iIIDIy0pROTU3Fw8PDlPb09OTIkSOmdHZ2No0bN2bMmDE89thjjB8/ng8++IBRo0bd89oSoIQQwsqUZYgvPDycnj17Ftt/Z+8JwGg0otHc7pkppYqkXVxcWLZsmSk9ePBgJk6cKAFKCCHEbQVlWMX356G8e/H29ubgwYOmdFpaGp6enqb05cuX2b17N3369AEKA5itbckhSFbxCSGElVGq9FtpBQQEsGfPHjIyMsjNzWXr1q106NDBdNzR0ZF58+Zx6dIllFJ89tlndOnSpcQyJUAJIYSVKY8A5eXlxahRowgLC+PFF18kODgYX19fhg4dytGjR6lRowbTpk3jjTfe4Pnnn0cpxWuvvVZimRqlKstNLx5eWbFjzF2Fh96NLgPNXYWHniySqBgPYpHEp/8p/cf+oPbm+1GvzEEJIYSVkZvFCiGEsEgFRnPXoHQkQAkhhJWpLBM7EqCEEMLKSIASQghhkeSJukIIISyS9KCEEEJYJKMskhBCCGGJJEAJIYSwSDIHJYQQwiKV7QZCcicJIYQQFUQWSQghhLBIMgclhBDCIkkPSgghhEWSe/EJIYSwSKpMy/hkkYQQQogKIsvMhRBCWCSZgxJCCGGRjJWkCyUBSgghrExlWWZuY+4KCCGEqFhGpUq9lUVCQgLdu3cnKCiIzz777J75fvrpJwIDA/+yPOlBCSGElVHl0INKSUkhJiaGDRs2YG9vT//+/fHz86N+/fpF8l29epW5c+eWqkzpQQkhhJVRSpV6K63du3fj7++Pm5sbzs7OdO3alc2bNxfLN3nyZCIiIkpVpvSghBDCypRlDkqn06HT6Yrtd3V1xdXV1ZROTU3Fw8PDlPb09OTIkSNFzlm1ahVNmjShefPmpbq2BCghhLAyZekZxcfHExcXV2x/REQEkZGRprTRaESjuf2jXqVUkfSpU6fYunUrn3zyCcnJyaW6tgQoIYSwMgUFpQ9Q4eHh9OzZs9j+O3tPAN7e3hw8eNCUTktLw9PT05TevHkzaWlp9O7dG71eT2pqKi+//DJr1qy557UlQAkhhJUpy+K8Pw/l3UtAQACxsbFkZGTg5OTE1q1bmT59uul4VFQUUVFRACQlJREWFlZicAIJUOJPtI83wqFtdzRaLQVXr5C3fT3o84vksa3XFHu/IFAKlZdD3o4vULp0sHfE8bm+2FT3BI0GQ+JBbv7yk3kaYsH2HPyFZav+hV6vp97jdRgbORwXZ+ciec6ev8j7y1aSnZ2DjY0No/9vKA3r1wPghUH/xMO9hilv/xdD6PJs+wptw8Oi+Yo5ZB09xdmYFeauSoUqjx/qenl5MWrUKMLCwtDr9fTp0wdfX1+GDh1KVFQUzZo1K3OZ5Rqg3n33XX755Rf0ej0XL17kiSeeACAsLIzevXsXyfvjjz9y/vx5XnvttXuWt2HDBvbv38+cOXMA2LhxI6tXr8ZgMGA0Gunbty9hYWEABAYGsmrVKnx8fMqpdQ8fjaMLjs/1I+eLxajrV7EP6I5DQHfyd351O5PWFsegAWT/awHqejp2Ldrj2DGU3IQVOPh3Rd24Ts73q8HWDpeBb2G4fA5j8gXzNcrCZF7XMXfRh8TNmYZPrUdYEv8ZS1etYdTwf5ry5OXn89bUmYyNGI5/65b8vO8AMxbEsvqDGC4mXca1ahWWL4w2YysqvyqN6vHUoim4PeNL1tFT5q5OhSvbE3VLLyQkhJCQkCL7li1bViyfj48PO3bs+MvyyjVATZkyBbjdndu0adM98x47dqxMZa9du5bPP/+cJUuW4OnpiU6nY/DgwTg5OdG3b9/7qre10tZ5EmPqJdT1qwDoj+7BZcCoogHKpvCXCRp7RxSgsXNAGQwA5P97E2j+d9zFFbS2kJ9boW2wdAf+e5hG9Z/Ap9YjALzwfBf++eZY3hw2xDShfOC/R6jl7YV/65YA/OOZ1jziVTiWf/zESWxsNEROmEJ2dg4dA/wY1LcXWq38YqQsHntjIJdWrCf30mVzV8UsyuN3UOWhwof4zp07xzvvvENmZibOzs5MmjQJZ2dnPv/8cwBq1apFu3btmDhxIllZWaSmptKzZ09GjhxZpJwPP/yQWbNmmSbhXF1dmTt3Ljdu3DDlWbx4MYmJieTm5hIdHU3z5s3Zv38/MTEx5OXlodPpmDBhAp07d2b8+PFkZmZy4cIFxowZg4uLCzNmzECr1dKiRQt+//13Vq9ezYULF5g6dSqZmZk4Ojry9ttv06RJExISEvj444/RarX4+Pgwb948HBwcKu6FfQBsqrphzMo0pdWN62gcnMDO4fYwn/4meT9twLlvBCo3G2xsyPli8e1ClBHHLgOwrd8Mw9ljGDPTKrYRFi71ajoe7jVNaQ/3mmTn5JKTm2sa5ku6fJkabm5Ex37EmXMXqOLizPBXBwJQYDTSqnkzXg97GYOhgAnT5+Ds7ETfF3qYpT2V1fGRhXMj7l3+YeaamEdZ7xBhLhX+tWvMmDG88sorJCQkMGHCBEaOHEmdOnXo378//fv3p3fv3nzzzTcEBwezbt06EhISiI+PJyMjw1RGRkYGV65coUmTJkXKfuKJJ4qsr69fvz4bN27klVdeYfny5QB8+umnzJgxg6+++ooZM2bw/vvvm/K7ubnx/fff0759e8aOHcu8efPYuHEjtra34/i4ceMYM2YMX331FdOnT2fUqFEALFy4kBUrVrBhwwYeffRRzp49Wy6vX7nS3OO5L3d83bKp6Y1Dm85kfzqf7JUzuHlgO07dw4pkz9v2L258PBWNgzP2z3QpzxpXOn9eenuLjc3tP0WDoYB9h/5LcNBzLF0wm17BzzNu2hxu6vUEBz3HyNcH4+ToSNUqLvQNDebnvQcqsgniIVBQYCz1Zk4VGqCys7O5ePEiQUFBALRo0YJq1aoV+zAfMmQIjzzyCMuXL2fmzJno9Xpyc28PFd36Y/6rHkrnzp2BwkB17do1AObNm8fp06dZvHgxK1euJDs725Tf19cXKFyvX7NmTRo1agRAnz59TPU/duwYEyZMIDQ0lNGjR5OTk8O1a9fo1KkTAwYMIDo6mq5du9K4ceO//TqZizErs3Bo7n80VVxReTlg0Jv2aes0pODK+cJFEYD+6G5sanijcXRGW+fJ2+frb6I/9Staj0crtA2WztPDnfSMa6b01fQMqlZxwcnR0bTPvUZ1Hqv9KE0aNgCgnV8bjEYjV5JT2Prjv/n9/O05PaUUWq224hogHgpKlX4zpwoNUHebmFNKUVBQUGTfnDlzWL16NbVq1eKNN96gevXqRc51c3Ojdu3axeat9u/fz/z5803pW3+4d35jffnllzly5AhNmzZl+PDhRc53/N+HhFarxXiXn1objUbs7e3ZtGmTaVu/fj1ubm5MnjyZRYsWUa1aNcaMGVPifJulKrh4Eq13HTTV3AGwa9oWw9njRfIY05LQPloPjVMVoHBFn9JloPJysK3f/HaPyUaLXQNfDElnKrQNlq5NC19+O3mapMtXAPh68zb+8UzrInmeadWSKympnDxT+MXt8PHf0Gg0eHt5cu7iJVasWUdBgZH8/Jt89d0WAtsHVHg7ROWmjKrUmzlVaICqUqUKPj4+bN26FYBff/2Vq1ev0qBBA7RaLYb/Tbbv2rWLIUOG0K1bN86dO0dKSkqxgDFkyBDmzJlDWlrhHEdGRgZz5szhscceu+f1MzMzOX/+PCNHjqRDhw5s3769WHAEqFevHjqdjpMnTwKFd+gFqFq1Ko8//rgp+OzatYuBAwdiMBgICgqievXqDBs2jNDQUBITE+/z1ap4KjebvB/W4dT9FZwHvoW2pjd5Pydg4+mDc//CocyCpN+5+ctOnHoNx3nAKOx8A8j99hMA8n9OQGPviPPLo3Hu/yYFqX+g//VnM7bI8lR3q8a4qDeYMncBYSNGcfbCJf5vcBgnTv/OkDfHAlCzuhszJr7Fwo+W82rkaBYvX8W08aNxsLcnvH8fXKtUYXDUWwweOYamjZ6kR5e/viu0EHcqr7uZP2gVvkhi3rx5TJ06ldjYWOzs7IiNjcXe3p42bdowbtw43N3dGTZsGGPHjsXR0RFvb2+aNm1KUlJSkXIGDBiAwWBg8ODBaDQalFL069evxBV8bm5u9OnThx49emBra4u/vz95eXnk5OQUyWdvb090dDTjxo3DxsaGunXrmnpXt+r/8ccfY2dnR0xMDHZ2dkRFRTF48GAcHByoWbOmaSl8ZVNw4QQ5F04U2WdMTSLn8xhTWn90N/qju4uffDOPvC33vsW+KOTfuqVphd4tf1463vypJnw4f2axcx0dHBgX9Ua519FaHBkywdxVMAtz94xKS6PKa0F8JWY0Gpk/fz4RERE4OzuzcuVKUlJSGD9+fLlcLyt2TLmUK2670WWguavw0PulWT9zV8Eq9NCfvO8y3piXWeq8H45xu+/r/V1yJ4m7sLGxMfW27OzsePTRR5k5s/i3WSGEqIwqSw9KAtQ9vP7667z++uvmroYQQjxwlWXgTAKUEEJYmfK4F195kAAlhBBWRnpQQgghLJLMQQkhhLBI5r6FUWlJgBJCCCsjPSghhBAWSeaghBBCWCRZxSeEEMIiyRCfEEIIiyRDfEIIISxSgaH4UxwsUYU/UVcIIYR5KaVKvZVFQkIC3bt3JygoiM8+K/5kg23bthESEkKPHj0YP348N2/eLLE8CVBCCGFlyuOBhSkpKcTExLBmzRo2btzI2rVrOXPm9gNLc3JymDZtGitXruTbb78lPz+fr776qsQyJUAJIYSVKY8AtXv3bvz9/XFzc8PZ2ZmuXbuyefNm03FnZ2d27NiBu7s7ubm5pKen4+rqWmKZMgclhBBWxqhKfycJnU6HTqcrtt/V1bVIgElNTcXDw8OU9vT05MiRI0XOsbOzY+fOnYwdOxZPT0/atWtX4rWlByWEEFbGaDCWeouPj+e5554rtsXHxxct02hEo9GY0kqpIulbOnbsyL59++jUqRNTp04tsZ7SgxJCCCtTlsUP4eHh9OzZs9j+Pw/PeXt7c/DgQVM6LS0NT09PUzozM5Njx46Zek0hISGMGjWqxGtLD0oIIayM0Wgs9ebq6oqPj0+x7c8BKiAggD179pCRkUFubi5bt26lQ4cOpuNKKcaMGcPly5cB2Lx5M08//XSJ9ZQelBBCWJnyuJOEl5cXo0aNIiwsDL1eT58+ffD19WXo0KFERUXRrFkzpk+fzrBhw9BoNNSvX5933323xDI1qrL8pPghlhU7xtxVeOjd6DLQ3FV46P3SrJ+5q2AVeuhP3n8Z/zxW6rzfftz0vq/3d0kPSgghrIzci08IIYRFKiioHLc6kgAlhBBWRnpQQgghLJIyyiPfhRBCWCDpQQkhhLBIqgy3OjInCVBCCGFl5JHvQgghLJKxkjywUAKUEEJYGRniE0IIYZEqyyIJudWREEIIiyR3MxdCCGGRJEAJIYSwSBKghBBCWCQJUEIIISySBCghhBAWSQKUEEIIiyQBSgghhEWSACWEEMIiSYASQghhkSRAWamsrCxGjBhh7mqYXVJSEg0bNuSdd94psj8xMZGGDRuyYcOGMpe5bt06vvnmGwDGjx9/1zJiY2OJjY0tsZyzZ88yfPhwQkJCCAkJYfTo0WRkZJT6/Mrg3XffJTQ0lO7du9O0aVNCQ0MJDQ3lyy+/LJb3xx9/ZOXKlSWWt2HDBsaPH29Kb9y4kd69exMaGkpISAirVq0yHQsMDCQpKenBNUY8cHIvPit1/fp1EhMTzV0Ni+Dm5sZ//vMfCgoK0Gq1AHz33XfUqFHjb5X3yy+/8Mwzz9xXnVJSUggLC2PatGkEBgailGLJkiVERESwZs2a+yrbkkyZMgUo/KIQFhbGpk2b7pn32LFjZSp77dq1fP755yxZsgRPT090Oh2DBw/GycmJvn373le9RcWQAGWlZsyYQWpqKiNGjKBLly7Ex8djNBp56qmnmDJlCg4ODrRr146uXbty6NAhtFotCxcupHbt2gQGBrJq1Sp8fHzYt28fcXFxrF69mldeeYVq1apx+vRpFi5cSFpaGosWLcJgMODj48P06dOpXr26uZtejIuLC40aNeLAgQP4+/sDsGvXLgICAoDCb+4LFy7EaDRSu3Ztpk2bhru7O4GBgbzwwgv8/PPP5ObmMnfuXHQ6HTt27GDv3r14eHgA8NNPP7FmzRrS09MZPnw4/fr1M117/fr17N27l/feew8o7Bk5ODiQk5ODv78/gYGBAGg0GoYOHYqPjw8GgwGAI0eO0L9/f1JSUujVqxeRkZHcuHGDiRMnkpKSQmpqKm3btmXmzJns37+fefPmYTQaadCgAZMnT2bs2LFcvHiR2rVrk5ycTFxcHI888gjR0dHs37+fgoICevXqxauvvkpycjJvvfUWOTk52NjYMHnyZFq0aFEu78e5c+d45513yMzMxNnZmUmTJuHs7Mznn38OQK1atWjXrh0TJ04kKyuL1NRUevbsyciRI4uU8+GHHzJr1iw8PT0BcHV1Ze7cudy4ccOUZ/HixSQmJpKbm0t0dDTNmzdn//79xMTEkJeXh06nY8KECXTu3Jnx48eTmZnJhQsXGDNmDC4uLsyYMQOtVkuLFi34/fffWb16NRcuXGDq1KlkZmbi6OjI22+/TZMmTUhISODjjz9Gq9Xi4+PDvHnzcHBwKJfX8KGhhFW6dOmS6tSpkzp16pQaMGCAysvLU0opNX/+fLV48WKllFJPPvmk2rZtm1JKqdmzZ6vZs2crpZTq1KmTunTpklJKqb1796pBgwYppZQaNGiQWrRokVJKqfT0dPXCCy+ozMxMpZRS//rXv9TEiRMrroGldOt1+Prrr9XUqVOVUkodPnxYjR8/Xo0bN04tXbpUtWvXztTeZcuWqcjISKVU4euwcuVKpZRSq1atUhEREUoppcaNG6e+/PJL07+HDRumjEajOnnypPLz81NKKbVo0SK1aNEidePGDdW2bVuVlZWllFIqKChIJScnq9dff12tWLHinvVetGiR6tmzp8rPz1fp6emqefPmKisrSyUkJKgPPvhAKaVUfn6+6ty5szp69Kjau3evatWqldLpdEqpwvdz7ty5Simljhw5oho3bqwuXbqk1qxZo2bNmmU6f9CgQerAgQMqNjZWLVu2TCml1M6dO9XHH3/8AF792269D0op1bt3b7VlyxallFL//e9/1bPPPqvy8/NNr5lSSn388cdqw4YNSimldDqdatmypUpPT1dffvmlGjdunEpPT1dPPvmkunbt2j2v2alTJ1M7Vq9ebXpfIyMj1ZkzZ5RSSu3evVsFBwcrpQrfy3HjximllLp586bq0KGDSkxMVEopNX36dNPfQb9+/dTx48eVUkqdPn1aBQUFKaWUCgwMVFevXlVKKTVnzhz122+/3e/L9tCTHpSV27dvHxcuXOCll14CQK/X06RJE9Px9u3bA9CgQQMOHjz4l+X5+voCcPjwYa5cuUJYWBgARqORatWqPejqPzCBgYGmXtL3339Pt27d+O6773BycsLX1xcfHx8A+vXrx9KlS03n3fn6bN269a5lP/fcc2g0Gho0aMC1a9eKHHNxcaFjx45s27aN2rVrU7t2bby8vNBoNNjb25dY5/bt22Nvb0+NGjWoXr06169fJzg4mCNHjvDJJ59w9uxZMjMzycnJAaBu3bpUrVoVKOwhzp8/H4BmzZrx5JNPArBnzx4SExPZu3cvADk5OZw8eZK2bdsSGRlJYmIiHTt2ZNCgQWV6fUsrOzubixcvEhQUBECLFi2oVq0aZ8+eLZJvyJAh7N27l+XLl3P69Gn0ej25ubmm4zY2hdPrf9VD6dy5MwD169dny5YtAMybN48ff/yRzZs3c/jwYbKzs035b/3/PnXqFDVr1qRRo0YA9OnTh5kzZ5Kdnc2xY8eYMGGC6ZycnByuXbtGp06dGDBgAJ07d6Zr1640btz4b71G1kQClJUrKCigW7duTJ48GSj8gCgouP20zVt/4BqNBnXHk1lu/fvWcNMtjo6OpnKffvppPvroIwDy8/OL/KFbmlvDfIcOHWLv3r2MHj2a7777DqOx6IPdlFJF2nzn63Mvt+a17pWnd+/efPjhh/j4+NCrVy8AmjZtWmzOxWg0EhUVxdSpUwGwtb3953vr/Vm9ejVbtmzhpZdeIiAggFOnTpneq1vvza06qbs8aaegoIAxY8aYAkRGRgYuLi44ODjw7bff8tNPP/Hdd9/x1Vdf/eWChb/jbnVSShX5PwkwZ84cLl26RHBwMJ07d2b37t1FznVzc6N27docO3aMNm3amPbv37+ff//737z11lvA3d+bl19+GT8/P/z8/Gjbtq0pL9x+DbVabbH/G1D4Htnb2xeZS0tOTsbNzY3Jkydz4sQJdu7cyZgxY4iIiCA0NLRMr4+1kVV8VsrW1haDwYCfnx/btm0jPT0dpRRTp04lPj6+xHOrV6/OmTNnANi+fftd8zRv3pxff/2Vc+fOAfDBBx8QHR39YBvxgHXr1o333nuPpk2bmj788/LyOHz4sGm119q1a/Hz8yuxHK1WW+wDtSStW7cmOTmZffv2mb7R9+vXj507d7Jz506g8EP6gw8+ID09HXd393uWtWvXLvr168cLL7xAfn4+J06cuOsHadu2bUlISADg5MmTnD59Go1Gg7+/P+vWrUOv15Odnc3LL7/Mr7/+SnR0NF9//TU9e/bknXfe4bfffit1+8qiSpUq+Pj4mHqjv/76K1evXqVBgwZotVrTl4Ndu3YxZMgQunXrxrlz50hJSSnWziFDhjBnzhzS0tKAwmA7Z84cHnvssXtePzMzk/PnzzNy5Eg6dOjA9u3b7/pe1qtXD51Ox8mTJwFMr2XVqlV5/PHHTQFq165dDBw4EIPBQFBQENWrV2fYsGGEhobKIqVSkB6UlapZsya1atVi5syZREREEB4ejtFopHHjxrz++uslnhsVFcX06dOJi4ujXbt2d83j4eHBrFmzePPNNzEajXh5eTFv3rzyaMoD06lTJyZNmlRkst3d3Z1p06YRERGBXq83vWYlCQgIYMGCBabhtNLo0qULmZmZpmE9Dw8Pli1bRnR0NPPnz6egoIAmTZqwePHiEssJDw9n6tSpLF26lCpVqtCyZUuSkpKoU6dOkXwjRoxgwoQJhISEUKdOHdzd3XF0dKR///5cuHCBnj17YjAY6NWrF35+ftSpU4fRo0ezYcMGtFotc+fOLXXbymrevHlMnTqV2NhY7OzsiI2Nxd7enjZt2jBu3Djc3d0ZNmwYY8eOxdHREW9vb5o2bVpsyfiAAQMwGAwMHjzY1MPs169fiSv43Nzc6NOnDz169MDW1hZ/f3/y8vJMw6S32NvbEx0dzbhx47CxsaFu3bqm3tWt+n/88cfY2dkRExODnZ0dUVFRDB48GAcHB2rWrMmcOXMe/Iv3kJEn6gphRkop9Ho9r732GhMnTuSpp56qkOtu2rQJHx8fWrVqxeXLlxk0aBA//PCDae5GlMxoNDJ//nwiIiJwdnZm5cqVpKSkFPkNlrh/0oMSwozS0tLo0aMHffv2rbDgBIVDVFOmTMFoNGJjY8O0adMkOJWBjY2NqbdlZ2fHo48++pc9a1F20oMSQghhkeQrkxBCCIskAUoIIYRFkgAlhBDCIkmAEkIIYZEkQAkhhLBIEqCEEEJYpP8PIh9r7sEd4kwAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 432x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# correlation between continous features\n",
+ "feature_corr = numeric_data.drop(\"SeniorCitizen\", axis=1).corr()\n",
+ "sns.heatmap(feature_corr, annot=True, cmap='coolwarm')"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Die Korrelationsmatrix zeigt, dass die Attribute \"Tenure\" und \"TotalCharges\" eine kritische positive Korrelation von \u00fcber 0,8 aufweisen. Diese Beziehung wird sp\u00e4ter im Zusammenhang mit der Multikollinearit\u00e4t erneut untersucht und muss entfernt werden."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Streudiagramme mit kontinuierlichen Merkmalen und Ziel"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 157,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='tenure', ylabel='MonthlyCharges'>"
+ ]
+ },
+ "execution_count": 157,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.scatterplot(data=data_no_mv, x=\"tenure\", y=\"MonthlyCharges\", hue=\"Churn\")"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Das Streudiagramm deutet darauf hin, dass Kunden im oberen linken Bereich, d. h. Kunden mit hohen monatlichen Kosten und kurzer Betriebszugeh\u00f6rigkeit, am ehesten abwandern."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:xlabel='tenure', ylabel='TotalCharges'>"
+ ]
+ },
+ "execution_count": 158,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.scatterplot(data=data_no_mv, x=\"tenure\", y=\"TotalCharges\", hue=\"Churn\")"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Es besteht eine rein logische, lineare Beziehung zwischen der Dauer der Betriebszugeh\u00f6rigkeit und den in Rechnung gestellten Gesamtkosten. Je l\u00e4nger eine Person Kunde ist, desto mehr monatliche Betr\u00e4ge musste sie bereits zahlen."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 2.4.2. Kategorische Merkmale"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Abwanderung (Ziel)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Zun\u00e4chst wird die Verteilung der Zielvariablen Churn untersucht."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Abwanderung im Vergleich zur Nicht-Abwanderung')"
+ ]
+ },
+ "execution_count": 159,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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6uZr01xIUFMScOXOYNGkSWq2WVq1aodfrS11YLk/+/v707t2bFStWMGnSJNfrjz32GDNmzCAxMRGHw0Hz5s2v2iVAr9czd+5cXnrpJebMmUODBg0ICwvDbDZTVFR0U+uLiYnh2WefdXURCg8P54033uDEiRNl2pc33ngDm83GjBkzXB/WVq1a8frrrzNixAjuvvtuwsPD6dGjx5/uXvHoo4+SnJx81fdmz57NK6+8wueff45Go+H11193tSKvpV+/fowePZq5c+fStGlT1+tff/010dHRdO7c+YrtDxw40PUH12KxkJiYiM1mY+rUqTRs2BCA3r178/HHH9OlSxcAunTpQmpqquu0eOLEiUyYMAFfX1/8/f3p0KEDJ0+evG6tI0aM4OTJkwwaNIigoCDq16/veu/555/n9ddfZ/DgwdhsNrp06VKqq9fvTZ06lWnTphEYGEiXLl1ueIwApkyZwtSpUxk8eDBBQUHUrl27VGvvCte8OifcKi8vT82YMUMVFhYqpZRKSUlRcXFxrovzVdH06dNVVlaWUqrk4nqHDh3UpUuX3FyV8CQLFixQv/76q1JKKYvFou6++27Xja2rqXItOFHC398fg8HAPffcg16vR6/X8/bbb1fYKWp5qFOnDmPGjHFdJ3nttdf+dP88Ia6mcePGvPrqqzidTmw2G/369XNdh7wajVIy4aUQwjNVuZsMQghRXiTghBAeSwJOCOGxJOCEEB5LAk4I4bEk4IQQHksCTgjhsSTghBAeSwJOCOGxJOCEEB5LAk4I4bEk4IQQHksCTgjhsSTghBAeSwJOCOGxJOCEEB5LAk4I4bEk4IQQHksCTgjhsSTghBAeSwJOCOGxJOCEEB5LAk4I4bEk4IQQHkuebC8qnVIKi9WBw6nQaMCg16LVaikqtpFXaCO3wEKx1YHDoXA4FQ6nE4fDicOp0Ou0BPga8TXr8TUb8DHpMRl1GA1abDYndqdCp9VgMujQajXu3lXhZhJwokJZbA7sdicmo468Qiunz+Vx9Ewup87lcfZ8ATl5xeTkWSgost3SdrRaDeFBPkSE+BIR4ktkmB9RtQKpW9OfmsG+2OwOtBoNZpP8ynsTjVJKubsI4TmKLHY0GnA4FKlpOew8eI7UtBzSMnKxWB1uqUmv0xBVK5Am9YJo2SiUZvVDCA/ywWJz4GPSS0vPg0nAiVvicDix2BwoBb8ezOSX1Ez2H7vA2QsF7i7tukxGHa2jw+jYshadWtTCz8eAQmE2SgvPk0jAiZtmtTtwOhUFRXZ+3nWaTbvTOXQyh+r8mxQR4ku7mJp0u70OTesH43QoOZ31ABJwokycToXFaqfI6mDVluNs3nOWU+fy3F1WhQj0MxLfpjb9uzQkMswPjQaMep27yxJ/ggScuK5iqx2tRsOvqZl8s/EYKcfOV+uW2s2qFepLwh316Ne5ASajDl+zwd0liZsgASeuqqjYTkGxjeUbjrJu50nyCm/tLmd1p9FAu5iaDO/VlOg6Qeh1GnQ66UZa1UnAiVKKLHYuXCris/8cYNu+s17VWiurehEB3NurKZ1bR8rpaxUnASdcHW/PZOXz2Xf7+S01y90lVQvBASZG9Inhzg5R6LQa9NKiq3Ik4LxckcXOmax8Ply+l/3Hs91dTrVUM9iHsYNa0qFFLQx6DVqtBF1VIQHnpYotdoqtDv6dtJste866uxyPUL9WAA8PbU1M/WDpT1dFXPdPzbZt2xg9erTr6/z8fIYPH8706dOvu9KHH36Yc+fOlUuBTzzxBIMHDy71WlJSEpMnTy6X9d+MPx6P6shmd1BstbN03WEefO0HCbdylJaRx9T3tvDyh1s5l11IscXu7pK8Xpnb0gUFBYwbN46OHTveMFw+/PBDIiIibrm47Oxs9u/fj5+fH7/++ustr8/bFVvtbN+Xwfg3fmTxj4ew2Z3uLskj7Tt2gUem/0jST0ewWO04nHKc3aVMAVdYWMj48ePp3LkzzzzzjOv1jRs3cs899zB06FAef/xxcnJyAEhISOD06dMkJSUxceJEHnzwQXr37s3LL7/sWvaDDz4gMTGRv/zlL8ycOZOrnSmvXLmSDh060KdPHxYtWlTqvbS0NEaNGsWgQYOYNWsWSikeeeQRNmzYAMCcOXMYN24cAJmZmQwaNAiAt956i+HDh9O3b19Gjx7N+fPnAYiPj+fVV19l6NCh3H333Zw6dQqATZs2MXDgQO666y6++uqrUtsfO3YsiYmJjBw5kv379wMwefJkHnnkEfr378+6detcxwJKtwBHjx7NzJkzuffee+ndu7er7oyMDP76178yePBg/vGPf9CtW7ey/Iiuy2pzkFdoZeb8nUyfv5OcPMstr1Ncn92h+HJNKk/MXs+RUxcpktacW9ww4IqKivjb3/7GoUOHGDNmjOv17OxsZs+ezccff8zy5cuJj49n1qxZVyz/22+/8e677/LNN9/w008/kZqaysaNG0lJSWHp0qUsX76cc+fO8c0331yxbFJSEv3796d///6sXr2aixcvut47ffo0c+fOJSkpiV9++YW1a9fSvXt3tm7dCsDOnTs5duwYDoeDn3/+me7du5OWlsaxY8dYtGgRq1evJjIy0rXdrKwsYmNjWb58OR06dGDhwoVYrVYmT57Mu+++S1JSEmaz2bX9SZMm8eyzz/L111/z6quvMnHiRNd7QUFBrFq1ioSEhOseW5vNxuLFi5kyZQrvvPMOAK+//jr9+/dn5cqV9OvX75ZP9YstdnbsP8f4N35kx4HyuWwgyu7s+QKeefdn3v96D0UWOw6HtOYq0w2vhO7du5e///3vNGrUiKlTpzJv3jwAdu/ezdmzZ7n//vsBcDqd1KhR44rlb7/9dvz9/QGoV68ely5dIjk5mT179nDXXXcBUFxcTO3atUstd+DAATIyMujSpQsGg4HmzZuzfPlyV8gmJCQQEhICQP/+/dm+fTtjx47l0UcfJT8/H4CYmBj27dvHxo0bGT16NPXr12fSpEksWbKE48ePs2vXLqKiolzb7Nq1KwBNmjRh586dpKamUrNmTaKjowFITEzknXfeoaCggJSUFKZMmeJatrCw0NWCve222250WK/Y3uXw3rx5M//zP/8DQO/evQkMDCzTuv7IZi8ZBP/Ool/ZmpLxp9Yhys/aHadIOXqBFx7qRESIr9yEqCQ3PMq33347jz32GEVFRQwdOpRFixYxYsQIHA4H7dq147333gPAYrFQUHDlDBImk8n1b41Gg1IKh8PBAw88wNixYwHIzc1FpyvdWXLZsmVYrVb69u0LlFwDXLRokSvg9Pr/lu50OtHr9URGRuJ0Ovnhhx9o164dYWFhbN26lX379nH77beTkpLCP/7xD8aMGUPfvn3RarWlTo0v13q5zsv/v+xyjU6nE6PRyIoVK1zvZWRkEBQUBFCqpQe41mG3lz5N+f32fr+NW72xXWy1k3Y2l1f/dxuX8q23tC5Rfs5lF/L32esZO6glfWPrS8hVghueohoMJWPvfHx8mDlzJjNnzuTIkSO0adOGXbt2cfz4cQD+9a9/MXPmzDJttHPnzqxYsYKCggLsdjsTJkxg9erVrvetVisrV67k008/Zd26daxbt461a9eSlZXFtm3bANiwYQO5ublYLBa+++47unTpAkC3bt3497//TceOHencuTOff/45bdq0QafTsWPHDjp27MjIkSNp0KAB69evx+G49hxlMTExnD9/noMHDwLwn//8B4CAgAAaNGjgCrjNmzczatSoq64jODiYI0eOALB27dobHpvY2FhWrlxZah9vhsVq5/vkEzw3b5OEWxXkcCo++iaFNz7dTn6RDZvdPXPkeYub+hPSpk0bxowZw8SJE1m6dClvvPEGTz31FE6nk4iICN58880yrSchIYGDBw8yfPhwHA4HXbt2JTEx0fX+unXrqFOnDm3atHG95u/vz7Bhw1i0aBFdu3alUaNGjB8/ntzcXAYNGkR8fDwAPXr04JNPPqF9+/b4+vpis9no2bMnAAMGDODxxx93dTtp1aqV6wbA1RgMBubMmcOzzz6LXq+nRYsWrvfefPNNXn75ZT766CMMBgNvvfVWqZbYZU8++SSvvvoq8+bNc9V4Pc8//zyTJk3iq6++olmzZmU+RXU6FRabg7e+/JXkvdL1o6r7LTWLx2asZdrfuhAZ6ofJKMO9KoJ09K1i5s+fT5cuXWjcuDH79u3jhRdeICkp6brLWG0OLuZZeOnDZE5n5ldSpaI8GPVaJt3fgdsah8n8cxVAAq6K2bBhA7Nnz0ar1WIymXjxxRdp2bLlNb/fYrVzPD2Xlz5MprBYuiJURxoNjBnYggFxDeW6XDmTgKvGiq12fj2YyZsLdmJ3yI+xuuvVMYpHEm+T09VyJAFXTRVb7KzdcZL3l++VKY08yG1NwnjhwU7SkisnEnDVULHVTtJPR/jyh1R3lyIqQKtGobz0cGcJuXIgAVfNFFvtLF6TytJ1R9xdiqhALRqG8PLDsfjIjYdbIhNXVSPFFjsrNh6VcPMC+49n88L7W2QM6y2SgKsmii12ftiWxoJVB91diqgkqWk5PP/vzRJyt0ACrhoottrZ+NtpPlyR4u5SRCU7fOoib3y6HYtVQu7PkICr4oqtdnYeOMe8pbvdXYpwk12Hsvh30h6KJeRumgRcFWa3OzmTmc/shb9IVxAvt3bHKb5ef1RmCb5JEnBVlFKK/CIbL32YLJ14BQBfrD5IcspZacndBAm4Kspic/DC+1tkRhBRyjuLfuPE2VzsMt18mUjAVUEWq53ZC3/lxNmbmypJeD6HU/H6/26nSFpxZSIBV8WU9HU7xtYUmfJIXN3FfAtvfLIdi1XmkrsRCbgqxOFwkn6+gIWrpa+buL6UYxdYsvaQ3HS4AQm4KsRqd/L6J9twOuWmgrixr9Ye4vCpizIr8HVIwFURxRY7/1q6m8ycIneXIqoJpeB/PpNT1euRgKsCrDYHv6Zmsv7Xa0+fLsTV5BXaeGvRb9J15Bok4KqAYquddxb/5u4yRDW1fV8Gvxw4h9UmLbk/koBzs2KLnf+3ZLdMNy5uyf9buhub9I27ggScGzkcTo6cvsgWeQqWuEV5hTb+nbRb7qr+gQScG9kdincX73J3GcJDbPj1DMfTL+F0SkvuMgk4Nym22lm+8ShnLxS4uxThQf7f0t3YZOyyi8yH7CZFxXa+WlP5z1TIPf0LOcc2ur522IqxF1+iYc9JZO3/FmtBJihFYN32hDQueWD2xbSt5Bxdj9bgQ+32ozH4hgBwetvHhLcYhCkgotL3Q1xdWkYeyXvSiWtTG4Nens4lAecGRRY7H3+TgtUNF4UD67YnsG57AJTTwakt/yakcU9yjm1E71OD2neMxmm3cmLDbHxCG+ETXJ/sIz/RoMcz5Gfs4+KJLYS3GERe+h5MARESblXQJ9/uJ7Z1pLvLqBIk4Nwgt8DKz7vOuLsMso+uR2fyJ6h+Z5RSoEoC127JRTntaPVmADRaHcphw2kvRqPV4XRYyTm2gbqdH3Zn+eIasnOL+ebnY/ylayNMXv5kLrkGV8kut97cPRrLYS0g59hGwlsMBkCj0aDR6jj725ekbZiDb2gjjP7hAIQ168+p5PfIz0ghqGE82YfXEdSgiysARdWzZO1hmUcQeWxgpTuTlc8j09e6uwwuHF6HrSCLWm3vveI9p91C+i+fYw6KIiymT6n3rAUXOLdnGXU7jyu5ZpefhV94E4Ibdaus0kUZJfaI5r4+zTB78aMHpQVXiYosdj6qIg+OyUvfTWC9O1xfF2SmYi++BIBWbyKgdlssl648jc7av5LwFgMpPH8Ep91CnY4PUpCZirXgfKXVLsrm++Q0vL31IgFXiS5cKmbngXPuLgOHtRBb4Xl8ghu4Xss7u4cLh35EKYXTYScvfTe+YdGllss/tx+9uQbmGnVQTjsajRaNRgOActgqcxdEGRRZ7Hy35bhXD+GSgKskRRY7X/14yN1lAGArvIDeFIhG+99uBOEtBuG0F5O2cQ4nN72DOaguQQ3jXe87HXayD68lLKYvAL5hTbEV5XB83QwMviGYAuWuXVW0fMNRr27FyTW4SlJYbGPUi99jd0gvc1G5Jo68nW6310Wv8772jPftsRtYbQ6+23JCwk24xeI1h7x2ElUJuEry7aZj7i5BeKn08wWkZXjnA4wk4CqY06nYfTiLC5eK3V2K8GIrfz5OYbH33QiSgKtgxVY7K6X1Jtxsy950dFrv+7h73x67we7D0kdMuJfF6mBrylmvm0pJAq4COZxONu9O99oLvKJq+W7LcYq97AE1EnAVyGJ1sHbnKXeXIQQA+49nY/GyTr8ScBXI6VTsP37B3WUI4ZK8Jx2HF52mSsBVEIfTyYbfTiPdqEVVsmVvBsUW72nFScBVkGKLg0270t1dhhCl7Dt2Hr3eez723rOnlcyg13IwLdvdZQhRit2hSDnqPXf1JeAqSGpatkw4KKqkn3edochLHi8oAVcBLFY7yXsz3F2GEFf1y4FM9DqNu8uoFBJwFcDphD1HstxdhhBXdTHfQn6hdwzbkoCrCBo4eS7P3VUIcU0H03LcXUKlkICrAMfOXJLuIaJK2304C4vV86/DScCVM+ncK6qDg2nZOLxgCKEEXDkrstg5cvqiu8sQ4rpOpOdi8IL+cJ6/h5VMq9Vw/Ix3Ti4oqg+HU5GeVeDuMiqcBFw502k1ZGR7/i+OqP5OnPX8P8QScOUs/XyB3GAQ1cLx9EvY7Z498F4CrpydSL/k7hKEKJP08wVY7J498F4Crhw5nIozWfnuLkOIMjmTlY+nj2eQgCtHVpuDrItF7i5DiDLJOF+AyaC78TdWYxJw5cjhVGTlSMCJ6sFqd1JY7NmdfSXgypFOq5EWnKhWcgut7i6hQknAlSOjQcsFCThRjeRLwImystudWD38trvwLJfyJeBEGUm4iermYp7F3SVUKAm4cmT1skeyieovO7cI5cE90yXgypG3PXNSVH+5hTZsHnzmIQFXjixe9tRwUf1ZrA6c0oITZVEsASeqGadSHj12Wu/uAjyJXIOrGIF+RsYPbY1W6+kDiypfRLAvGo3nJpwEXDnSecmTiirbAwNb0KWhnqK0FHeX4nEMmtoYVEPA4O5SKoQEXDky6j17XJ+7hAf5kH9gMzk/LXB3KR4noM2dhPZp5O4yKoxcgytH3jAFtDuEBehx5MlzLiqEVgsePKeIfCLLkbTgKkagjxZHnnc85q6yaXR60EjAiTKQFlzFMBt02PMl4CqCRqtHIwEnykIvAVch9AY9jvxsd5fhkXQBIWj0nnmDASTgypWfWe7ZVAStwYgj/6K7y/BIhuBa7i6hQknAlSO9TuvxM6RWttrhfii7DeWwubsUj6SvEebuEiqUBFw5stqdBAea3F2GR2lQKxBHUZ67y/BYOv9gd5dQoSTgypHD6SQk0OzuMjxKnZr+cnpagXRmf3eXUKEk4MqRVqMhpIYEXHmqFeqLPfe8u8vwSBqTr0d3EQEJuHJl0GsJDfRxdxkeJSzIF/vFTHeX4ZH0ASEou8zoK8rIoNdRv1aAu8vwKGEBOmnBVRB9QIhHT3YJEnDlrkm9IHeX4FFqmHU4pJNvhdAHhqHRenYEePbeuUFkmGdftK1sJqMWu3TyrRCmOjFojZ59SUUCrgKEBcmNhvJiMBjkLmoFMUc1d3cJFU4CrpzZHU7q1wp0dxkeo2QUg5yiljudHkNQhLurqHAScOXMZNDRIFICrjxEhvqiHHaPv9PnDqaIBji94LhKwJUzvV7LbY3D3V2GR4iKDMRRKKMYKoKpdhM0Ws8fVigBVwGaNfDs4S+VpV5NfxwFF91dhkfyqd8KrcHzhxVKwFUAjUZDVIT0h7tVtUL9pA9cBTHVaeruEiqFBFwF0GqgdWPPnqWhMoQF+cgohgqgNfuh8/WO68QScBXAZNTTsYVnz7NVGcIC9NKCqwCmujE4bRZ3l1EpJOAqSPOGIZ4+jrnC1fCRUQwVwb9lV7Qmz+7ge5kEXAVqVKeGu0uo1sxGLfY8GcVQrjRa/Jp0QKPxjo++d+ylGxh0WuJuq+3uMqq1klEM0oIrT+aoFig8e4D970nAVRC9Xkv3dnXdXUa1JqMYyp9/q65oDd4zlFACrgIF+hmpW1MG3/8ZESEyiqH8afBrFuvxM4j8nvfsqRvotBppxf1JJc9iyHd3GR7FVKep11x7u8y79raSGfQ6enWIcncZ1VKdCHkWQ3nzbxmPxmB0dxmVSgKugvn5GGgaJUO3blatUD/seRfcXYZH8W8R5xXjT39PAq6CmQxahnaPdncZ1U7NIB/sF8+5uwyPYY5qiUbvXa03kICrcFqtlk4ta+HvY3B3KdVKWIAeh4xiKDdBXRLRGD1/cP0fScBVAqdS9Ook1+JuRqCPdPItL7qA0JIWnJfdYAAJuEphNupJ7N5Yhm7dBB+TTm4ylJMad/QHL/3d07u7AG9hNupo2ySc3w5lubuUakGv11f6w2bWHbvI0n3n0QAmvYZHOtTmq5Qszub9ty9eRr6V1hF+vJxQn+8OZbMkJYsAk45/douiVkDJNa4X1p7g4fa1iKoCz+bQ6I0Etu+L1guvv4EEXKXxMekZ1a+ZBFwZ6QymSh3FcPqShY9+yWDewGhCfA1sP53Ha+tPMv+eGNf3pJ4v5I0Np5jQKRKAr1Ky+GBIE7aczGVl6gUeviOSn09cIqqGqUqEG0BA2zs9/un11yOnqJVEo9FQv1YgLRqGuLuUKi88yIxyOlCVOKWPQafhqdg6hPiW3AxqGupDTrEdm8MJgM3hZPbmM4zvEEm4X0lrSK/RYLE7KbQ5MWg1FNudLNt/nlFtalZa3del0RIcP8zjHw14PRJwlchk1DF2UEt3l1HlNahdA0dR5T6LIcLfSMe6JbMwK6X4YOdZOtUNwKAr+YisPpJDqI+euKj/ThQ5pl0Ez60+zua0XIY0D2PR3iwGx4Tga6gafc38WnTxyq4hvyenqJVIo9HQILKkFbf/uNwhvJY6bnwWQ7HNyewtp8kqsPFarwau15fvv8CTsaVnh4mvX4P4+iVTYqXnWTiYVcj9bWvy3o6znMm1cHukP3e1cNPMzhotIT1Hec28b9ciLbhKJq24G6sd6ofDDaMYMvOtPP39MbQaDTP6NMTfWNISO3KhCIdStI7wu+ayH+7IYFz7Wvx2Np8im4NpCfXZeSaP9Fz3zJwb2L4vOh/vmJb8eiTgKplGo6FB7UBaNQp1dylVVlhw5T+LodDmYNIPx4mLCmRKt3qY9P/9aOw9V0CbWv5ornGxftvpXEJ9DTQO9cHmUOg0Gtf3WhyVP/ea1uxPSI9RaI1V40aHO0nAuYHZqOfJe29Hq/Xeu1vXEx5gwH6pckcxrDyYTWaBjS0nc5mw8ojrv9xiO+l5ViL8rz4Sxepw8uWeLB64veTGQrva/mQW2Hjw60PU8jfSMLjyQyak5yjwsjGn16JRSnnP9J5VSJHFzvzv9vPtpuPuLqXKmf/P7lg3fkLB/k3uLqXaMYTVpc6DM73imadlIS04N/Ex6bl/QAsC/bz7LtfV+Bi1OCq5k6+nCBvwCBqdjHu+TALOjXQ6DQ/9RW44/JHeoJepyv8En8btMEU09KoZe29EjoQbGfU64m6rTZN6Qe4upUrRGYzYJeBujlZPeP9H5MbCH0jAuZnRoGPS/R0w6OVHARBaw4xSCmUtdncp1Upw93vRmq/djcVbyafKzTQaDTX8jTw4WE5VARrWDsRZyaMYqjtzVAtqdBgorberkICrAsxGPb07RnFbYzf1eq9C6tYMwF5wyd1lVBtasz8Rdz8rd02vQQKuijAZ9Tw3+g78zN49eq5WqJ/M5HsTag59yqsH09+IBFwV4mMq6QDszcKDfbBfqtxRDNVVQLs+mOs1R6OXbiHXIgFXhRgNOtrF1KRfbAN3l+I2JaMYZM68GzGE1SW01wNy3e0GJOCqGLNJz7i/tKJZA+981GANX510EbkBjc5AreFT0Oikk/iNSMBVQSajjpce6kxIoPf9dfYxanHkScBdT9jAR9H5B0uH3jKQI1RFmU16pv0t1uv6xxlkFMN1BXcbgV9MJ7lrWkbe9empRvQ6LbVCfHlqhHfddCgZxSDjUK8moG0vanQaLNfdboIEXBVmMurp2KIWYwe1cHcplSIk0AwyiuGqfJvcQWifByXcbpIEXBVnNukZENeQexIau7uUCtcgMhBHUb67y6hyTLWbUHPoRDkt/RMk4KoBs1HPvb1j6Nu5vrtLqVD1agZgd9OzGKoqQ2gdIu97UVpuf5IEXDVhNuoZN6QV8W1q3/ibq6laYb5ueRZDVaXzDyZy9DQ0Bgm3P0sCrhoxG/U8NeJ2Oreq5e5SKkRNGcXgogsIofYDb6A1+0t3kFsgR66aMRn1PDOqPb061HN3KeUuzA3PYqiK9EER1HlwJrqAELQ67x6bfKsk4Kohk1HP3+66jaHdo91dSrkK8tXhyPPuLiKG8HrUeXAGOt8aEm7lQAKumjIb9Yzq24wHBjZ3dynlxsfk3cO0THWbUeeBN9Ca/eS0tJzIn4hqzGzSMyiuETX8TMxbuhuns3o/IM2bRzH4Ne9C+ODHpStIOZM/E9Wc2aSn6+11mD4hDn+f6j1tjrc+iyEoNlHCrYJIwHkAs1FP4zpBzHu2J1ERAe4u508JDjABCmUpdHcplUZjMFMz8WmC4u+5qXA7dOgQMTExrF692vVaQkICp0+frogyr2v06NFs27at0rdbVhJwHsJg0BEcYGb2U93o2rb69ZWrHxmIo6jA3WVUGmNEQ+o98i6+TTrcdCfeZcuW0a9fPxYvXlxB1XkOuQbnQbRaDWZjyazAraPD+HBFCja7091llUm9CO8ZxRDYcTAhPUai0RvRaDQ3tazNZmPlypUsXLiQESNGcPLkSaKiogCYN28eBw8exGQy8corr2C323nllVdYsmQJhYWFdOzYkYULF9KmTRtefPFFYmNjiY6O5tVXX6WwsJDs7GzGjx/PyJEjmTt3LufOnSMtLY0zZ84wbNgwHn30UaxWK88//zwpKSnUqVOHnJz/XlL44IMPWLVqFQ6Hg/j4eJ599lnOnDnDuHHjCA4Oxmw2M3jwYLZv38706dOBkhbg448/DsD777+P2Wzm6NGjxMTEMGvWLIxGI/Pnz2fBggUEBATQqFEjoqKieOKJJ8p0vKQF54HMRj0976jHvyfdSXSdGu4up0wiw/w8vouI1ieAyPteJqT7CLQG002HG8CGDRuoXbs2DRs2pFevXqVacfXr12f58uU89thjTJ48mZYtW5KZmUleXh47d+4kMDCQ7du3A7B161a6du3KkiVLeOyxx1i2bBnz589n5syZrvWlpqby8ccfs2TJEj744ANyc3P5/PPPAVi1ahVTp07l5MmTAGzcuJGUlBSWLl3K8uXLOXfuHN988w0Ax48f58033+STTz657r799ttvvPjii6xatYr09HQ2bdrEwYMHWbhwIUlJSXzxxRekpaXd1PGSgPNQZqOemsE+zHi8KyP7xKDV3vyHqTLVDPLBfvGcu8uoMOaoltR7dC7mes1uaVzpsmXLGDRoEAADBgwgKSkJq9UKwLBhwwDo3r076enp5OXl0aVLF7Zt28bWrVt54IEH2LFjB0eOHCEyMhJ/f38mT56MxWLh/fff5+2336aw8L/XQDt16oTRaCQ0NJSgoCDy8vLYvn07/fv3B6BBgwbcfnvJdF7Jycns2bOHu+66i8TERFJSUjhy5AgAoaGh1K1b94b71qRJE2rVqoVWqyU6OppLly6RnJxMz5498ff3x2QyMXDgwJs6XnKK6sE0Gg0mo467ejSmy221+Z9Pt5N+vmpe5woLNGI/53nPYtAYTAR3G0Fg+763fJf0woUL/Pzzz+zbt4/58+ejlCI3N5c1a9YAoNPpXN+rlEKv19OjRw+Sk5NJSUnho48+YvHixfz000/07NkTgKeeeorAwEB69uzJgAED+Pbbb13rMJn+W69Go0Ep5fr/ZXp9SYQ4HA4eeOABxo4dC0Bubi46nY6cnBzMZvMV67nMZrNdd3tarRan889fZpEWnBcwm/RERfjz7j968Nf+zTFWwVmCg3y1OPIvuruMcuXXIp6ox98jsH2fcukCsmLFCjp37szGjRtZt24dP/30E4888giLFi0CYOXKlQCsWbOG6OhofH19iYuLY9OmTWi1WgICAmjevDnz58+nR48eAGzevJknn3ySXr16sXHjRqAkrK4lNjaWlStX4nQ6OXPmDL/++isAnTt3ZsWKFRQUFGC325kwYUKpu7yXBQcHc/ToUZRSnDp1itTU1Ovuc2xsLBs2bCA/Px+r1coPP/xwU6f20oLzElqtFpNRy5BujejXuT7/WrqbLXvPurssF1+jjhwPmcnXGNGQ8EGPYQiJLNdnln799ddMnDix1GujRo3io48+wt/fnxMnTjBkyBD8/PxcF/H9/f2pVasWrVu3BkqC6MiRIzRo0ACAJ554gvvuuw+TyUSzZs2oU6fOdbub3HfffRw+fJj+/ftTp04dmjZtCpR0Uzl48CDDhw/H4XDQtWtXEhMTOXPmTKnlu3Tp4roL3LBhQ9q3b3/dfW7atCn3338/9957L76+vgQHB5dq6d2IRv2+vSi8RpHFTlpGLnMX7+LkuTx3l8PXr/Xi7Px/Yjtf+X25yovWN5DQOx/Ar3ksGr0BjabqtZSrm+PHj7NhwwbGjBkDwKOPPsqwYcNISEgo0/LSgvNSPiY9TesFMWdidzbvPsMXq1M5l+2+TrZag7H63kXV6gi8oz8h3UeAVo9WHsRcburUqcPevXsZNGgQGo2G+Ph41/XDspAWnMDucOJ0KpL3nmXB9wfIuFC5QRfkb2T+i705Pv3eSt3urdIYzQS07UVQl0S0BrPMulsFSQtOoNdpQQdxbWrTuXUk2/dl8PmqA5ytpDuu9avZsxh0ASHU6DSYwNv7AEiwVWEScMJFr9Oi10Fs60g6tazF4VM5JK0/ys79GVTkRCX1agbgKLxUcRsoJ8aa9QmKH4Zv4/ag0cipaDUgASeucLlF17JRGA1r18DuUHy76RjfJ58gJ89S7tuLDPfDnltFn8Wg1eHTqA3B8cMw1qyPRqdHo9XdeDlRJUjAievyNZe0Uu5OaMI9CU3Yf/wCP+44xY79GRQW28tlGzWDfLBfOlIu6yofGkx1Ywhok4B/8y6AQmvydXdR4k+QgBNlYjKUtFraNq1J06hgDPe25eCJHNbuPMm2lAzyi2w3WMO1hdUwYT/i5ofNaHWY6zXHv3kX/Jp3KWmpGYzSWqvmJODETbvcqmvdOIzGdWsw4Z42pJ3NY/v+DPYcOU9qWg52R9mH1wT7aN0yk68+OBJznab4xnTEt1FblNOJ1miSUPMgEnDilvj8X9g1rhdE/chAhnSLxmjQkXY2lx0HMkg5doG0s7lcyrdecx2+Jh0X8yo24LRmP0y1m2CqG4NPg9aYIhoCJWM2dabyG20gqhYJOFFuDHothv8b59q4XhANIgP5S1cHBr0Wu8PJmawCDp/K4ejpi5w8l0dWThE5ucUYDOXzsBmN3oguIAR9QCj6wDD0gSEYa0VjrtsUnW8NnDZLyTRF8rQqryEdfUWlK7LYcToVep0GvV6Hxm7BUXgJR/5FHAUXseddwFlUABpNyX9oQKtFw/99/X//1pp90QdFoA8IRedXA43egLJbUU4HGo22ZEJJCTOvJgEnhPBYMhpYCOGxJOCEEB5LAk4I4bEk4IQQHksCTgjhsSTghBAeSwJOCOGxJOCEEB5LAk4I4bEk4IQQHksCTgjhsSTghBAeSwJOCOGxJOCEEB5LAk4I4bEk4IQQHksCTgjhsSTghBAeSwJOCOGxJOCEEB5LAk4I4bEk4IQQHksCTgjhsSTghBAeSwJOCOGxJOCEEB5LAk4I4bEk4IQQHuv/A+D7R+js7OS1AAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# produce pie chart for churn\n",
+ "# generate procentage relationship\n",
+ "churn_rate = data_no_mv.Churn.value_counts() / len(data_no_mv.Churn)\n",
+ "\n",
+ "# Plot\n",
+ "labels = 'Keine Abwanderung', 'Abwanderung'\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.pie(churn_rate, labels=labels, autopct='%.f%%') \n",
+ "ax.set_title('Abwanderung im Vergleich zur Nicht-Abwanderung')"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "- Die Abwanderungen machen etwa 27 % des gesamten Datensatzes aus, w\u00e4hrend die Nicht-Abwanderungen etwa 73 % ausmachen.\n",
+ "- Dies ist ein unausgewogener Datensatz und eine andere Metrik muss in der Bewertungsphase verwendet werden.\n"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Geschlecht"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"gender\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Die Abwanderungsrate zwischen M\u00e4nnern und Frauen ist ungef\u00e4hr gleich hoch."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Senioren"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 161,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"SeniorCitizen\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Bei Kunden, die als Senioren eingestuft werden, ist die Wahrscheinlichkeit h\u00f6her, dass sie abwandern."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Partner"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 162,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"Partner\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Kunden, die keinen Partner haben, sind eher bereit, abzuwandern."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Angeh\u00f6rige"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 163,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"Dependents\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Kunden, die Verwandte haben, sind eher bereit, abzuwandern."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Mehrere Anschl\u00fcsse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 164,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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rIiImuC00Nm3axMKFCwkICACguLiY7Oxs4uLiCA0NZe3atTidTrKysigpKaF79+4AREREkJaWRllZGfv372fIkCHl2kVExHOs7trwsmXLyk3n5eXRp08fFi5ciL+/PzNmzGDz5s107NgRm83mWs5ms5GTk0N+fj5+fn5YrdZy7SIi4jluC42fu/HGG1m3bp1rOioqiuTkZDp06IDFYnG1G4aBxWJx/Xu5n0+b0bKl368vWsTNbDZ/T5cgv1J93XemQiMnJ4dWrVqVa/v++++5+eabTb/Qt99+S2Zmpqu7yTAMrFYrrVu3Jjc317VcXl4eAQEBtGjRgoKCAhwOB97e3uTm5rq6uirj1KlCnE6j0utJ7VJbf4Fzcws8XUKNUBv3X13dd15elqv+sX3VaxpnzpzhzJkz3H///Zw9e9Y1nZeXx6xZsypViGEYLF++nLNnz1JWVsaf//xnQkJCCAwMxNfXl/T0dABSUlIICgqiQYMG9OrVi9TUVACSk5MJCgqq1GuKiEjVuuqZxhNPPMHu3bsB6N27979WslpdZwxmde7cmQceeICJEydit9sZPHgwI0eOBCAxMZH4+HgKCwvp2rUr0dHRACxcuJDY2FhefPFF2rRpw+rVqyv1miIiUrUshmFcs+9m3rx5rFixojrqqXLqnqofbDZ/Iudu9HQZlfLWqkl1toujsmrb/qvL++5a3VOmrmmsWLGCrKwszp49y+UZ07Vr199eoYiI1BqmQmPt2rW8+uqrtGzZ0tVmsVj4+OOP3VaYiIjUPKZCIzk5me3bt1f4BpWIiNQvpu4Ib9OmjQJDRETMnWn07duXVatWMWjQIBo2bOhq1zUNEZH6xVRovPfeewDlxn7SNQ0RkfrHVGjs3LnT3XWIiEgtYCo0XnvttSu2T5s2rUqLERGRms1UaPzjH/9w/XzhwgX2799P37593VaUiIjUTKZv7rtcTk4O8+fPd0tBIiJSc/2qhzC1atWKrKysqq5FRERquEpf0zAMg4yMjHJ3h4uISP1Q6WsacPFmv7lz57qlIBERqbkqdU0jKysLu91O27Zt3VqUiIjUTKZC4+jRozz00EOcPHkSp9NJ8+bNeemll+jQoYO76xMRkRrE1IXwxYsXM336dPbv3096ejozZ85k0aJF7q5NRERqGFOhcerUKUaPHu2aHjNmDPn5+W4rSkREaiZToeFwODhz5oxr+vTp0+6qR0REajBT1zQmT57M+PHjGTZsGBaLhdTUVKZMmeLu2kREpIYxdaYRHBwMQFlZGT/88AM5OTmEhIS4tTAREal5TJ1pxMbGMmnSJKKjoyktLeXtt98mLi6OV155xd31iYhIDWLqTCM/P5/o6GgAfH19mTp1Krm5uW4tTEREah7TF8JzcnJc03l5eRiG4baiRESkZjLVPTV16lRGjRpF//79sVgs7NmzR8OIiIjUQ6ZCY+zYsXTr1o29e/fi7e3NfffdR6dOndxdm4iI1DCmQgOgc+fOdO7c2Z21iIhIDfernqchIiL1k0JDRERMU2iIiIhpCg0RETFNoSEiIqYpNERExDS3hkZhYSEjR47k+PHjAOzZs4fQ0FAGDx7MmjVrXMsdPnyYiIgIhgwZwvz587Hb7QBkZ2czadIkhg4dysyZMykqKnJnuSIicg1uC40DBw4wceJEMjMzASgpKSEuLo6kpCRSU1PJyMjg008/BSAmJoYFCxbw0UcfYRgGmzZtAmDRokVERkaSlpZGt27dSEpKcle5IiJigttCY9OmTSxcuJCAgAAADh48SNu2bbnxxhuxWq2EhoaSlpZGVlYWJSUldO/eHYCIiAjS0tIoKytj//79DBkypFy7iIh4juk7witr2bJl5aZPnjyJzWZzTQcEBJCTk1Oh3WazkZOTQ35+Pn5+flit1nLtIiLiOW4LjZ9zOp1YLBbXtGEYWCyWX2y/9O/lfj5tRsuWfr++aBE3s9n8PV2C/Er1dd9VW2i0bt263DM4cnNzCQgIqNCel5dHQEAALVq0oKCgAIfDgbe3t2v5yjp1qhCnU8O413W19Rc4N7fA0yXUCLVx/9XVfeflZbnqH9vV9pXb22+/nSNHjnD06FEcDgdbt24lKCiIwMBAfH19SU9PByAlJYWgoCAaNGhAr169SE1NBSA5OZmgoKDqKldERK6g2s40fH19SUhI4JFHHqG0tJTg4GCGDh0KQGJiIvHx8RQWFtK1a1fXUwIXLlxIbGwsL774Im3atGH16tXVVa6IiFyB20Nj586drp/79u3Lli1bKizTuXNnNm/eXKE9MDCQN9980631iYiIebojXERETFNoiIiIaQoNERExTaEhIiKmKTRERMQ0hYaIiJim0BAREdOq7eY+ESnPaS+rVcNn2C+Ukn/2gqfLqBFq276Dqtt/Cg0RD/GyNiB91XRPl2Faz7nrAYUG1L59B1W3/9Q9JSIipik0RETENIWGiIiYptAQERHTFBoiImKaQkNERExTaIiIiGkKDRERMU2hISIipik0RETENIWGiIiYptAQERHTFBoiImKaQkNERExTaIiIiGkKDRERMU2hISIipik0RETENIWGiIiYptAQERHTFBoiImKaQkNERExTaIiIiGlWT7xoVFQUp0+fxmq9+PKLFy+mqKiIFStWUFpayrBhw3jssccAOHz4MPPnz6eoqIhevXqxaNEi13oiIlK9qv3T1zAMMjMz+eSTT1wf/iUlJQwdOpQ333yTNm3aMGPGDD799FOCg4OJiYlh6dKldO/enbi4ODZt2kRkZGR1ly0iInige+rHH38E4N577yUsLIwNGzZw8OBB2rZty4033ojVaiU0NJS0tDSysrIoKSmhe/fuAERERJCWllbdJYuIyP+p9jONc+fO0bdvX5566inKysqIjo5m+vTp2Gw21zIBAQHk5ORw8uTJcu02m42cnJxKvV7Lln6mlrtQ5sCngXeltu1ptbFmqd1sNn9PlyC/QVXsv2oPjR49etCjRw/X9NixY1m7di09e/Z0tRmGgcViwel0YrFYKrRXxqlThTidxjWXs9n8iZy7sVLb9rS3Vk0iN7fA02XUCPowqx7uOt60/6qHmf3n5WW56h/b1d499eWXX/L555+7pg3DIDAwkNzcXFdbbm4uAQEBtG7dulx7Xl4eAQEB1VqviIj8S7WHRkFBAatWraK0tJTCwkLef/99Hn/8cY4cOcLRo0dxOBxs3bqVoKAgAgMD8fX1JT09HYCUlBSCgoKqu2QREfk/1d49NWDAAA4cOMCoUaNwOp1ERkbSo0cPEhISeOSRRygtLSU4OJihQ4cCkJiYSHx8PIWFhXTt2pXo6OjqLllERP6PR254mDNnDnPmzCnX1rdvX7Zs2VJh2c6dO7N58+ZqqkxERK5Gd4SLiIhpCg0RETFNoSEiIqYpNERExDSFhoiImKbQEBER0xQaIiJimkJDRERMU2iIiIhpCg0RETFNoSEiIqYpNERExDSFhoiImKbQEBER0xQaIiJimkJDRERMU2iIiIhpCg0RETFNoSEiIqYpNERExDSFhoiImGb1dAHy6zntZdhs/p4uo1LsF0rJP3vB02WIyK+k0KjFvKwNSF813dNlVErPuesBhYZIbaXuKRERMU2hISIipik0RETENIWGiIiYptAQERHTFBoiImKaQkNERExTaIiIiGkKDRERMa1WhMYHH3zA8OHDGTx4MBs3bvR0OSIi9VaNH0YkJyeHNWvW8N577+Hj48OECRPo3bs3N998s6dLExGpd2p8aOzZs4c+ffrQrFkzAIYMGUJaWhqzZs0ytb6Xl8X0a13fvMmvKdGjfK5r6ekSKq0y+6QytP/cz137Dmrf/qtt+w7M7b9rLWMxDMOoqoLc4aWXXuL8+fM89thjALzzzjscPHiQJUuWeLgyEZH6p8Zf03A6nVgs/0o+wzDKTYuISPWp8aHRunVrcnNzXdO5ubkEBAR4sCIRkfqrxofGXXfdxeeff87p06cpLi5m+/btBAUFebosEZF6qcZfCG/VqhWPPfYY0dHRlJWVMXbsWG677TZPlyUiUi/V+AvhIiJSc9T47ikREak5FBoiImKaQkNERExTaIiIiGkKjZ85fvw4t9xyC7t37y7XPnDgQI4fP/6bt3/LLbf85m140vz58/n66689XUat4e7jSare4sWLmT17drm2Xbt2MWjQIAoLCz1UVc2h0LiCBg0a8NRTT+kAuYJly5Zx6623erqMWkXHU+3yxBNPkJGRwccffwzA+fPnefrpp1m+fDl+fn4ers7zFBpXEBAQwF133cXKlSuvOP8Pf/gDw4cPJzQ0lISEBBwOR7n5x48fJzQ0lEcffZQRI0Zw//33c+bMGdf8BQsWEBYWRlhYGEePHgXgq6++Yty4cYSFhTFlyhRXe1RUFKtWrWL8+PGEhITw6aefApCXl8dDDz1EREQEY8aMYc+ePRXq/Pzzz4mIiCAiIoJp06Zx+vRpAJKTkxk9ejTh4eHExcVRWloKQJ8+fZg+fTrh4eE8/PDDfPTRR65tRUREcOjQIaKiovjiiy8wDINnnnmGIUOGMHz4cN544w0Ajh49yrRp0xg9ejQTJ07k0KFDv2YX1ClXO56udSxJ9WvSpAlLly5lyZIlnD9/nrVr1zJw4EAaNWrExIkTGT16NPfeey/Hjh0D4LXXXiMsLIxRo0axYMECD1dfDQwp59ixY8aAAQOMgoIC4+677zZ27dplGIZhDBgwwDh27Jjx17/+1Rg3bpxx/vx5o6yszHjwwQeNDRs2VNhGp06djL179xqGYRgrVqwwlixZYhiGYXTq1MnYtm2bYRiGkZCQYCQkJBilpaXGgAEDjAMHDhiGYRipqalGRESEYRiGMXnyZGPp0qWGYRjGxx9/bIwePdowDMOYM2eO8Ze//MUwDMPIyckxBg0aZBQUFJSrY/Lkya5tvvzyy8bf/vY34x//+IcxceJEo6SkxDAMw0hMTDTWrVvnqu1Szdu3bzceeeQRwzAM48iRI8bw4cNd29y7d6+RmppqTJgwwSgtLTUKCwuNsLAw4+TJk8b48eONb775xjAMw/juu++MwYMH/5bdUetd7XjauHHjNY8l8Zz58+cbs2fPNkaOHGmcO3fOCA0NNbKysgzDMIzPPvvMmDJlimG3243evXsbFy5cMBwOhxEbG2v89NNPHq7cvWr8HeGe4ufnx5IlS3jqqafYsmWLq33v3r2MGDGCRo0aATBmzBiSk5OZNGlSufVvuukmevfuDcCoUaN48sknXfPuueceAG6++Wa+/PJLMjMzue6661x3ug8bNowFCxZQUFAAQP/+/QHo2LGj64xlz549/Pjjj6xduxYAu93OsWPH6NKli+t1Bg0axKxZs7jnnnsYNGgQv//979mwYQNHjx7lP//zPwEoKyvj3//9313r3H777QAEBwezePFiCgsL2bp1K2FhYeXe3/79+xk2bBg+Pj74+PiQkpJCUVERGRkZzJs3z7Xc+fPnyc/Pp3nz5ub/8+ugKx1PZo8l8YzY2Fjuvvtu1q1bx4kTJzh27BgzZ850zS8sLMTb25sePXowduxYBg0axLRp02jVqpUHq3Y/hcZV9OvXr0K3gtPprLCc3W6v0Ga1/uu/1jAMvL29K8yzWCwYhnHFbRqG4eqq8PX1dS1/eR1vvPGG6zkjJ0+epGXL8uP7T506lQEDBvDJJ5/wzDPPcPDgQRo3bsywYcOIj48HoKioqFyXSMOGDQHw8fFhwIAB7Ny5k7S0NF566aUK7+/yeo4fP07Tpk1dAXLJTz/95Kqxvvv58WT2WBLP8PPz47rrriMwMJDCwkJ+97vfuY5th8NBXl4eAElJSXz11Vd89tlnTJ8+ncTERO68805Plu5WuqZxDbGxsezatYuTJ08CF/v9P/zwQ0pKSrDb7bz77rv06dOnwnpHjhzh8OHDALz77rtXHWSxffv2nDlzhoMHDwKQmprKDTfccNUP2z59+vDWW28B8P333xMaGkpxcXG5ZcaNG0dRURFTp05l6tSpHDp0iN69e7Njxw5OnTqFYRg8/fTTrusRPxceHs5rr71Gs2bNCAwMLDfvjjvuYPv27ZSVlVFcXMz06dPJy8vjpptucv1i7d69W381/8zlx5PZY0k8r3379pw9e5Yvv/wSuPg7/eSTT3L69GmGDx9Op06dePTRR/n973/Pt99+6+Fq3UtnGtdwqVvhvvvuA2DAgAEcPnyYMWPGYLfb6devH5MnT66wXtOmTVm7di3//Oc/ueWWW1i6dOkvvoaPjw9r1qxhyZIlFBcX07RpU9asWXPVuuLj41mwYAGhoaEArFq1qsI3Ox5//HFiY2OxWq00btyYpUuXctNNNzFr1iymTJmC0+mkS5cuPPDAA1d8jZ49e1JQUMDEiRMrzAsJCSEjI4OIiAicTifR0dG0a9eOZ555hqeffpr169fToEED1qxZo+efXOby4+nuu+/m3Llz1zyWxPN8fHx47rnnWLZsGaWlpfj5+bFy5UpatGjB+PHjGTt2LI0aNaJdu3aMGTPG0+W6lQYsdIPjx48THR3Nzp07PV2KiEiVUveUiIiYpjMNERExTWcaIiJimkJDRERMU2iIiIhpCg2pVy6NOnulr7bGxsZyyy23uMboupLnn3+exYsXA/DOO++wceNGAN5++21efvnlq772F198wciRI69ZY1RUFGlpaRXav/766wqjr4pUN92nIfWOr68vR44cISsry3XT4vnz5/mf//mfSm0nPT2djh07AlzxXpaqduutt7qGjRHxFJ1pSL3j7e3NsGHD+OCDD1xt27dvZ9CgQQDs27ev3BnBlc4QduzYwc6dO3n99dfZuHFjuTOQgQMH8uyzzxIREUFISIjrzv3LXbhwgeXLlzN69GjCwsKIjY295tDpl9cRGxvL0qVLiYqKIiQkhFmzZlFUVATADz/8wL333ktERATh4eFs3rwZuDhkzOzZswkPD2f06NHEx8dfcSgTkatRaEi9NGrUqHJjZF0aLt6skJAQBg4cyNSpU684VMrZs2d59913efPNN1m7dm2FoSVefvllvL29ee+999iyZQsBAQEkJiZW6j1kZGTw6quvkpqaSlZWFmlpadjtdmbPns0TTzzBe++9x4YNG/jjH//IV199xY4dOygqKiIlJcUVJJeG9xYxS91TUi9169YNb29vMjIyaNmyJUVFRXTq1KnKth8ZGYnFYqF169b079+f3bt307VrV9f8v/71rxQUFLieg1JWVlZhwMlr6d+/Pz4+PgB06tSJs2fPkpmZyT//+U/i4uJcy5WUlHDo0CH69+/PmjVriIqK4q677mLKlCm0bdu2Ct6t1CcKDam3wsLC2LJlCy1atCA8PNzV3qJFCy6/57WsrKzS2758lGOn04mXV/mTeqfTSVxcHMHBwcDFrqNLD8My69KIxPCvEZMdDgf+/v7lzqLy8vLw9/fH19eXHTt28MUXX7B3716mTZvG4sWLGThwYKXfn9Rf6p6Seis8PJy0tDRSU1PLXbOwWCxkZ2e7RgL+8MMPr7i+t7f3Lw5lnpycDEB2dja7d++uMMpxv3792LhxIxcuXMDpdPLUU0+xevXq3/ye2rVrR8OGDV2hceLECUaOHElGRgZvvfUW8+bNo1+/fsTExNCvXz89WVEqTWcaUm+1atWKDh064O/vX24Y+g4dOjBhwgTGjBmDzWbj7rvv5uuvv66wflBQEAkJCVfc9vHjx4mIiKCkpIT4+Hjat29Pbm6ua/5DDz3EypUrGT16NA6Hgy5duhAbG+uaP3fu3HIPs4qMjLzq8PqX+Pj4kJSUxLJly1i/fj12u51HH32Unj170qVLF/bt28fw4cNp1KgRbdq0ISoqysx/lYiLxp4SqWIDBw7kueee49Zbb/V0KSJVTt1TIiJims40RETENJ1piIiIaQoNERExTaEhIiKmKTRERMQ0hYaIiJim0BAREdP+P4ScA19xjEo7AAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"MultipleLines\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Bei Kunden, die mehrere Anschl\u00fcsse haben, ist die Wahrscheinlichkeit einer Abwanderung geringer."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Internet Service"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 165,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"InternetService\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Wenn ein Kunde einen Glasfaseranschluss hat, ist es wahrscheinlicher, dass er ausf\u00e4llt als ein Kunde mit DSL."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Online-Sicherheit"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 166,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"OnlineSecurity\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Kunden, die den Internet-Sicherheitsdienst nicht nutzen, werden eher abwandern."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Online Backup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 167,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"OnlineBackup\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Personen, die keine Online-Datensicherung nutzen, sind eher bereit, umzuziehen."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Ger\u00e4teschutz\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 168,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"DeviceProtection\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Kunden, die keinen zus\u00e4tzlichen Ger\u00e4teschutz erworben haben, werden mit gr\u00f6\u00dferer Wahrscheinlichkeit migrieren."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Technischer Support"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"TechSupport\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Kunden, die keinen technischen Support in Anspruch nehmen, werden eher abwandern."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Streaming-TV/ Streaming-Filme"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 170,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for col in [\"StreamingTV\", \"StreamingMovies\"]:\n",
+ " sns.countplot(x=col, hue='Churn', data=data_no_mv)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Die Hinzunahme von Film- und TV-Streaming-Angeboten hat kaum Auswirkungen auf die Abwanderungsrate."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Papierlose Abrechnung"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 171,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"PaperlessBilling\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Kunden, die ohne Rechnung bezahlen, werden eher abwandern."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Zahlungsmethode"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 172,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"PaymentMethod\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Kunden, die mit elektronischen Schecks bezahlen, wandern deutlich h\u00e4ufiger ab als Kunden, die eine andere Zahlungsmethode verwenden."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Vertrag"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 173,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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TEZHSx1JorF27lk2bNl1wBZWIiFxfLN3cV7t2bQWGiIhY29No3bo18fHxdOjQgfLly7vbdU5DROT6Yik0Vq9eDUBSUpK7Tec0RESuP5ZCY8uWLZ6uQ0REygBLobF48eKLtj/22GPFWoyIiJRulkLjX//6l/v/Z86cYefOnbRu3dpjRYmISOlk+ea+86WmpjJhwgSPFCQiIqXXVT1Po2bNmqSkpBR3LSIiUsoV+ZyGMYbdu3cXujtcRESuD0U+pwFnb/YbM2aMRwoSEZHSq0jnNFJSUigoKKB+/foeLUpEREonS6Fx6NAhhg4dSlpaGi6Xi6pVq/Laa69x2223ebo+EREpRSydCH/uuecYMGAAO3fuJDk5mSFDhhAXF+fp2kREpJSxFBqZmZk8+OCD7umePXuSlZXlsaJERKR0shQaTqeT48ePu6ePHTvmqXpERKQUs3RO45FHHqFXr15069YNm83G+vXrefTRRz1dm4iIlDKW9jTCwsIAcDgc/Pvf/yY1NZVOnTp5tDARESl9LIXGuHHj6Nu3L6NHj2b27NmMGDGC2NjYKy6Xk5NDeHg4R44cAWDbtm1ERETQuXNn5s2b555vz549REVF0aVLFyZMmEBBQQEAR48epW/fvnTt2pUhQ4aQm5t7Na9RRESKiaXQyMrKon///gD4+/vz5z//mfT09Msus2vXLvr06cPBgwcByMvLIzY2loSEBNavX8/u3bvZunUrAKNHj2bSpEls3LgRYwwrVqwAIC4ujpiYGJKSkmjUqBEJCQlX+zpFRKQYWD4Rnpqa6p7OyMjAGHPZZVasWMHkyZMJCgoC4Ntvv6V+/frUq1cPu91OREQESUlJpKSkkJeXR9OmTQGIiooiKSkJh8PBzp076dKlS6F2ERHxHksnwv/85z/zwAMP0K5dO2w2G9u2bbviMCLTp08vNJ2WlkZgYKB7OigoiNTU1AvaAwMDSU1NJSsri4CAAOx2e6F2ERHxHkuhER0dTaNGjdi+fTu+vr488cQT3HnnnUVakcvlwmazuaeNMdhstku2n/v3fL+dtqJ69YAiLyOeFRhYydsllBp6L8qu63XbWQoNgIYNG9KwYcOrXlGtWrUKnQdJT08nKCjogvaMjAyCgoKoVq0a2dnZOJ1OfH193fMXVWZmDi7X5Q+lwfX7C+AN6enZxd5nWd1+nngvyqKyuP2u1W3n42O77Jftq3qextVo0qQJBw4c4NChQzidTtatW0doaCh169bF39+f5ORkABITEwkNDaVcuXKEhISwfv16ANauXUtoaGhJlSsiIhdheU/j9/L392fWrFkMHz6c/Px8wsLC6Nq1KwBz5sxh4sSJ5OTkEBwc7L5Sa/LkyYwbN46FCxdSu3Zt5s6dW1LliojIRXg8NLZs2eL+f+vWrfnggw8umKdhw4asXLnygva6deuybNkyj9YnIiLWldjhKRERKfsUGiIiYplCQ0RELFNoiIiIZQoNERGxTKEhIiKWKTRERMQyhYaIiFim0BAREcsUGiIiYplCQ0RELFNoiIiIZQoNERGxTKEhIiKWKTRERMQyhYaIiFim0BAREcsUGiIiYplCQ0RELFNoiIiIZQoNERGxTKEhIiKWKTRERMQyhYaIiFim0BAREcsUGiIiYplCQ0RELFNoiIiIZQoNERGxTKEhIiKWKTRERMQyhYaIiFim0BAREcsUGiIiYplCQ0RELFNoiIiIZQoNERGxTKEhIiKW2b2x0n79+nHs2DHs9rOrf+6558jNzWXmzJnk5+fTrVs3Ro4cCcCePXuYMGECubm5hISEEBcX515ORERKVol/+hpjOHjwIJ988on7wz8vL4+uXbuybNkyateuzaBBg9i6dSthYWGMHj2aadOm0bRpU2JjY1mxYgUxMTElXbaIiOCFw1P79+8H4PHHHycyMpLly5fz7bffUr9+ferVq4fdbiciIoKkpCRSUlLIy8ujadOmAERFRZGUlFTSJYuIyK9KPDROnjxJ69ateeWVV1iyZAnvvvsuR48eJTAw0D1PUFAQqamppKWlFWoPDAwkNTW1pEsWEZFflfjhqWbNmtGsWTP3dHR0NPPnz6d58+buNmMMNpsNl8uFzWa7oL0oqlcP+P1FS7EKDKzk7RJKDb0XZdf1uu1KPDT+8Y9/4HA4aN26NXA2COrWrUt6erp7nvT0dIKCgqhVq1ah9oyMDIKCgoq0vszMHFwuc8X5rtdfAG9IT88u9j7L6vbzxHtRFpXF7XetbjsfH9tlv2yX+OGp7Oxs4uPjyc/PJycnhzVr1vDss89y4MABDh06hNPpZN26dYSGhlK3bl38/f1JTk4GIDExkdDQ0JIuWUREflXiexrt27dn165dPPDAA7hcLmJiYmjWrBmzZs1i+PDh5OfnExYWRteuXQGYM2cOEydOJCcnh+DgYPr371/SJYuIyK+8csPDiBEjGDFiRKG21q1b88EHH1wwb8OGDVm5cmUJVSYiIpejO8JFRMQyhYaIiFim0BAREcsUGiIiYplCQ0RELFNoiIiIZQoNERGxTKEhIiKWKTRERMQyhYaIiFim0BAREcsUGiIiYplCQ0RELFNoiIiIZQoNERGxTKEhIiKWKTRERMQyhYaIiFim0BAREcsUGiIiYplCQ0RELFNoiIiIZQoNERGxTKEhIiKWKTRERMQyhYaIiFhm93YBItcrV4GDwMBK3i7DsoIz+WSdOOPtMsTLFBoiXuJjL0dy/ABvl2FZ8zFvAAqN650OT4mIiGUKDRERsUyhISIilik0RETEMoWGiIhYptAQERHLFBoiImKZQkNERCxTaIiIiGW6I1xEpIjK2hAwUHzDwJSJ0Pjwww9ZuHAhBQUFPProo/Tt29fbJYnIdaysDQEDxTcMTKkPjdTUVObNm8fq1avx8/Ojd+/etGzZkttvv93bpYmIXHdKfWhs27aNVq1aUaVKFQC6dOlCUlISw4YNs7S8j4/N8rpqVK14NSV6ld+N1b1dQpEVZZsUhbaf53lq20HZ235lbduBte13pXlsxhhTXAV5wmuvvcapU6cYOXIkAO+//z7ffvstU6dO9XJlIiLXn1J/9ZTL5cJm+2/yGWMKTYuISMkp9aFRq1Yt0tPT3dPp6ekEBQV5sSIRketXqQ+NNm3a8OWXX3Ls2DFOnz7Npk2bCA0N9XZZIiLXpVJ/IrxmzZqMHDmS/v3743A4iI6OpnHjxt4uS0TkulTqT4SLiEjpUeoPT4mISOmh0BAREcsUGiIiYplCQ0RELFNoWHDkyBEaNGjApEmTCrXv2bOHBg0asHr16iL3uWLFCtatWwfAuHHjLPVx/jLelJ2dzVNPPQWcfW/uu+8+L1fkWbm5ucTFxdGpUyciIyOJiYnhyy+/9HZZYkFcXBw9evTg/vvvp1GjRvTo0YMePXqwatUqb5dWZpX6S25LiypVqvDZZ5/hdDrx9fUFYP369VSrVu2q+vv6669p0aKFx5fxhBMnTrBnzx5vl1EijDEMHjyYu+66i7///e/4+fnxww8/MHDgQF544QVatmzp7RLlMiZPngyc/XLTv39/EhMTvVxR2ac9DYsqVqzIXXfdxc6dO91tX3zxBW3atAHgk08+oUePHkRERDB06FAyMjIAuO+++3jxxReJjo6me/fu7N69m23btrFlyxbmz5/PZ599BsD//d//ER0dTfv27XnvvfcuWP9vl8nIyGDQoEFERETw4IMP8umnn1607n79+jFz5kyio6OJiIhg69atPPHEE9x7770sWbIEgNOnTzNq1CjCw8OJiIhg7dq1AKxevZqRI0fy+OOP06lTJ6ZMmQLAtGnTSEtLc+9t5OXlMXLkSMLDw4mJiSErK+t3v9+lxY4dOzh69Cjjx4/Hz88PgD/+8Y8MGTKEhIQE4Ox7HB8fT69evejUqRNbt24FICMjg6FDhxIVFUXPnj3Ztm1bob5dLhf33XcfBw4cAODUqVOEhYWRn5/Pp59+SnR0NA888ADDhg1zv6cbNmzg4YcfJjIykq5du/L111+7axg2bBhdunS5bgL99xg8eLB7O82dO5cBA84Oc56WlkZ4eDgAq1atcv9NjBs3jtzc3EJ9vP/++4waNco9vWDBAhYtWkRubi5jx44lKiqKHj16uI8O5OTk8PTTT9OrVy/at29PbGwsxhi++uoroqOjiYqKYuzYsSXx8n8fI1d0+PBh0759e/PBBx+YKVOmGGOM2bVrlxk3bpwZO3asWbRokWnbtq05fPiwMcaY119/3QwfPtwYY0z79u3N4sWLjTHGvPXWW2bYsGHGGGPGjh1rVq1a5f7/oEGDjMvlMvv27TMtW7a8aB3nL/P000+bv/71r8YYY/7zn/+YP/3pTyY9Pf2CZR555BEzffp0Y4wxCxYsMB07djSnTp0yR44cMSEhIcYYY55//nkzdepUY4wxmZmZ5r777jN79uwxq1atMmFhYSY7O9ucOnXKhIaGmr1797rfj3PvTYMGDcyuXbuMMcYMHz7cLF++/Grf6lLn9ddfN08//fQF7fv27TPNmjUzxpx9j6dNm2aMMebjjz82Dz74oDHGmBEjRpiPPvrIGGNMamqq6dChg8nOzi7Uz0svvWRefPFFY4wxa9asMZMmTTKZmZkmMjLSHD9+3BhjzDvvvGNiY2ON0+k0/fv3N5mZmcYYY95//30zaNAgdw3z588v7pd/zTj/d9YYY95++20za9YsY4wxffr0Me3btzcFBQVm5cqVJj4+3uzdu9d07NjRHDt2zBhjzJQpU9zzn5OTk2Nat27t3qadO3c2v/zyi5k9e7ZZunSpMcaY7Oxs0717d/Of//zHfPjhhyYhIcEYY0x+fr7p2LGj+e6778z27dtN8+bNzcmTJz3+PhQHHZ4qgnN7DS6Xiw0bNtCtWzfWr19PhQoVaNy4MTfddBMAvXr1YtGiRe7l2rVrB8Add9zBpk2bLtp3hw4dsNls3HHHHZa+qW/fvp1p06YBUK9ePZo0acKuXbvo0KHDBfOeG3alTp06NGnShAoVKlC3bl1Onjzp7mvGjBkAVKtWjQ4dOrBjxw4CAgJo1qwZAQEB7vWcOHGCihULD2EdFBTkvkv/9ttvv6b2NGw2G06n84J2h8NRaODM87fx8ePHgbN7h/v372f+/PkAFBQUcPjwYe666y73clFRUTz22GM888wzrFmzhmeffZZdu3bx888/079/f+DsHknlypXx8fHhlVdeYcuWLRw4cIAdO3bg4/PfgwUaKcG6e++9lyFDhpCTkwNAgwYN+P777/n000/p168fO3fupH379lStWhU4+zc9fvz4Qn1UrFiRsLAwNm/eTL169ahXrx41a9Zk27Zt5OXluc+bnDp1ih9//JHw8HC+/fZblixZwv79+zl+/DinTp0C4JZbbqFSpbLxJECFRhFUrFiRhg0bkpyczPbt2xk1ahTr16/H5XIVms8YQ0FBgXva398f4LKj8547T3L+PE8++SRpaWkAhULo3Dp+O+10Oi+6TLly5dzz2e0XbvJL9XV+7edq++28v+3zUvOUVU2aNGHZsmU4HI5C7+M///lPGjVq5J6+2DZ2uVwsXbrU/SyYtLQ0qlcv/AyGm266iTp16rBp0yYyMzNp0qQJH330EXfffTevvvoqAPn5+eTm5pKbm0t0dDSRkZHcc889NGjQgL/97W/uvsqXL1/sr/9aVbt2bVwuF5s2beLuu++mRo0abN++ne+//55mzZrxww8/FJr/t3/T5/Ts2ZOFCxdy0003ERUVBZzd7rNnzyY4OBg4e5iycuXKLFu2jI0bN/Lwww/Tpk0b/vWvf7n/VsrSttM5jSLq1q0bL7zwAo0aNXJ/WObl5bFr1y6OHDkCwHvvvXfFE6S+vr4X/QZ7vtdff53ExEQSExOpWbNmoWVatWrFypUrATh8+DBff/01TZs2vWAZK87v69ixY3z88ceXPeFut9sv+gd0LQoJCeH2229nxowZOBwOAHbv3s3ChQsZOnToZZdt1aoVb7/9NgA//fQTERERnD59+oL5evbsybRp04iMjATOBtU///lP97mOhIQE4uPjOXjwIDabjcGDB9OyZUs2b958xd8hubTQ0FAWLlxIixYtaNWqFcuWLaNJkyb4+vrSokULtmzZ4t5rXLFixUX/pkNCQvjll1/46quv6NixI3B2u7/zzjvA2S8KkZGR/Pzzz3zxxRf06tWLyMhI8vPz2bt37wVfOMsC7WkUUfv27ZkwYQLPPPOMu61GjRo899xzDBs2DIfDQZ06dZg+ffpl+2nTpg1z584t0i7p+ctMmDCBSZMmuS/VnTZt2lUPGf/UU08xZcoUIiIicDqdDB48mODgYPbt23fR+atXr06dOnXcJ9mvdS+//DLz5s0jPDwcX19fKleuzOzZs6/4xWDixIlMmjSJiIgIAOLj492H+s7XuXNn/vKXv9CjRw8AAgMDmTFjBiNGjMDlclGzZk1mz57NjTfeyF133UW3bt2w2Wy0bduW5OTk4n/B14l7772XxYsX07x5c2644QYcDgft27cHoGHDhgwaNIh+/frhcDgIDg4mLi7uov106tSJ48ePuy+UGDZsGFOmTCE8PByn08no0aO5+eabefTRR5kyZQqLFi1yH/o9cuQIN998c4m95uKgAQtFvMgYw6effso777zjPhwlZYMxBofDwWOPPUZsbKz7cNS1TnsaIl40Y8YMPvnkE15//XVvlyJFlJ6eTvfu3XnooYeum8AA7WmIiEgR6ES4iIhYptAQERHLFBoiImKZQkPEAqfTyeLFi93jCd1///3Mnj2bM2fOXFV/2dnZ7ju+i0Nx9ydyKQoNEQumTJnCN998w9KlS0lMTGTlypUcOHCACRMmXFV/J06c4Lvvviu2+oq7P5FL0dVTIldw5MgRwsPD+fzzzwvdnJeens7XX39NmzZtiIuLY+/evdhsNtq1a8ezzz6L3W7nf/7nfxg4cCBffPEFaWlpDBgwgJiYGPr168c//vEP7rzzTlavXk2TJk3o0KEDe/fuZc6cOezbt4/33nsPh8PBiRMnePLJJ4mJiQHgtddeY82aNdjtdurXr8+sWbMYOnRoof7ODUsjUuxKfIhEkTImKSnJ9OzZ85I/HzNmjJk6dapxuVwmPz/fPP744+a1114zxhhz5513mmXLlhljjPnuu+9Mo0aNTF5enjl8+LBp2rSpu48777zTrFmzxhhzdvTUhx9+2D3C6jfffOOe96OPPjKdO3d2j4A7Y8YMk5CQcEF/Ip6im/tErsDHx+eyYwSdu6PbZrPh5+dH7969Wbp0KQMHDgRwjzwcHBzMmTNn3COb/lZISAhwdmDMV199la1bt3Lw4EH27t3rXubLL7+ka9euVK5cGcA98uq5cc9EPE3nNESuoHHjxuzfv989jPY5qampDBw4EJfLdcHotpcb5dhc4ojwDTfcAMAvv/zCAw88QEpKCs2bN2fEiBHueXx9fQut6+TJkwoMKVEKDZErqFmzJhEREcTGxrqDIycnhylTplClShXatm3L8uXLMcZw5swZVqxY4X6i46XY7XacTudFA2T37t1Uq1aNoUOH0rZtWz755BPg7BVcbdq0YfPmze46FixYwJIlSy7bn0hx0uEpEQsmT55MQkICvXv3xtfXlzNnztCxY0eGDx9Obm4u06ZNIyIiAofDQbt27Rg8ePBl+wsMDKRx48Z079690DMxAP70pz+xcuVKunbtis1mo0WLFlSrVo1Dhw4RFhbGTz/9RJ8+fYCzD72aOnWq+0Fg5/o79/AgkeKmq6dERMQyHZ4SERHLFBoiImKZQkNERCxTaIiIiGUKDRERsUyhISIilik0RETEMoWGiIhY9v+wC1pChZXi1wAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.countplot(x=\"Contract\", hue=\"Churn\", data=data_no_mv)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Bei Kunden mit kurzfristigen Vertr\u00e4gen ist die Wahrscheinlichkeit gr\u00f6\u00dfer, dass sie das Unternehmen verlassen, als bei Kunden mit l\u00e4ngerfristigen Vertr\u00e4gen."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenvorbereitung"
+ },
+ "source": [
+ "# 3. Aufbereitung der Daten"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenvorbereitung"
+ },
+ "source": [
+ "## 3.1. Reduzieren der Kunden-ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 174,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# Removing the Customer ID, it does not add value to the model\n",
+ "data_prep = data_no_mv.drop(\"customerID\", axis = 1)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenvorbereitung"
+ },
+ "source": [
+ "## 3.2. Umkodierung der kategorialen Variablen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 175,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# Convert binary variables to 1 and 0 with Yes and No\n",
+ "bin_var = [\"Partner\",\"Dependents\",\"PhoneService\",\"PaperlessBilling\",\"Churn\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 176,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "def binaer_umwandeln(x):\n",
+ " return x.map({'Yes':1,'No':0})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 177,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>gender</th>\n",
+ " <th>SeniorCitizen</th>\n",
+ " <th>Partner</th>\n",
+ " <th>Dependents</th>\n",
+ " <th>tenure</th>\n",
+ " <th>PhoneService</th>\n",
+ " <th>MultipleLines</th>\n",
+ " <th>InternetService</th>\n",
+ " <th>OnlineSecurity</th>\n",
+ " <th>OnlineBackup</th>\n",
+ " <th>DeviceProtection</th>\n",
+ " <th>TechSupport</th>\n",
+ " <th>StreamingTV</th>\n",
+ " <th>StreamingMovies</th>\n",
+ " <th>Contract</th>\n",
+ " <th>PaperlessBilling</th>\n",
+ " <th>PaymentMethod</th>\n",
+ " <th>MonthlyCharges</th>\n",
+ " <th>TotalCharges</th>\n",
+ " <th>Churn</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Female</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>No phone service</td>\n",
+ " <td>DSL</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Month-to-month</td>\n",
+ " <td>1</td>\n",
+ " <td>Electronic check</td>\n",
+ " <td>29.85</td>\n",
+ " <td>29.85</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Male</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>34</td>\n",
+ " <td>1</td>\n",
+ " <td>No</td>\n",
+ " <td>DSL</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>One year</td>\n",
+ " <td>0</td>\n",
+ " <td>Mailed check</td>\n",
+ " <td>56.95</td>\n",
+ " <td>1889.50</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Male</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>No</td>\n",
+ " <td>DSL</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Month-to-month</td>\n",
+ " <td>1</td>\n",
+ " <td>Mailed check</td>\n",
+ " <td>53.85</td>\n",
+ " <td>108.15</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>Male</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>45</td>\n",
+ " <td>0</td>\n",
+ " <td>No phone service</td>\n",
+ " <td>DSL</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>One year</td>\n",
+ " <td>0</td>\n",
+ " <td>Bank transfer (automatic)</td>\n",
+ " <td>42.30</td>\n",
+ " <td>1840.75</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>Female</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>No</td>\n",
+ " <td>Fiber optic</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Month-to-month</td>\n",
+ " <td>1</td>\n",
+ " <td>Electronic check</td>\n",
+ " <td>70.70</td>\n",
+ " <td>151.65</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " gender SeniorCitizen Partner Dependents tenure PhoneService \\\n",
+ "0 Female 0 1 0 1 0 \n",
+ "1 Male 0 0 0 34 1 \n",
+ "2 Male 0 0 0 2 1 \n",
+ "3 Male 0 0 0 45 0 \n",
+ "4 Female 0 0 0 2 1 \n",
+ "\n",
+ " MultipleLines InternetService OnlineSecurity OnlineBackup \\\n",
+ "0 No phone service DSL No Yes \n",
+ "1 No DSL Yes No \n",
+ "2 No DSL Yes Yes \n",
+ "3 No phone service DSL Yes No \n",
+ "4 No Fiber optic No No \n",
+ "\n",
+ " DeviceProtection TechSupport StreamingTV StreamingMovies Contract \\\n",
+ "0 No No No No Month-to-month \n",
+ "1 Yes No No No One year \n",
+ "2 No No No No Month-to-month \n",
+ "3 Yes Yes No No One year \n",
+ "4 No No No No Month-to-month \n",
+ "\n",
+ " PaperlessBilling PaymentMethod MonthlyCharges TotalCharges \\\n",
+ "0 1 Electronic check 29.85 29.85 \n",
+ "1 0 Mailed check 56.95 1889.50 \n",
+ "2 1 Mailed check 53.85 108.15 \n",
+ "3 0 Bank transfer (automatic) 42.30 1840.75 \n",
+ "4 1 Electronic check 70.70 151.65 \n",
+ "\n",
+ " Churn \n",
+ "0 0 \n",
+ "1 0 \n",
+ "2 1 \n",
+ "3 0 \n",
+ "4 1 "
+ ]
+ },
+ "execution_count": 177,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_prep[bin_var]=data_prep[bin_var].apply(binaer_umwandeln)\n",
+ "data_prep.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 178,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>SeniorCitizen</th>\n",
+ " <th>Partner</th>\n",
+ " <th>Dependents</th>\n",
+ " <th>tenure</th>\n",
+ " <th>PhoneService</th>\n",
+ " <th>PaperlessBilling</th>\n",
+ " <th>MonthlyCharges</th>\n",
+ " <th>TotalCharges</th>\n",
+ " <th>Churn</th>\n",
+ " <th>gender_Male</th>\n",
+ " <th>...</th>\n",
+ " <th>TechSupport_Yes</th>\n",
+ " <th>StreamingTV_No internet service</th>\n",
+ " <th>StreamingTV_Yes</th>\n",
+ " <th>StreamingMovies_No internet service</th>\n",
+ " <th>StreamingMovies_Yes</th>\n",
+ " <th>Contract_One year</th>\n",
+ " <th>Contract_Two year</th>\n",
+ " <th>PaymentMethod_Credit card (automatic)</th>\n",
+ " <th>PaymentMethod_Electronic check</th>\n",
+ " <th>PaymentMethod_Mailed check</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>29.85</td>\n",
+ " <td>29.85</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>34</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>56.95</td>\n",
+ " <td>1889.50</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>53.85</td>\n",
+ " <td>108.15</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>45</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>42.30</td>\n",
+ " <td>1840.75</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>...</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>70.70</td>\n",
+ " <td>151.65</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows \u00d7 31 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " SeniorCitizen Partner Dependents tenure PhoneService PaperlessBilling \\\n",
+ "0 0 1 0 1 0 1 \n",
+ "1 0 0 0 34 1 0 \n",
+ "2 0 0 0 2 1 1 \n",
+ "3 0 0 0 45 0 0 \n",
+ "4 0 0 0 2 1 1 \n",
+ "\n",
+ " MonthlyCharges TotalCharges Churn gender_Male ... TechSupport_Yes \\\n",
+ "0 29.85 29.85 0 0 ... 0 \n",
+ "1 56.95 1889.50 0 1 ... 0 \n",
+ "2 53.85 108.15 1 1 ... 0 \n",
+ "3 42.30 1840.75 0 1 ... 1 \n",
+ "4 70.70 151.65 1 0 ... 0 \n",
+ "\n",
+ " StreamingTV_No internet service StreamingTV_Yes \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " StreamingMovies_No internet service StreamingMovies_Yes \\\n",
+ "0 0 0 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " Contract_One year Contract_Two year \\\n",
+ "0 0 0 \n",
+ "1 1 0 \n",
+ "2 0 0 \n",
+ "3 1 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check \\\n",
+ "0 0 1 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 1 \n",
+ "\n",
+ " PaymentMethod_Mailed check \n",
+ "0 0 \n",
+ "1 1 \n",
+ "2 1 \n",
+ "3 0 \n",
+ "4 0 \n",
+ "\n",
+ "[5 rows x 31 columns]"
+ ]
+ },
+ "execution_count": 178,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# create dummies\n",
+ "data_enc = pd.get_dummies(data_prep, drop_first=True)\n",
+ "data_enc.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 179,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# Dropping of dummies that also contain No phone service and No Internet service\n",
+ "dup_variables = [\"OnlineSecurity_No internet service\",\"OnlineBackup_No internet service\", \"TechSupport_No internet service\",\"StreamingTV_No internet service\",\"StreamingMovies_No internet service\", \"DeviceProtection_No internet service\",\"MultipleLines_No phone service\"]\n",
+ "data_enc.drop(dup_variables, axis=1, inplace=True)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenvorbereitung"
+ },
+ "source": [
+ "## 3.3. Test auf Multikollinearit\u00e4t"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenvorbereitung"
+ },
+ "source": [
+ "Um ein korrektes Funktionieren der sp\u00e4teren Regression zu gew\u00e4hrleisten, darf keine Multikollinearit\u00e4t zwischen den Variablen bestehen. Das Vorhandensein einer solchen wird mit Hilfe der Bibliothek Statsmodel \u00fcberpr\u00fcft."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 180,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " feature VIF\n",
+ "0 SeniorCitizen 1.376564\n",
+ "1 Partner 2.824725\n",
+ "2 Dependents 1.969391\n",
+ "3 tenure 20.482153\n",
+ "4 PhoneService 47.244378\n",
+ "5 PaperlessBilling 2.956951\n",
+ "6 MonthlyCharges 212.353073\n",
+ "7 TotalCharges 21.374002\n",
+ "8 gender_Male 2.021331\n",
+ "9 MultipleLines_Yes 2.861614\n",
+ "10 InternetService_Fiber optic 17.695260\n",
+ "11 InternetService_No 8.234451\n",
+ "12 OnlineSecurity_Yes 2.682712\n",
+ "13 OnlineBackup_Yes 2.909898\n",
+ "14 DeviceProtection_Yes 2.992570\n",
+ "15 TechSupport_Yes 2.758343\n",
+ "16 StreamingTV_Yes 4.928957\n",
+ "17 StreamingMovies_Yes 5.090603\n",
+ "18 Contract_One year 2.056188\n",
+ "19 Contract_Two year 3.487502\n",
+ "20 PaymentMethod_Credit card (automatic) 1.984196\n",
+ "21 PaymentMethod_Electronic check 2.955994\n",
+ "22 PaymentMethod_Mailed check 2.383290\n"
+ ]
+ }
+ ],
+ "source": [
+ "# independent variables\n",
+ "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
+ " \n",
+ "# VIF dataframe \n",
+ "vif_data = pd.DataFrame() \n",
+ "vif_data[\"feature\"] = vif_test.columns \n",
+ " \n",
+ "# VIF for each Feature \n",
+ "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
+ " for i in range(len(vif_test.columns))] \n",
+ " \n",
+ "print(vif_data)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "\"MonthlyCharges\" hat den h\u00f6chsten VIF und wird aus dem Datensatz entfernt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 181,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "data_enc.drop(\"MonthlyCharges\", axis=1, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 182,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " feature VIF\n",
+ "0 SeniorCitizen 1.366018\n",
+ "1 Partner 2.817414\n",
+ "2 Dependents 1.961947\n",
+ "3 tenure 17.073930\n",
+ "4 PhoneService 9.277446\n",
+ "5 PaperlessBilling 2.796488\n",
+ "6 TotalCharges 18.028499\n",
+ "7 gender_Male 1.942509\n",
+ "8 MultipleLines_Yes 2.514269\n",
+ "9 InternetService_Fiber optic 4.186492\n",
+ "10 InternetService_No 3.473225\n",
+ "11 OnlineSecurity_Yes 1.986701\n",
+ "12 OnlineBackup_Yes 2.182678\n",
+ "13 DeviceProtection_Yes 2.299462\n",
+ "14 TechSupport_Yes 2.099655\n",
+ "15 StreamingTV_Yes 2.749724\n",
+ "16 StreamingMovies_Yes 2.771330\n",
+ "17 Contract_One year 2.056169\n",
+ "18 Contract_Two year 3.468149\n",
+ "19 PaymentMethod_Credit card (automatic) 1.820729\n",
+ "20 PaymentMethod_Electronic check 2.535918\n",
+ "21 PaymentMethod_Mailed check 1.982063\n"
+ ]
+ }
+ ],
+ "source": [
+ "# the independent variables set \n",
+ "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
+ " \n",
+ "# VIF dataframe \n",
+ "vif_data = pd.DataFrame() \n",
+ "vif_data[\"feature\"] = vif_test.columns \n",
+ " \n",
+ "# VIF for each Feature \n",
+ "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
+ " for i in range(len(vif_test.columns))] \n",
+ " \n",
+ "print(vif_data)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "\"TotalCharges\" hat den h\u00f6chsten VIF und wird aus dem Datensatz entfernt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 183,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "data_enc.drop(\"TotalCharges\", axis=1, inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 184,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " feature VIF\n",
+ "0 SeniorCitizen 1.363244\n",
+ "1 Partner 2.816895\n",
+ "2 Dependents 1.956413\n",
+ "3 tenure 7.530356\n",
+ "4 PhoneService 9.260839\n",
+ "5 PaperlessBilling 2.757816\n",
+ "6 gender_Male 1.931277\n",
+ "7 MultipleLines_Yes 2.426699\n",
+ "8 InternetService_Fiber optic 3.581328\n",
+ "9 InternetService_No 3.321342\n",
+ "10 OnlineSecurity_Yes 1.947904\n",
+ "11 OnlineBackup_Yes 2.093763\n",
+ "12 DeviceProtection_Yes 2.241375\n",
+ "13 TechSupport_Yes 2.060410\n",
+ "14 StreamingTV_Yes 2.636855\n",
+ "15 StreamingMovies_Yes 2.661529\n",
+ "16 Contract_One year 2.055971\n",
+ "17 Contract_Two year 3.456061\n",
+ "18 PaymentMethod_Credit card (automatic) 1.794059\n",
+ "19 PaymentMethod_Electronic check 2.401970\n",
+ "20 PaymentMethod_Mailed check 1.967082\n"
+ ]
+ }
+ ],
+ "source": [
+ "# the independent variables set \n",
+ "vif_test = data_enc.drop(\"Churn\", axis=1)\n",
+ " \n",
+ "# VIF dataframe \n",
+ "vif_data = pd.DataFrame() \n",
+ "vif_data[\"feature\"] = vif_test.columns \n",
+ " \n",
+ "# calculating VIF for each feature \n",
+ "vif_data[\"VIF\"] = [variance_inflation_factor(vif_test.values, i) \n",
+ " for i in range(len(vif_test.columns))] \n",
+ " \n",
+ "print(vif_data)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Keine der Variablen hat jetzt einen VIF von mehr als 10."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 3.4. Merkmalsskalierung"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 185,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# Separate target variable and predictors\n",
+ "y = data_enc[\"Churn\"]\n",
+ "X = data_enc.drop(labels = [\"Churn\"], axis = 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 186,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
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+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>SeniorCitizen</th>\n",
+ " <th>Partner</th>\n",
+ " <th>Dependents</th>\n",
+ " <th>tenure</th>\n",
+ " <th>PhoneService</th>\n",
+ " <th>PaperlessBilling</th>\n",
+ " <th>gender_Male</th>\n",
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+ " <th>InternetService_No</th>\n",
+ " <th>...</th>\n",
+ " <th>OnlineBackup_Yes</th>\n",
+ " <th>DeviceProtection_Yes</th>\n",
+ " <th>TechSupport_Yes</th>\n",
+ " <th>StreamingTV_Yes</th>\n",
+ " <th>StreamingMovies_Yes</th>\n",
+ " <th>Contract_One year</th>\n",
+ " <th>Contract_Two year</th>\n",
+ " <th>PaymentMethod_Credit card (automatic)</th>\n",
+ " <th>PaymentMethod_Electronic check</th>\n",
+ " <th>PaymentMethod_Mailed check</th>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.239504</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0.512486</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>-1.239504</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows \u00d7 21 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " SeniorCitizen Partner Dependents tenure PhoneService \\\n",
+ "0 0 1 0 -1.280248 0 \n",
+ "1 0 0 0 0.064303 1 \n",
+ "2 0 0 0 -1.239504 1 \n",
+ "3 0 0 0 0.512486 0 \n",
+ "4 0 0 0 -1.239504 1 \n",
+ "\n",
+ " PaperlessBilling gender_Male MultipleLines_Yes \\\n",
+ "0 1 0 0 \n",
+ "1 0 1 0 \n",
+ "2 1 1 0 \n",
+ "3 0 1 0 \n",
+ "4 1 0 0 \n",
+ "\n",
+ " InternetService_Fiber optic InternetService_No ... OnlineBackup_Yes \\\n",
+ "0 0 0 ... 1 \n",
+ "1 0 0 ... 0 \n",
+ "2 0 0 ... 1 \n",
+ "3 0 0 ... 0 \n",
+ "4 1 0 ... 0 \n",
+ "\n",
+ " DeviceProtection_Yes TechSupport_Yes StreamingTV_Yes \\\n",
+ "0 0 0 0 \n",
+ "1 1 0 0 \n",
+ "2 0 0 0 \n",
+ "3 1 1 0 \n",
+ "4 0 0 0 \n",
+ "\n",
+ " StreamingMovies_Yes Contract_One year Contract_Two year \\\n",
+ "0 0 0 0 \n",
+ "1 0 1 0 \n",
+ "2 0 0 0 \n",
+ "3 0 1 0 \n",
+ "4 0 0 0 \n",
+ "\n",
+ " PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check \\\n",
+ "0 0 1 \n",
+ "1 0 0 \n",
+ "2 0 0 \n",
+ "3 0 0 \n",
+ "4 0 1 \n",
+ "\n",
+ " PaymentMethod_Mailed check \n",
+ "0 0 \n",
+ "1 1 \n",
+ "2 1 \n",
+ "3 0 \n",
+ "4 0 \n",
+ "\n",
+ "[5 rows x 21 columns]"
+ ]
+ },
+ "execution_count": 186,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Scaling the variables\n",
+ "num_features = ['tenure']\n",
+ "\n",
+ "scaler = StandardScaler()\n",
+ "\n",
+ "X[num_features] = scaler.fit_transform(X[num_features])\n",
+ "X.head()"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 3.5. Undersampling "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 187,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "iht = InstanceHardnessThreshold(random_state=0,estimator=LogisticRegression (solver='lbfgs', multi_class='auto'))\n",
+ " \n",
+ "X_resampled, y_resampled = iht.fit_resample(X, y)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 3.6. Erstellen von Test- & Trainingsdaten"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 188,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# Split dataset in train and test datasets\n",
+ "# The default value of 80% to 20% is used.\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, random_state=110)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenmodell"
+ },
+ "source": [
+ "# 4. Modellierung und Auswertung"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 4.1. Logistische Regression"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenmodell"
+ },
+ "source": [
+ "Zur L\u00f6sung des Problems wird die logistische Regression verwendet. Hierf\u00fcr werden die beiden Bibliotheken Statsmodels und Scikit-Learn verwendet. Die komplette Auswertung des Modells findet erst im Unterkapitel zu Scikit-Learn statt."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenmodell"
+ },
+ "source": [
+ "## Statistische Modelle"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenmodell"
+ },
+ "source": [
+ "### Training und Vorhersage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 189,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Optimization terminated successfully.\n",
+ " Current function value: 0.082006\n",
+ " Iterations 11\n",
+ " Logit Regression Results \n",
+ "==============================================================================\n",
+ "Dep. Variable: Churn No. Observations: 2803\n",
+ "Model: Logit Df Residuals: 2781\n",
+ "Method: MLE Df Model: 21\n",
+ "Date: Thu, 21 Oct 2021 Pseudo R-squ.: 0.8817\n",
+ "Time: 15:00:28 Log-Likelihood: -229.86\n",
+ "converged: True LL-Null: -1942.4\n",
+ "Covariance Type: nonrobust LLR p-value: 0.000\n",
+ "=========================================================================================================\n",
+ " coef std err z P>|z| [0.025 0.975]\n",
+ "---------------------------------------------------------------------------------------------------------\n",
+ "const 5.1912 0.828 6.266 0.000 3.567 6.815\n",
+ "SeniorCitizen 0.4609 0.457 1.008 0.313 -0.435 1.357\n",
+ "Partner -0.4112 0.302 -1.362 0.173 -1.003 0.181\n",
+ "Dependents -0.5746 0.294 -1.952 0.051 -1.151 0.002\n",
+ "tenure -2.9281 0.309 -9.468 0.000 -3.534 -2.322\n",
+ "PhoneService -1.2307 0.544 -2.261 0.024 -2.298 -0.164\n",
+ "PaperlessBilling 1.2621 0.288 4.385 0.000 0.698 1.826\n",
+ "gender_Male -0.1334 0.255 -0.524 0.600 -0.633 0.366\n",
+ "MultipleLines_Yes 1.0865 0.336 3.231 0.001 0.427 1.746\n",
+ "InternetService_Fiber optic 3.1681 0.400 7.916 0.000 2.384 3.952\n",
+ "InternetService_No -2.8314 0.567 -4.992 0.000 -3.943 -1.720\n",
+ "OnlineSecurity_Yes -1.7901 0.321 -5.581 0.000 -2.419 -1.161\n",
+ "OnlineBackup_Yes -0.3203 0.309 -1.036 0.300 -0.926 0.286\n",
+ "DeviceProtection_Yes 0.4336 0.331 1.312 0.190 -0.214 1.082\n",
+ "TechSupport_Yes -0.8710 0.329 -2.648 0.008 -1.516 -0.226\n",
+ "StreamingTV_Yes 1.1971 0.351 3.414 0.001 0.510 1.884\n",
+ "StreamingMovies_Yes 1.4263 0.374 3.815 0.000 0.693 2.159\n",
+ "Contract_One year -3.5720 0.488 -7.317 0.000 -4.529 -2.615\n",
+ "Contract_Two year -6.5206 0.584 -11.164 0.000 -7.665 -5.376\n",
+ "PaymentMethod_Credit card (automatic) -0.0720 0.313 -0.230 0.818 -0.686 0.542\n",
+ "PaymentMethod_Electronic check 1.2794 0.406 3.154 0.002 0.484 2.075\n",
+ "PaymentMethod_Mailed check -0.3240 0.398 -0.813 0.416 -1.105 0.457\n",
+ "=========================================================================================================\n",
+ "\n",
+ "Possibly complete quasi-separation: A fraction 0.37 of observations can be\n",
+ "perfectly predicted. This might indicate that there is complete\n",
+ "quasi-separation. In this case some parameters will not be identified.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# add constant\n",
+ "X_const = sm.add_constant(X_train)\n",
+ "# create model\n",
+ "log_reg = sm.Logit(y_train, X_const).fit() \n",
+ "print(log_reg.summary())"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenmodell"
+ },
+ "source": [
+ "Das trainierte Modell zeigt statistisch nicht-signifikante Variablen an. Dies ist gegeben, wenn der Wert P>|z| gr\u00f6\u00dfer als 0,05 ist und es sich nicht um die Konstante handelt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 190,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# Removing the statistically non-significant features (P>|z|> 0.05)\n",
+ "insignificant_features = [\"Partner\", \"gender_Male\", \"OnlineBackup_Yes\", \"DeviceProtection_Yes\", \"PaymentMethod_Credit card (automatic)\",\"PaymentMethod_Mailed check\"]\n",
+ "X_train.drop(insignificant_features, axis=1, inplace=True)\n",
+ "X_test.drop(insignificant_features, axis=1, inplace=True)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Datenmodell"
+ },
+ "source": [
+ "Nun kann ein zweites Modell erstellt werden:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 191,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Optimization terminated successfully.\n",
+ " Current function value: 0.083077\n",
+ " Iterations 11\n",
+ " Logit Regression Results \n",
+ "==============================================================================\n",
+ "Dep. Variable: Churn No. Observations: 2803\n",
+ "Model: Logit Df Residuals: 2787\n",
+ "Method: MLE Df Model: 15\n",
+ "Date: Thu, 21 Oct 2021 Pseudo R-squ.: 0.8801\n",
+ "Time: 15:00:28 Log-Likelihood: -232.87\n",
+ "converged: True LL-Null: -1942.4\n",
+ "Covariance Type: nonrobust LLR p-value: 0.000\n",
+ "==================================================================================================\n",
+ " coef std err z P>|z| [0.025 0.975]\n",
+ "--------------------------------------------------------------------------------------------------\n",
+ "const 4.7119 0.718 6.566 0.000 3.305 6.118\n",
+ "SeniorCitizen 0.3954 0.458 0.864 0.387 -0.501 1.292\n",
+ "Dependents -0.7328 0.262 -2.797 0.005 -1.246 -0.219\n",
+ "tenure -2.9242 0.297 -9.845 0.000 -3.506 -2.342\n",
+ "PhoneService -1.2073 0.540 -2.235 0.025 -2.266 -0.149\n",
+ "PaperlessBilling 1.2161 0.285 4.273 0.000 0.658 1.774\n",
+ "MultipleLines_Yes 1.0989 0.331 3.320 0.001 0.450 1.748\n",
+ "InternetService_Fiber optic 3.1159 0.391 7.966 0.000 2.349 3.883\n",
+ "InternetService_No -2.8462 0.529 -5.381 0.000 -3.883 -1.809\n",
+ "OnlineSecurity_Yes -1.7441 0.313 -5.576 0.000 -2.357 -1.131\n",
+ "TechSupport_Yes -0.8357 0.325 -2.569 0.010 -1.473 -0.198\n",
+ "StreamingTV_Yes 1.2193 0.348 3.508 0.000 0.538 1.901\n",
+ "StreamingMovies_Yes 1.4394 0.368 3.908 0.000 0.717 2.161\n",
+ "Contract_One year -3.4572 0.471 -7.337 0.000 -4.381 -2.534\n",
+ "Contract_Two year -6.3299 0.557 -11.372 0.000 -7.421 -5.239\n",
+ "PaymentMethod_Electronic check 1.3103 0.362 3.623 0.000 0.601 2.019\n",
+ "==================================================================================================\n",
+ "\n",
+ "Possibly complete quasi-separation: A fraction 0.36 of observations can be\n",
+ "perfectly predicted. This might indicate that there is complete\n",
+ "quasi-separation. In this case some parameters will not be identified.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# new model\n",
+ "X_const = sm.add_constant(X_train)\n",
+ "log_reg2 = sm.Logit(y_train, X_const).fit() \n",
+ "print(log_reg2.summary())"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Keine statistisch nicht signifikanten Variablen mehr. Das endg\u00fcltige Modell wurde modelliert:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 192,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Optimization terminated successfully.\n",
+ " Current function value: 0.083077\n",
+ " Iterations 11\n",
+ " Logit Regression Results \n",
+ "==============================================================================\n",
+ "Dep. Variable: Churn No. Observations: 2803\n",
+ "Model: Logit Df Residuals: 2787\n",
+ "Method: MLE Df Model: 15\n",
+ "Date: Thu, 21 Oct 2021 Pseudo R-squ.: 0.8801\n",
+ "Time: 15:00:28 Log-Likelihood: -232.87\n",
+ "converged: True LL-Null: -1942.4\n",
+ "Covariance Type: nonrobust LLR p-value: 0.000\n",
+ "==================================================================================================\n",
+ " coef std err z P>|z| [0.025 0.975]\n",
+ "--------------------------------------------------------------------------------------------------\n",
+ "const 4.7119 0.718 6.566 0.000 3.305 6.118\n",
+ "SeniorCitizen 0.3954 0.458 0.864 0.387 -0.501 1.292\n",
+ "Dependents -0.7328 0.262 -2.797 0.005 -1.246 -0.219\n",
+ "tenure -2.9242 0.297 -9.845 0.000 -3.506 -2.342\n",
+ "PhoneService -1.2073 0.540 -2.235 0.025 -2.266 -0.149\n",
+ "PaperlessBilling 1.2161 0.285 4.273 0.000 0.658 1.774\n",
+ "MultipleLines_Yes 1.0989 0.331 3.320 0.001 0.450 1.748\n",
+ "InternetService_Fiber optic 3.1159 0.391 7.966 0.000 2.349 3.883\n",
+ "InternetService_No -2.8462 0.529 -5.381 0.000 -3.883 -1.809\n",
+ "OnlineSecurity_Yes -1.7441 0.313 -5.576 0.000 -2.357 -1.131\n",
+ "TechSupport_Yes -0.8357 0.325 -2.569 0.010 -1.473 -0.198\n",
+ "StreamingTV_Yes 1.2193 0.348 3.508 0.000 0.538 1.901\n",
+ "StreamingMovies_Yes 1.4394 0.368 3.908 0.000 0.717 2.161\n",
+ "Contract_One year -3.4572 0.471 -7.337 0.000 -4.381 -2.534\n",
+ "Contract_Two year -6.3299 0.557 -11.372 0.000 -7.421 -5.239\n",
+ "PaymentMethod_Electronic check 1.3103 0.362 3.623 0.000 0.601 2.019\n",
+ "==================================================================================================\n",
+ "\n",
+ "Possibly complete quasi-separation: A fraction 0.36 of observations can be\n",
+ "perfectly predicted. This might indicate that there is complete\n",
+ "quasi-separation. In this case some parameters will not be identified.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# final model\n",
+ "X_const = sm.add_constant(X_train)\n",
+ "log_reg_final = sm.Logit(y_train, X_const).fit() \n",
+ "print(log_reg_final.summary())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 193,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# prediction\n",
+ "y_hat = log_reg_final.predict(sm.add_constant(X_test)) \n",
+ "# Statsmodel only gives the probabilities, therefore rounding is required. \n",
+ "prediction = list(map(round, y_hat))"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 4.1. Auswertung"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Zur Evaluation sollen mehrere Metriken verwendet werden, die komfortabler mittels Scikit-Learn erzeugt werden k\u00f6nnen. Deshalb wird das identische Modell wie mit Statsmodels nochmals in Scikit-Learn erzeugt."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## Scikit-Learn"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "### Training und Vorhersage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 194,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# C is needed to build the exact same model as with Statsmodels; source: https://www.kdnuggets.com/2016/06/regularization-logistic-regression.html\n",
+ "logistic_model = LogisticRegression(random_state=0, C=1e8)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 195,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# prediction with testdata\n",
+ "result = logistic_model.fit(X_train,y_train)\n",
+ "prediction_test = logistic_model.predict(X_test)\n",
+ "prediction_train = logistic_model.predict(X_train)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Evaluation"
+ },
+ "source": [
+ "### Evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 196,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy with testdata: 0.9882352941176471\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Accuracy Score \n",
+ "acc = metrics.accuracy_score(y_test, prediction_test)\n",
+ "print('Accuracy with testdata: {}'.format(acc))"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Evaluation"
+ },
+ "source": [
+ "Die Genauigkeit deutet auf ein \u00fcberdurchschnittliches Modell hin. Allerdings handelt es sich um einen unausgewogenen Datensatz. Daher m\u00fcssen weitere Metriken analysiert werden.\n",
+ "\t\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 197,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "traindata:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.96 1.00 0.98 1374\n",
+ " 1 1.00 0.96 0.98 1429\n",
+ "\n",
+ " accuracy 0.98 2803\n",
+ " macro avg 0.98 0.98 0.98 2803\n",
+ "weighted avg 0.98 0.98 0.98 2803\n",
+ "\n",
+ "testdata:\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.98 1.00 0.99 495\n",
+ " 1 1.00 0.98 0.99 440\n",
+ "\n",
+ " accuracy 0.99 935\n",
+ " macro avg 0.99 0.99 0.99 935\n",
+ "weighted avg 0.99 0.99 0.99 935\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# classification report\n",
+ "\n",
+ "print(\"traindata:\")\n",
+ "print(classification_report(y_train,prediction_train))\n",
+ "print(\"testdata:\")\n",
+ "print(classification_report(y_test,prediction_test))"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "H\u00f6here Genauigkeit f\u00fcr das Training als f\u00fcr den Testdatensatz. Insgesamt sind die Werte f\u00fcr den Test- und den Trainingsdatensatz sehr \u00e4hnlich. Daher sollte nicht von einem Overfitting oder Underfitting ausgegangen werden."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 198,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 39.5, 'Predicted label')"
+ ]
+ },
+ "execution_count": 198,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 720x504 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Confusion matrix testdata\n",
+ "cm = confusion_matrix(y_test,prediction_test)\n",
+ "df_cm = pd.DataFrame(cm, index=['No Churn','Churn'], columns=['No Churn', 'Churn'],)\n",
+ "fig = plt.figure(figsize=[10,7])\n",
+ "heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
+ "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=14)\n",
+ "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=14)\n",
+ "plt.ylabel('True label')\n",
+ "plt.xlabel('Predicted label')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 199,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "True Negatives: 493\n",
+ "False Positives: 2\n",
+ "False Negatives: 9\n",
+ "True Positives: 431\n",
+ "Recall: 0.9795454545454545\n",
+ "Precision: 0.9953810623556582\n"
+ ]
+ }
+ ],
+ "source": [
+ "# metrics from confusion matrix\n",
+ "tn, fp, fn, tp = cm.ravel()\n",
+ "recall = tp/(fn+tp)\n",
+ "precision = tp/(tp+fp)\n",
+ "print(\"True Negatives: \" + str(tn))\n",
+ "print(\"False Positives: \" + str(fp))\n",
+ "print(\"False Negatives: \" + str(fn))\n",
+ "print(\"True Positives: \" + str(tp))\n",
+ "print(\"Recall: \" + str(recall))\n",
+ "print(\"Precision: \" + str(precision))"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Pr\u00e4zision und Recall vermitteln ein viel realistischeres Bild des Modells. Es erreicht eine Pr\u00e4zision von rund 68 % und eine Wiederauffindbarkeit von 52 %. Der Recall ist f\u00fcr den Anwendungsfall eindeutig wichtiger und muss daher auf Kosten der Pr\u00e4zision verbessert werden."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 200,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 576x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# ROC-Kurve, AUC\n",
+ "fig, ax = plt.subplots(figsize=(8,6))\n",
+ "ax.set_title('ROC Kurve')\n",
+ "plot = metrics.plot_roc_curve(logistic_model, X_test, y_test, ax=ax);\n",
+ "ax.plot([0,1], [0,1], '--');"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Der AUC der ROC-Kurve ergibt einen guten Wert von 0,84. Daraus l\u00e4sst sich schlie\u00dfen, dass durch die Optimierung des Schwellenwertes Optimierungspotenzial besteht."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 4.3. Interpretation"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Zun\u00e4chst sollen jedoch die Ergebnisse f\u00fcr das Unternehmen veranschaulicht werden und es soll gekl\u00e4rt werden, welche Kunden zur Abwanderung f\u00fchren und welche gegen eine Abwanderung sprechen."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 201,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "InternetService_Fiber optic 3.115901\n",
+ "StreamingMovies_Yes 1.439381\n",
+ "PaymentMethod_Electronic check 1.310265\n",
+ "StreamingTV_Yes 1.219198\n",
+ "PaperlessBilling 1.216093\n",
+ "MultipleLines_Yes 1.098867\n",
+ "SeniorCitizen 0.395488\n",
+ "Dependents -0.732812\n",
+ "TechSupport_Yes -0.835712\n",
+ "PhoneService -1.207319\n",
+ "OnlineSecurity_Yes -1.744166\n",
+ "InternetService_No -2.846463\n",
+ "tenure -2.924275\n",
+ "Contract_One year -3.457173\n",
+ "Contract_Two year -6.329852\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 201,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Read out regression coefficients and thus find out importance of individual attributes\n",
+ "weights = pd.Series(logistic_model.coef_[0],\n",
+ " index=X_train.columns.values)\n",
+ "weights.sort_values(ascending = False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 202,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AxesSubplot(0.125,0.125;0.775x0.755)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Graphical representation of key features that lead to churn.\n",
+ "weights = pd.Series(logistic_model.coef_[0],\n",
+ " index=X_train.columns.values)\n",
+ "print (weights.sort_values(ascending = False)[:7].plot(kind='bar'))"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Die drei Hauptmerkmale, die zur Abwanderung f\u00fchren, sind:\n",
+ "\n",
+ "- Der Glasfaserdienst (InternetService_Glasfaser),\n",
+ "- Die Online-Zahlungen (PaperlessBilling) und\n",
+ "- Das Abonnement des zus\u00e4tzlichen Filmstreamingdienstes (StreamingMovies_Yes).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 203,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AxesSubplot(0.125,0.125;0.775x0.755)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Most important features that keep customers from churning\n",
+ "print(weights.sort_values(ascending = False)[-8:].plot(kind='bar'))"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Die drei wichtigsten Merkmale, die Kunden von der Abwanderung abhalten, sind:\n",
+ "\n",
+ "- Die Vertr\u00e4ge, die f\u00fcr zwei Jahre gek\u00fcndigt werden k\u00f6nnen (Contract_Two year),\n",
+ "- Die Zeit, die man Kunde eines Unternehmens ist (Tenure) und\n",
+ "- kein Abonnement f\u00fcr den Internetdienst (InternetService_No).\n"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 4.4. Modell-Optimierung"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Die Recall-Rate ist als Zielmetrik zu niedrig und muss daher erh\u00f6ht werden. Daher werden die Metriken bei verschiedenen Schwellenwerten der logistischen Regression analysiert."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 204,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "******** For a Threshold about 0.05 ******\n",
+ "Accuracy: 0.8588235294117647\n",
+ "True Negatives: 367\n",
+ "False Positives: 128\n",
+ "False Negatives: 4\n",
+ "True Positives: 436\n",
+ "Recall: 0.990909090909091\n",
+ "Precision: 0.7730496453900709\n",
+ "\n",
+ "******** For a Threshold about 0.1 ******\n",
+ "Accuracy: 0.9144385026737968\n",
+ "True Negatives: 420\n",
+ "False Positives: 75\n",
+ "False Negatives: 5\n",
+ "True Positives: 435\n",
+ "Recall: 0.9886363636363636\n",
+ "Precision: 0.8529411764705882\n",
+ "\n",
+ "******** For a Threshold about 0.15 ******\n",
+ "Accuracy: 0.9422459893048128\n",
+ "True Negatives: 446\n",
+ "False Positives: 49\n",
+ "False Negatives: 5\n",
+ "True Positives: 435\n",
+ "Recall: 0.9886363636363636\n",
+ "Precision: 0.8987603305785123\n",
+ "\n",
+ "******** For a Threshold about 0.2 ******\n",
+ "Accuracy: 0.9657754010695188\n",
+ "True Negatives: 468\n",
+ "False Positives: 27\n",
+ "False Negatives: 5\n",
+ "True Positives: 435\n",
+ "Recall: 0.9886363636363636\n",
+ "Precision: 0.9415584415584416\n",
+ "\n",
+ "******** For a Threshold about 0.25 ******\n",
+ "Accuracy: 0.9786096256684492\n",
+ "True Negatives: 481\n",
+ "False Positives: 14\n",
+ "False Negatives: 6\n",
+ "True Positives: 434\n",
+ "Recall: 0.9863636363636363\n",
+ "Precision: 0.96875\n",
+ "\n",
+ "******** For a Threshold about 0.3 ******\n",
+ "Accuracy: 0.9818181818181818\n",
+ "True Negatives: 486\n",
+ "False Positives: 9\n",
+ "False Negatives: 8\n",
+ "True Positives: 432\n",
+ "Recall: 0.9818181818181818\n",
+ "Precision: 0.9795918367346939\n",
+ "\n",
+ "******** For a Threshold about 0.35 ******\n",
+ "Accuracy: 0.986096256684492\n",
+ "True Negatives: 490\n",
+ "False Positives: 5\n",
+ "False Negatives: 8\n",
+ "True Positives: 432\n",
+ "Recall: 0.9818181818181818\n",
+ "Precision: 0.988558352402746\n",
+ "\n",
+ "******** For a Threshold about 0.4 ******\n",
+ "Accuracy: 0.9871657754010695\n",
+ "True Negatives: 491\n",
+ "False Positives: 4\n",
+ "False Negatives: 8\n",
+ "True Positives: 432\n",
+ "Recall: 0.9818181818181818\n",
+ "Precision: 0.9908256880733946\n",
+ "\n",
+ "******** For a Threshold about 0.45 ******\n",
+ "Accuracy: 0.9893048128342246\n",
+ "True Negatives: 493\n",
+ "False Positives: 2\n",
+ "False Negatives: 8\n",
+ "True Positives: 432\n",
+ "Recall: 0.9818181818181818\n",
+ "Precision: 0.9953917050691244\n",
+ "\n",
+ "******** For a Threshold about 0.5 ******\n",
+ "Accuracy: 0.9882352941176471\n",
+ "True Negatives: 493\n",
+ "False Positives: 2\n",
+ "False Negatives: 9\n",
+ "True Positives: 431\n",
+ "Recall: 0.9795454545454545\n",
+ "Precision: 0.9953810623556582\n",
+ "\n",
+ "******** For a Threshold about 0.55 ******\n",
+ "Accuracy: 0.9882352941176471\n",
+ "True Negatives: 493\n",
+ "False Positives: 2\n",
+ "False Negatives: 9\n",
+ "True Positives: 431\n",
+ "Recall: 0.9795454545454545\n",
+ "Precision: 0.9953810623556582\n",
+ "\n",
+ "******** For a Threshold about 0.6 ******\n",
+ "Accuracy: 0.9893048128342246\n",
+ "True Negatives: 494\n",
+ "False Positives: 1\n",
+ "False Negatives: 9\n",
+ "True Positives: 431\n",
+ "Recall: 0.9795454545454545\n",
+ "Precision: 0.9976851851851852\n",
+ "\n",
+ "******** For a Threshold about 0.65 ******\n",
+ "Accuracy: 0.9893048128342246\n",
+ "True Negatives: 494\n",
+ "False Positives: 1\n",
+ "False Negatives: 9\n",
+ "True Positives: 431\n",
+ "Recall: 0.9795454545454545\n",
+ "Precision: 0.9976851851851852\n",
+ "\n",
+ "******** For a Threshold about 0.7 ******\n",
+ "Accuracy: 0.9903743315508021\n",
+ "True Negatives: 495\n",
+ "False Positives: 0\n",
+ "False Negatives: 9\n",
+ "True Positives: 431\n",
+ "Recall: 0.9795454545454545\n",
+ "Precision: 1.0\n",
+ "\n",
+ "******** For a Threshold about 0.75 ******\n",
+ "Accuracy: 0.9903743315508021\n",
+ "True Negatives: 495\n",
+ "False Positives: 0\n",
+ "False Negatives: 9\n",
+ "True Positives: 431\n",
+ "Recall: 0.9795454545454545\n",
+ "Precision: 1.0\n",
+ "\n",
+ "******** For a Threshold about 0.8 ******\n",
+ "Accuracy: 0.9893048128342246\n",
+ "True Negatives: 495\n",
+ "False Positives: 0\n",
+ "False Negatives: 10\n",
+ "True Positives: 430\n",
+ "Recall: 0.9772727272727273\n",
+ "Precision: 1.0\n",
+ "\n",
+ "******** For a Threshold about 0.85 ******\n",
+ "Accuracy: 0.9882352941176471\n",
+ "True Negatives: 495\n",
+ "False Positives: 0\n",
+ "False Negatives: 11\n",
+ "True Positives: 429\n",
+ "Recall: 0.975\n",
+ "Precision: 1.0\n",
+ "\n",
+ "******** For a Threshold about 0.9 ******\n",
+ "Accuracy: 0.9871657754010695\n",
+ "True Negatives: 495\n",
+ "False Positives: 0\n",
+ "False Negatives: 12\n",
+ "True Positives: 428\n",
+ "Recall: 0.9727272727272728\n",
+ "Precision: 1.0\n",
+ "\n",
+ "******** For a Threshold about 0.95 ******\n",
+ "Accuracy: 0.9807486631016042\n",
+ "True Negatives: 495\n",
+ "False Positives: 0\n",
+ "False Negatives: 18\n",
+ "True Positives: 422\n",
+ "Recall: 0.9590909090909091\n",
+ "Precision: 1.0\n",
+ "\n",
+ "******** For a Threshold about 0.99 ******\n",
+ "Accuracy: 0.9497326203208556\n",
+ "True Negatives: 495\n",
+ "False Positives: 0\n",
+ "False Negatives: 47\n",
+ "True Positives: 393\n",
+ "Recall: 0.8931818181818182\n",
+ "Precision: 1.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Testing the metrics at different thresholds\n",
+ "threshold_list = [0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,.7,.75,.8,.85,.9,.95,.99]\n",
+ "pred_proba_df = y_hat\n",
+ "for i in threshold_list:\n",
+ " print ('\\n******** For a Threshold about {} ******'.format(i))\n",
+ " # Round up if value is above threshold\n",
+ " y_test_pred = pred_proba_df.apply(lambda x: 1 if x>i else 0)\n",
+ " # read metrics\n",
+ " test_accuracy = metrics.accuracy_score(y_test, y_test_pred)\n",
+ " print(\"Accuracy: {}\".format(test_accuracy))\n",
+ " # Confusion matrix\n",
+ " c = confusion_matrix(y_test, y_test_pred)\n",
+ " tn, fp, fn, tp = c.ravel()\n",
+ " recall = tp/(fn+tp)\n",
+ " precision = tp/(tp+fp)\n",
+ " # print metrics\n",
+ " print(\"True Negatives: \" + str(tn))\n",
+ " print(\"False Positives: \" + str(fp))\n",
+ " print(\"False Negatives: \" + str(fn))\n",
+ " print(\"True Positives: \" + str(tp))\n",
+ " print(\"Recall: \" + str(recall))\n",
+ " print(\"Precision: \" + str(precision))"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Ein Schwellenwert von 0,3 bietet ein besseres Ergebnis f\u00fcr die Anwendung. Er erh\u00f6ht die Wiederauffindbarkeit auf ein zufriedenstellendes Niveau von 73,21 %, was zu Lasten der Pr\u00e4zision geht. Die Pr\u00e4zision ist jedoch vernachl\u00e4ssigbar."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Daraus ergeben sich die folgenden Werte:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 205,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.98 0.98 0.98 495\n",
+ " 1 0.98 0.98 0.98 440\n",
+ "\n",
+ " accuracy 0.98 935\n",
+ " macro avg 0.98 0.98 0.98 935\n",
+ "weighted avg 0.98 0.98 0.98 935\n",
+ "\n",
+ "Confusion matrix for the new threshold:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 720x504 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " \n",
+ "Metrics for the new threshold:\n",
+ "Accuracy: 0.9818181818181818\n",
+ "True Negatives: 486\n",
+ "False Positives: 9\n",
+ "False Negatives: 8\n",
+ "True Positives: 432\n",
+ "Recall: 0.9818181818181818\n",
+ "Precision: 0.9795918367346939\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Threshold about 0,3\n",
+ "y_test_pred = pred_proba_df.apply(lambda x: 1 if x>0.30 else 0)\n",
+ "test_accuracy = metrics.accuracy_score(y_test, y_test_pred)\n",
+ "c = confusion_matrix(y_test, y_test_pred)\n",
+ "# read values from confusion matrix\n",
+ "tn, fp, fn, tp = c.ravel()\n",
+ "recall = tp/(fn+tp)\n",
+ "precision = tp/(tp+fp)\n",
+ "print(classification_report(y_test,y_test_pred))\n",
+ "# create confusion matrix\n",
+ "print(\"Confusion matrix for the new threshold:\")\n",
+ "df_cm = pd.DataFrame(c, index=['No Churn','Churn'], columns=['No Churn', 'Churn'],)\n",
+ "fig = plt.figure(figsize=[10,7])\n",
+ "heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
+ "heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=14)\n",
+ "heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=14)\n",
+ "plt.ylabel('True label')\n",
+ "plt.xlabel('Predicted label')\n",
+ "plt.show()\n",
+ "print(\" \")\n",
+ "# print metrics\n",
+ "print(\"Metrics for the new threshold:\")\n",
+ "print(\"Accuracy: {}\".format(test_accuracy))\n",
+ "print(\"True Negatives: \" + str(tn))\n",
+ "print(\"False Positives: \" + str(fp))\n",
+ "print(\"False Negatives: \" + str(fn))\n",
+ "print(\"True Positives: \" + str(tp))\n",
+ "print(\"Recall: \" + str(recall))\n",
+ "print(\"Precision: \" + str(precision))"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Evaluation"
+ },
+ "source": [
+ "Erwartungsgem\u00e4\u00df steigt die Rate der f\u00e4lschlicherweise als abgewandert eingestuften Kunden. Im Gegenzug steigt aber auch die Anzahl der Kunden, die korrekt als Abwanderer vorhergesagt werden (True Positives). Wie in der Hausarbeit ausgef\u00fchrt, ist dies essentiell, denn im Zweifelsfall w\u00fcrde ein Kunde f\u00e4lschlicherweise vom Serviceteam angerufen werden und diesen Anruf sogar als guten Service wahrnehmen und l\u00e4ngerfristig an das Unternehmen binden."
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false,
+ "paragraph": "Umsetzung"
+ },
+ "source": [
+ "# 5. Deployment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 206,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# Separate individual (scaled) customer\n",
+ "customer_df = X_test.iloc[896]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 207,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "SeniorCitizen 0.000000\n",
+ "Dependents 0.000000\n",
+ "tenure -0.302393\n",
+ "PhoneService 1.000000\n",
+ "PaperlessBilling 0.000000\n",
+ "MultipleLines_Yes 0.000000\n",
+ "InternetService_Fiber optic 0.000000\n",
+ "InternetService_No 1.000000\n",
+ "OnlineSecurity_Yes 0.000000\n",
+ "TechSupport_Yes 0.000000\n",
+ "StreamingTV_Yes 0.000000\n",
+ "StreamingMovies_Yes 0.000000\n",
+ "Contract_One year 0.000000\n",
+ "Contract_Two year 1.000000\n",
+ "PaymentMethod_Electronic check 0.000000\n",
+ "Name: 1544, dtype: float64"
+ ]
+ },
+ "execution_count": 207,
+ "metadata": {
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Overview about the customer\n",
+ "customer_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 208,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# execute prediction\n",
+ "cust_pred = logistic_model.predict([customer_df])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 209,
+ "metadata": {
+ },
+ "outputs": [
+ ],
+ "source": [
+ "# evaluate results\n",
+ "def check_prediction(pred):\n",
+ " if pred[0] == 1:\n",
+ " print(\"The customer will probably churn! Inform Customer Relationship Management!\")\n",
+ " else:\n",
+ " print(\"The customer probably will not churn.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 210,
+ "metadata": {
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The customer probably will not churn.\n"
+ ]
+ }
+ ],
+ "source": [
+ "check_prediction(cust_pred)"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "# Zusammenfassung"
+ ]
+ },
+ {
+ "attachments": {
+ },
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "Das Notebook hat gezeigt, wie eine logistische Regression verwendet werden kann, um die Abwanderung von Kunden im Telekommunikationssegment vorherzusagen."
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/Marketing/Predicting clicks on online advertising by Facebook/notebook.ipynb b/Marketing/Predicting clicks on online advertising by Facebook/notebook.ipynb
index 0a64acd5752508f261c2b1ba1a0e2dc27b462eed..1bbfbfab9df57f00bd8a849930290eae39ee367f 100644
--- a/Marketing/Predicting clicks on online advertising by Facebook/notebook.ipynb
+++ b/Marketing/Predicting clicks on online advertising by Facebook/notebook.ipynb
@@ -1,56 +1,150 @@
{
+ "metadata": {
+ "branche": "Medien",
+ "dataSource": "https://storage.googleapis.com/ml-service-repository-datastorage/Predicting_clicks_on_online_advertising_by_Facebook_data.csv",
+ "funktion": "Marketing",
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ },
+ "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/blob/main/Marketing/Predicting%20clicks%20on%20online%20advertising%20by%20Facebook/notebook.ipynb?ref_type=heads",
+ "skipNotebookInDeployment": false,
+ "teaser": "In diesem Beispiel auf Basis der demographischen Merkmale eines Nutzers sowie seiner Internetnutzung, prognostiziert ob er auf eine Werbeanzeige klicken wird.",
+ "title": "Vorhersage der Interaktion eines Nutzers mit einer Werbeanzeige"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4,
"cells": [
- {
+ {
"attachments": {},
"cell_type": "markdown",
"metadata": {
"editable": true,
- "include": false,
+ "include": true,
+ "paragraph": "Business",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
- "# Notebook zur Vorhersage von Werbung\n",
- "In diesem Notizbuch wird der Advertising-Datensatz von Kaggle analysiert (https://www.kaggle.com/fayomi/advertising). Er besteht aus 10 Spalten mit insgesamt 1000 Zeilen. Der Anwendungsfall besteht in der Vorhersage, ob ein Website-Besucher auf eine Werbung klicken wird oder nicht, basierend auf seinen demografischen Daten und Daten zur Internetnutzung.\n",
- "Der Ansatz des Notebooks basiert auf dem CRISP-DM-Modell, das die Phasen in einem Data-Science-Projekt klar unterteilt. \n",
- "\n",
- "<img src=\"https://statistik-dresden.de/wp-content/uploads/2012/04/CRISP-DM_Process_Diagram1.png\" alt=\"CRISP-DM Modell\" width=\"300\" height=\"300\">\n",
- "\n",
- "\n"
+ "Für Anbieter einer Werbeplattform ist es wichtig, dass möglichst viele der Nutzer, welchen eine Werbeanzeige ausgespielt wird diese auch anklicken. Erreicht ein Anbieter hier besonders gute Ergebnisse ist er interesanter für portentzielle Kunden und kann höhere Preise verlangen. Darum wird in diesem Beispiel anhand von demographischen Merkmalen eines Nutzers sowie Daten zu seiner Internetnutzung, vorhergesagt ob ein Nutzer mit einer Anzeige interagieren wird oder nicht."
]
},
- {
+ {
"attachments": {},
"cell_type": "markdown",
"metadata": {
"editable": true,
- "include": false,
- "paragraph": "Business",
+ "include": true,
+ "paragraph": "Daten",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
- "## 1. Business Understanding"
+ "Der Datensatz welcher in diesem Beispiel verwendet wird, wurde ursprünglich auf [Kaggle](https://www.kaggle.com/) veröffentlicht, steht dort jedoch nichtmehr zur Verfügung, daher kann er nun vom [Google Cloud Storage](https://storage.googleapis.com/ml-service-repository-datastorage/Predicting_clicks_on_online_advertising_by_Facebook_data.csv) bezogen werden. Jede Zeile des Datensatzes stellt einen Nutzer dar. Die Spalten beinhalten Informationen zum Nutzer in numerischer sowie kategorialer Form. Ein Beispiel für ein numerisches Merkmal ist Zeit welche der Nutzer täglich auf der Seite verbringt oder auch sein Alter, kategoriale Merkmale sind Wohnort und Gechlecht. Zusätzlich sind auch noch Spalten vorhanden welche Merkmale der Werbeanzeige beinhalten nämlich das Thema der Anzeige sowie ob der Nutzer sie angeklickt hat."
]
},
+
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"editable": true,
"include": true,
- "paragraph": "Business",
+ "paragraph": "Datenvorbereitung",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
- "Für Anbieter einer Werbeplattform ist es wichtig, dass möglichst viele der Nutzer, welchen eine Werbeanzeige ausgespielt wird diese auch anklicken. Erreicht ein Anbieter hier besonders gute Ergebnisse ist er interesanter für portentzielle Kunden und kann höhere Preise verlangen. Darum wird in diesem Beispiel anhand von demographischen Merkmalen eines Nutzers sowie Daten zu seiner Internetnutzung, vorhergesagt ob ein Nutzer mit einer Anzeige interagieren wird oder nicht."
+ "Um später ein Modell zur Vorhersage effektiv trainieren zu können muss die Qualität der Daten sichergestellt werden. \n",
+ "Hierzu wird in einem ersten Schritt überprüft ob es Spalten gibt, welche fehlende Werte für Zeilen aufweisen, zusätzlich wird der Datensatz auf Duplikate gechekt. Im nächsten Schritt wird die Ausgewogenheit des Datensatzes überprüft, bei einer Klassifikationsaufgabe wie dieser sollte der Datensatz möglichst die gleiche Anzahl an Einträgen für jede Klassifikationsmöglichkeit enthalten. Dieser Datensatz ist enthält 500 Einträge in welchen die Anzeige angeklickt wurde und 500 Einträge in welchen die Anzeige nicht angeklickt wurde und ist somit schon perfekt ausgewogen. \n",
+ "Nun beginnt die explorative Datenanalyse, in ihr werden die verschiedenen Merkmale in Verhältnis zueinander gesetzt und das Ergebnis visuell dargestellt und eine Korrelationsanalyse durchgeführt. Das dient dazu mögliche Zusammenhänge zwischen der Zielvariable, in unserem Fall \"Anzeige angeklickt\", und den Merkmalen zu finden. \n",
+ "Zusätzlich wird aus dem Timestamp an welchem die Werbeanzeige angezeigt wurde, folgende Merkmale Monat, Tag, Stund sowie Wochentag extrahiert und in neuen Spalten gespeichert. Das Alter wurde in Altersgruppen eingeteilt und in einer neuen Spalte gespeichert. Mit disen neuen Spalten wird nochmal eine explorative Analyse ausgeführt.\n",
+ "Die Ergebnisse dieser Analyse werden verwendet um Merkmale bei welchen sich kein Zusammenhang finden lässt werden aus dem Datensatz entfernt, dieser Schritt wird Dimensionsreduktion genannt. Im Falle dieses Datensatzes betrifft es die Spalten Land, Stadt, Thema der Anzeige, Tag, Monat, Stunde, Wochentag, Alter und Altersgruppe. \n",
+ "Dieser Datensatz wird abschließend auf ein gemeinsames Messniveau skaliert, auch Standardisierung gennant, und in einen Trainings- sowie Testdatensatz aufgeteilt."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "include": true,
+ "paragraph": "Datenmodell"
+ },
+ "source": [
+ "Es werden nun zwei Modelle mit dem erstellten Datensatz gebaut, um sie anschließend miteinander zu vergleichen und das bessere der beiden zu Verwenden. \n",
+ "Modell 1 basiert auf der logistischen Regression, welche bei binären Zielvariablen immer sehr gute Ergebnisse liefert. \n",
+ "Modell 2 basiert auf einem Entscheidungsbaum."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import required libraries for the model creation\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "from sklearn.metrics import roc_auc_score, accuracy_score, confusion_matrix, classification_report"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 7 Evaluation"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "include": true,
+ "paragraph": "Evaluation"
+ },
+ "source": [
+ "Die trainierten Modelle werden nun mit dem Testdatensatz getestet. Es gibt verschiedene Metriken zur Bewertung der Qualität des Modells unteranderem Accuracy (= allgemeine Genauigkeit der Klassifikation), Precision (= Päzision der positiven Vorhersagen) und Recall (= Stellt dar wie viele der tatsächlich positiven Einträge im Testdatensatz als positiv erkannt wurden). \n",
+ "Hier bei erreicht das Lineare-Regressions-Modell eine Accuracy von 97,8%, eine Precision von 100% und einen Recall von 95,41%. Das Entscheidungsbaum-Modell erreicht die folgenden Werte: Accuracy 93,9% , Precision 90.60% , Recall 97.25%. \n",
+ "Da es sich hier nicht um einen Fall handelt bei dem alle Fälle erkannt werden müssen bei der eine Anzeige angeklickt wird, sollte Modell 1 verwendet werden. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "include": false
+ },
+ "source": [
+ "## 8 Deployment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "include": true,
+ "paragraph": "Umsetzung"
+ },
+ "source": [
+ "Es würde sich anbieten dieses Modell beim Ausspielen der Anzeigen zu implementieren, um vor dem Ausspielen einer Anzeige vorherzusagen ob sie geklickt werden würde oder nicht. Hierdurch kann vermieden werden das Nutzer von Werbeanzeigen genervt werden, welche sie sowieso nicht anklicken werden. Dadurch entsteht ein Mehrwert für den Werbekunden sowie für den Nutzer."
]
},
{
@@ -59,14 +153,19 @@
"metadata": {
"editable": true,
"include": false,
- "paragraph": "Daten",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
- "## 2. Daten und Datenverständnis"
+ "# Notebook zur Vorhersage von Werbung\n",
+ "In diesem Notizbuch wird der Advertising-Datensatz von Kaggle analysiert (https://www.kaggle.com/fayomi/advertising). Er besteht aus 10 Spalten mit insgesamt 1000 Zeilen. Der Anwendungsfall besteht in der Vorhersage, ob ein Website-Besucher auf eine Werbung klicken wird oder nicht, basierend auf seinen demografischen Daten und Daten zur Internetnutzung.\n",
+ "Der Ansatz des Notebooks basiert auf dem CRISP-DM-Modell, das die Phasen in einem Data-Science-Projekt klar unterteilt. \n",
+ "\n",
+ "<img src=\"https://statistik-dresden.de/wp-content/uploads/2012/04/CRISP-DM_Process_Diagram1.png\" alt=\"CRISP-DM Modell\" width=\"300\" height=\"300\">\n",
+ "\n",
+ "\n"
]
},
{
@@ -74,15 +173,15 @@
"cell_type": "markdown",
"metadata": {
"editable": true,
- "include": true,
- "paragraph": "Daten",
+ "include": false,
+ "paragraph": "Business",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
- "Der Datensatz welcher in diesem Beispiel verwendet wird, wurde ursprünglich auf [Kaggle](https://www.kaggle.com/) veröffentlicht, steht dort jedoch nichtmehr zur Verfügung, daher kann er nun vom [Google Cloud Storage](https://storage.googleapis.com/ml-service-repository-datastorage/Predicting_clicks_on_online_advertising_by_Facebook_data.csv) bezogen werden. Jede Zeile des Datensatzes stellt einen Nutzer dar. Die Spalten beinhalten Informationen zum Nutzer in numerischer sowie kategorialer Form. Ein Beispiel für ein numerisches Merkmal ist Zeit welche der Nutzer täglich auf der Seite verbringt oder auch sein Alter, kategoriale Merkmale sind Wohnort und Gechlecht. Zusätzlich sind auch noch Spalten vorhanden welche Merkmale der Werbeanzeige beinhalten nämlich das Thema der Anzeige sowie ob der Nutzer sie angeklickt hat."
+ "## 1. Business Understanding"
]
},
{
@@ -90,20 +189,15 @@
"cell_type": "markdown",
"metadata": {
"editable": true,
- "include": true,
- "paragraph": "Datenvorbereitung",
+ "include": false,
+ "paragraph": "Daten",
"slideshow": {
"slide_type": ""
},
"tags": []
},
"source": [
- "Um später ein Modell zur Vorhersage effektiv trainieren zu können muss die Qualität der Daten sichergestellt werden. \n",
- "Hierzu wird in einem ersten Schritt überprüft ob es Spalten gibt, welche fehlende Werte für Zeilen aufweisen, zusätzlich wird der Datensatz auf Duplikate gechekt. Im nächsten Schritt wird die Ausgewogenheit des Datensatzes überprüft, bei einer Klassifikationsaufgabe wie dieser sollte der Datensatz möglichst die gleiche Anzahl an Einträgen für jede Klassifikationsmöglichkeit enthalten. Dieser Datensatz ist enthält 500 Einträge in welchen die Anzeige angeklickt wurde und 500 Einträge in welchen die Anzeige nicht angeklickt wurde und ist somit schon perfekt ausgewogen. \n",
- "Nun beginnt die explorative Datenanalyse, in ihr werden die verschiedenen Merkmale in Verhältnis zueinander gesetzt und das Ergebnis visuell dargestellt und eine Korrelationsanalyse durchgeführt. Das dient dazu mögliche Zusammenhänge zwischen der Zielvariable, in unserem Fall \"Anzeige angeklickt\", und den Merkmalen zu finden. \n",
- "Zusätzlich wird aus dem Timestamp an welchem die Werbeanzeige angezeigt wurde, folgende Merkmale Monat, Tag, Stund sowie Wochentag extrahiert und in neuen Spalten gespeichert. Das Alter wurde in Altersgruppen eingeteilt und in einer neuen Spalte gespeichert. Mit disen neuen Spalten wird nochmal eine explorative Analyse ausgeführt.\n",
- "Die Ergebnisse dieser Analyse werden verwendet um Merkmale bei welchen sich kein Zusammenhang finden lässt werden aus dem Datensatz entfernt, dieser Schritt wird Dimensionsreduktion genannt. Im Falle dieses Datensatzes betrifft es die Spalten Land, Stadt, Thema der Anzeige, Tag, Monat, Stunde, Wochentag, Alter und Altersgruppe. \n",
- "Dieser Datensatz wird abschließend auf ein gemeinsames Messniveau skaliert, auch Standardisierung gennant, und in einen Trainings- sowie Testdatensatz aufgeteilt."
+ "## 2. Daten und Datenverständnis"
]
},
{
@@ -1827,30 +1921,6 @@
"# 6 Modellbildung"
]
},
- {
- "cell_type": "markdown",
- "metadata": {
- "include": true,
- "paragraph": "Datenmodell"
- },
- "source": [
- "Es werden nun zwei Modelle mit dem erstellten Datensatz gebaut, um sie anschließend miteinander zu vergleichen und das bessere der beiden zu Verwenden. \n",
- "Modell 1 basiert auf der logistischen Regression, welche bei binären Zielvariablen immer sehr gute Ergebnisse liefert. \n",
- "Modell 2 basiert auf einem Entscheidungsbaum."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 58,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Import required libraries for the model creation\n",
- "from sklearn.metrics import accuracy_score\n",
- "from sklearn.metrics import confusion_matrix\n",
- "from sklearn.metrics import roc_auc_score, accuracy_score, confusion_matrix, classification_report"
- ]
- },
{
"attachments": {},
"cell_type": "markdown",
@@ -2083,75 +2153,6 @@
"print(f\"Recall: {recall:.2%}\")\n",
"print(f\"F1-Score: {f1_score:.2%}\")"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 7 Evaluation"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "include": true,
- "paragraph": "Evaluation"
- },
- "source": [
- "Die trainierten Modelle werden nun mit dem Testdatensatz getestet. Es gibt verschiedene Metriken zur Bewertung der Qualität des Modells unteranderem Accuracy (= allgemeine Genauigkeit der Klassifikation), Precision (= Päzision der positiven Vorhersagen) und Recall (= Stellt dar wie viele der tatsächlich positiven Einträge im Testdatensatz als positiv erkannt wurden). \n",
- "Hier bei erreicht das Lineare-Regressions-Modell eine Accuracy von 97,8%, eine Precision von 100% und einen Recall von 95,41%. Das Entscheidungsbaum-Modell erreicht die folgenden Werte: Accuracy 93,9% , Precision 90.60% , Recall 97.25%. \n",
- "Da es sich hier nicht um einen Fall handelt bei dem alle Fälle erkannt werden müssen bei der eine Anzeige angeklickt wird, sollte Modell 1 verwendet werden. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "include": false
- },
- "source": [
- "## 8 Deployment"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "include": true,
- "paragraph": "Umsetzung"
- },
- "source": [
- "Es würde sich anbieten dieses Modell beim Ausspielen der Anzeigen zu implementieren, um vor dem Ausspielen einer Anzeige vorherzusagen ob sie geklickt werden würde oder nicht. Hierdurch kann vermieden werden das Nutzer von Werbeanzeigen genervt werden, welche sie sowieso nicht anklicken werden. Dadurch entsteht ein Mehrwert für den Werbekunden sowie für den Nutzer."
- ]
}
- ],
- "metadata": {
- "branche": "Medien",
- "dataSource": "https://storage.googleapis.com/ml-service-repository-datastorage/Predicting_clicks_on_online_advertising_by_Facebook_data.csv",
- "funktion": "Marketing",
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.3"
- },
- "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/blob/main/Marketing/Predicting%20clicks%20on%20online%20advertising%20by%20Facebook/notebook.ipynb?ref_type=heads",
- "skipNotebookInDeployment": false,
- "teaser": "In diesem Beispiel auf Basis der demographischen Merkmale eines Nutzers sowie seiner Internetnutzung, prognostiziert ob er auf eine Werbeanzeige klicken wird.",
- "title": "Vorhersage der Interaktion eines Nutzers mit einer Werbeanzeige"
- },
- "nbformat": 4,
- "nbformat_minor": 4
+ ]
}