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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "business",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "# Predicting mental illness for health insurance\n",
+ "# 1. Business Understanding\n",
+ "Das weltweit agierende Versicherungsunternehmen New York Life Insurance Company\n",
+ "mit Hauptsitz in den USA betreut Privat- und Geschäftskunden. Der Hauptgeschäftsbereich liegt in der Absicherung der leiblichen Gesundheit im Sinne von Krankenkassen\n",
+ "und gesundheitlicher Vorsorge. Dazu gehören neben der klassischen Krankenversicherung auch Unfallversicherungen, Risikolebensversicherungen und Berufsunfähigkeitsversicherungen.\n",
+ "In unserer aktuellen Gesellschaft ist der Mensch nicht nur klassischen körperlichen\n",
+ "Erkrankungen ausgesetzt, sondern zunehmend kommen – beispielsweise durch\n",
+ "Überlastungen am Arbeitsplatz oder Stress – psychische Erkrankungen hinzu. Das\n",
+ "Bewusstsein für diese Art von Erkrankung und die Klassifikation als „Krankheit“\n",
+ "durchdringt jedoch noch nicht alle gesellschaftlichen Bereiche.\n",
+ "Für das Versicherungsunternehmen New York Life Insurance Company bietet es sich\n",
+ "daher an, den Geschäftsbereich zu erweitern und zukünftig nicht nur körperliche,\n",
+ "sondern auch psychische Erkrankungen zu versichern. Um zunächst Kosten\n",
+ "einzusparen, wird keine eigene Umfrage durch das Unternehmen erstellt, sondern auf\n",
+ "schon bestehende Daten zurückgegriffen (OSMI Mental Health in Tech Survey 2016,\n",
+ "2020). \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 2. Data and Data Understanding \n",
+ "## 2.1. Import of Relevant Modules "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "daten",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "daten"
+ ]
+ },
+ "source": [
+ "Der Code importiert Bibliotheken für numerische Berechnungen (NumPy), Datenanalyse (Pandas), statistische Modelle (Statsmodels), Visualisierungen (Matplotlib, Plotly Express, Seaborn), Datenvorverarbeitung (scikit-learn's StandardScaler und LabelEncoder), Modelltraining und -bewertung (train_test_split, DecisionTreeClassifier, RandomForestClassifier, LogisticRegression, KMeans, metrics, confusion_matrix, classification_report, SVM), sowie Multikollinearitätsprüfung (variance_inflation_factor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import statsmodels.api as sm\n",
+ "import matplotlib.pyplot as plt\n",
+ "import plotly.express as px\n",
+ "%matplotlib inline\n",
+ "import seaborn as sns\n",
+ "sns.set()\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "# from sklearn.linear_model import LinearRegression\n",
+ "from sklearn.cluster import KMeans\n",
+ "from sklearn import metrics\n",
+ "from sklearn.metrics import confusion_matrix, classification_report\n",
+ "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+ "from sklearn import svm, datasets\n",
+ "\n",
+ "from sklearn.preprocessing import LabelEncoder\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2.2. Read Data "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Jede Zeile der Tabelle stellt eine einzelne Beobachtung oder Antwort dar."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "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>Timestamp</th>\n",
+ " <th>Age</th>\n",
+ " <th>Gender</th>\n",
+ " <th>Country</th>\n",
+ " <th>state</th>\n",
+ " <th>self_employed</th>\n",
+ " <th>family_history</th>\n",
+ " <th>treatment</th>\n",
+ " <th>work_interfere</th>\n",
+ " <th>no_employees</th>\n",
+ " <th>...</th>\n",
+ " <th>leave</th>\n",
+ " <th>mental_health_consequence</th>\n",
+ " <th>phys_health_consequence</th>\n",
+ " <th>coworkers</th>\n",
+ " <th>supervisor</th>\n",
+ " <th>mental_health_interview</th>\n",
+ " <th>phys_health_interview</th>\n",
+ " <th>mental_vs_physical</th>\n",
+ " <th>obs_consequence</th>\n",
+ " <th>comments</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2014-08-27 11:29:31</td>\n",
+ " <td>37</td>\n",
+ " <td>Female</td>\n",
+ " <td>United States</td>\n",
+ " <td>IL</td>\n",
+ " <td>NaN</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Often</td>\n",
+ " <td>6-25</td>\n",
+ " <td>...</td>\n",
+ " <td>Somewhat easy</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Some of them</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>Maybe</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2014-08-27 11:29:37</td>\n",
+ " <td>44</td>\n",
+ " <td>M</td>\n",
+ " <td>United States</td>\n",
+ " <td>IN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Rarely</td>\n",
+ " <td>More than 1000</td>\n",
+ " <td>...</td>\n",
+ " <td>Don't know</td>\n",
+ " <td>Maybe</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Don't know</td>\n",
+ " <td>No</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2014-08-27 11:29:44</td>\n",
+ " <td>32</td>\n",
+ " <td>Male</td>\n",
+ " <td>Canada</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Rarely</td>\n",
+ " <td>6-25</td>\n",
+ " <td>...</td>\n",
+ " <td>Somewhat difficult</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2014-08-27 11:29:46</td>\n",
+ " <td>31</td>\n",
+ " <td>Male</td>\n",
+ " <td>United Kingdom</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Often</td>\n",
+ " <td>26-100</td>\n",
+ " <td>...</td>\n",
+ " <td>Somewhat difficult</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Some of them</td>\n",
+ " <td>No</td>\n",
+ " <td>Maybe</td>\n",
+ " <td>Maybe</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2014-08-27 11:30:22</td>\n",
+ " <td>31</td>\n",
+ " <td>Male</td>\n",
+ " <td>United States</td>\n",
+ " <td>TX</td>\n",
+ " <td>NaN</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Never</td>\n",
+ " <td>100-500</td>\n",
+ " <td>...</td>\n",
+ " <td>Don't know</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Some of them</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Don't know</td>\n",
+ " <td>No</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 27 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Timestamp Age Gender Country state self_employed \\\n",
+ "0 2014-08-27 11:29:31 37 Female United States IL NaN \n",
+ "1 2014-08-27 11:29:37 44 M United States IN NaN \n",
+ "2 2014-08-27 11:29:44 32 Male Canada NaN NaN \n",
+ "3 2014-08-27 11:29:46 31 Male United Kingdom NaN NaN \n",
+ "4 2014-08-27 11:30:22 31 Male United States TX NaN \n",
+ "\n",
+ " family_history treatment work_interfere no_employees ... \\\n",
+ "0 No Yes Often 6-25 ... \n",
+ "1 No No Rarely More than 1000 ... \n",
+ "2 No No Rarely 6-25 ... \n",
+ "3 Yes Yes Often 26-100 ... \n",
+ "4 No No Never 100-500 ... \n",
+ "\n",
+ " leave mental_health_consequence phys_health_consequence \\\n",
+ "0 Somewhat easy No No \n",
+ "1 Don't know Maybe No \n",
+ "2 Somewhat difficult No No \n",
+ "3 Somewhat difficult Yes Yes \n",
+ "4 Don't know No No \n",
+ "\n",
+ " coworkers supervisor mental_health_interview phys_health_interview \\\n",
+ "0 Some of them Yes No Maybe \n",
+ "1 No No No No \n",
+ "2 Yes Yes Yes Yes \n",
+ "3 Some of them No Maybe Maybe \n",
+ "4 Some of them Yes Yes Yes \n",
+ "\n",
+ " mental_vs_physical obs_consequence comments \n",
+ "0 Yes No NaN \n",
+ "1 Don't know No NaN \n",
+ "2 No No NaN \n",
+ "3 No Yes NaN \n",
+ "4 Don't know No NaN \n",
+ "\n",
+ "[5 rows x 27 columns]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data = pd.read_csv('survey.csv')\n",
+ "data.head(5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "1. Timestamp: Der Zeitpunkt der Datenerfassung.\n",
+ "2. Age: Das Alter der befragten Person.\n",
+ "3. Gender: Das Geschlecht der befragten Person.\n",
+ "4. Country: Das Land, in dem die befragte Person lebt.\n",
+ "5. state: Der Bundesstaat, in dem die befragte Person lebt (nur relevant für die USA).\n",
+ "6. self_employed: Gibt an, ob die Person selbstständig ist.\n",
+ "7. family_history: Gibt an, ob es eine Familiengeschichte von psychischen Erkrankungen gibt.\n",
+ "8. treatment: Gibt an, ob die Person derzeit in Behandlung ist.\n",
+ "9. work_interfere: Gibt an, wie oft die Arbeit durch psychische Gesundheitsprobleme beeinträchtigt wird.\n",
+ "10. no_employees: Die Anzahl der Mitarbeiter im Unternehmen der befragten Person.\n",
+ "11. leave: Gibt an, wie einfach es für die Person ist, eine Beurlaubung aufgrund von psychischen Gesundheitsproblemen zu nehmen.\n",
+ "12. mental_health_consequence: Gibt an, ob die Person glaubt, dass es Konsequenzen für die psychische Gesundheit gibt.\n",
+ "13. phys_health_consequence: Gibt an, ob die Person glaubt, dass es Konsequenzen für die körperliche Gesundheit gibt.\n",
+ "14. coworkers: Gibt an, ob die Person mit ihren Kollegen über ihre psychische Gesundheit sprechen kann.\n",
+ "15. supervisor: Gibt an, ob die Person mit ihrem Vorgesetzten über ihre psychische Gesundheit sprechen kann.\n",
+ "16. mental_health_interview: Gibt an, ob die Person während eines Interviews über ihre psychische Gesundheit sprechen würde.\n",
+ "17. phys_health_interview: Gibt an, ob die Person während eines Interviews über ihre körperliche Gesundheit sprechen würde.\n",
+ "18. mental_vs_physical: Gibt an, ob die Person glaubt, dass psychische und körperliche Gesundheitsprobleme gleich behandelt werden sollten.\n",
+ "19. obs_consequence: Gibt an, ob die Person beobachtet hat, dass jemand aufgrund von psychischen Gesundheitsproblemen negative Konsequenzen erfahren hat.\n",
+ "20. comments: Zusätzliche Kommentare der befragten Person (falls vorhanden)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2.3. Descriptive Analytics "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die angezeigte Datenrahmenstruktur enthält eine Zusammenfassung der Datenstruktur, einschließlich der Datenzeilen und Datenspalten, der Datentypen für jede Spalte und der Zahl der vorhandenen (Nicht-Null-) Werte in jeder Spalte. Diese Informationen sind hilfreich, um die Datenqualität zu bewerten, fehlende Daten zu ermitteln und den Dateninhalt vor der Datenanalyse besser zu verstehen."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 1259 entries, 0 to 1258\n",
+ "Data columns (total 27 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 Timestamp 1259 non-null object\n",
+ " 1 Age 1259 non-null int64 \n",
+ " 2 Gender 1259 non-null object\n",
+ " 3 Country 1259 non-null object\n",
+ " 4 state 744 non-null object\n",
+ " 5 self_employed 1241 non-null object\n",
+ " 6 family_history 1259 non-null object\n",
+ " 7 treatment 1259 non-null object\n",
+ " 8 work_interfere 995 non-null object\n",
+ " 9 no_employees 1259 non-null object\n",
+ " 10 remote_work 1259 non-null object\n",
+ " 11 tech_company 1259 non-null object\n",
+ " 12 benefits 1259 non-null object\n",
+ " 13 care_options 1259 non-null object\n",
+ " 14 wellness_program 1259 non-null object\n",
+ " 15 seek_help 1259 non-null object\n",
+ " 16 anonymity 1259 non-null object\n",
+ " 17 leave 1259 non-null object\n",
+ " 18 mental_health_consequence 1259 non-null object\n",
+ " 19 phys_health_consequence 1259 non-null object\n",
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+ " 22 mental_health_interview 1259 non-null object\n",
+ " 23 phys_health_interview 1259 non-null object\n",
+ " 24 mental_vs_physical 1259 non-null object\n",
+ " 25 obs_consequence 1259 non-null object\n",
+ " 26 comments 164 non-null object\n",
+ "dtypes: int64(1), object(26)\n",
+ "memory usage: 265.7+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
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+ {
+ "data": {
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+ " <td>2014-08-27 12:44:51</td>\n",
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+ " <td>Don't know</td>\n",
+ " <td>No</td>\n",
+ " <td>* Small family business - YMMV.</td>\n",
+ " </tr>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>NaN</td>\n",
+ " <td>2.818299e+09</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>NaN</td>\n",
+ " <td>-1.726000e+03</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>2.700000e+01</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>3.100000e+01</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>NaN</td>\n",
+ " <td>3.600000e+01</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>NaN</td>\n",
+ " <td>1.000000e+11</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>...</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>11 rows × 27 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Timestamp Age Gender Country state \\\n",
+ "count 1259 1.259000e+03 1259 1259 744 \n",
+ "unique 1246 NaN 49 48 45 \n",
+ "top 2014-08-27 12:44:51 NaN Male United States CA \n",
+ "freq 2 NaN 615 751 138 \n",
+ "mean NaN 7.942815e+07 NaN NaN NaN \n",
+ "std NaN 2.818299e+09 NaN NaN NaN \n",
+ "min NaN -1.726000e+03 NaN NaN NaN \n",
+ "25% NaN 2.700000e+01 NaN NaN NaN \n",
+ "50% NaN 3.100000e+01 NaN NaN NaN \n",
+ "75% NaN 3.600000e+01 NaN NaN NaN \n",
+ "max NaN 1.000000e+11 NaN NaN NaN \n",
+ "\n",
+ " self_employed family_history treatment work_interfere no_employees \\\n",
+ "count 1241 1259 1259 995 1259 \n",
+ "unique 2 2 2 4 6 \n",
+ "top No No Yes Sometimes 6-25 \n",
+ "freq 1095 767 637 465 290 \n",
+ "mean NaN NaN NaN NaN NaN \n",
+ "std NaN NaN NaN NaN NaN \n",
+ "min NaN NaN NaN NaN NaN \n",
+ "25% NaN NaN NaN NaN NaN \n",
+ "50% NaN NaN NaN NaN NaN \n",
+ "75% NaN NaN NaN NaN NaN \n",
+ "max NaN NaN NaN NaN NaN \n",
+ "\n",
+ " ... leave mental_health_consequence phys_health_consequence \\\n",
+ "count ... 1259 1259 1259 \n",
+ "unique ... 5 3 3 \n",
+ "top ... Don't know No No \n",
+ "freq ... 563 490 925 \n",
+ "mean ... NaN NaN NaN \n",
+ "std ... NaN NaN NaN \n",
+ "min ... NaN NaN NaN \n",
+ "25% ... NaN NaN NaN \n",
+ "50% ... NaN NaN NaN \n",
+ "75% ... NaN NaN NaN \n",
+ "max ... NaN NaN NaN \n",
+ "\n",
+ " coworkers supervisor mental_health_interview phys_health_interview \\\n",
+ "count 1259 1259 1259 1259 \n",
+ "unique 3 3 3 3 \n",
+ "top Some of them Yes No Maybe \n",
+ "freq 774 516 1008 557 \n",
+ "mean NaN NaN NaN NaN \n",
+ "std NaN NaN NaN NaN \n",
+ "min NaN NaN NaN NaN \n",
+ "25% NaN NaN NaN NaN \n",
+ "50% NaN NaN NaN NaN \n",
+ "75% NaN NaN NaN NaN \n",
+ "max NaN NaN NaN NaN \n",
+ "\n",
+ " mental_vs_physical obs_consequence comments \n",
+ "count 1259 1259 164 \n",
+ "unique 3 2 160 \n",
+ "top Don't know No * Small family business - YMMV. \n",
+ "freq 576 1075 5 \n",
+ "mean NaN NaN NaN \n",
+ "std NaN NaN NaN \n",
+ "min NaN NaN NaN \n",
+ "25% NaN NaN NaN \n",
+ "50% NaN NaN NaN \n",
+ "75% NaN NaN NaN \n",
+ "max NaN NaN NaN \n",
+ "\n",
+ "[11 rows x 27 columns]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.describe(include='all') "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Alle Daten statistisch anschauen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "| Feature | Data Type|\n",
+ "|-----|------|\n",
+ "|Timestamp | str [ example: \"2014-08-28 10:34:01\" ] |\n",
+ "|Age | int64 |\n",
+ "|Gender | str [ example: \"m\" ] |\n",
+ "|Country | str [ example: \"United States\" ] |\n",
+ "|state | str [ example: \"CA\" ] |\n",
+ "|self_employed | str {\"Yes\"; \"No\"} |\n",
+ "|family_history | str {\"No\"; \"Yes\"} |\n",
+ "|treatment | str {\"Yes\"; \"No\"} |\n",
+ "|work_interfere | str {\"Often\"; \"Rarely\"; \"Never\"; \"Sometimes\"} |\n",
+ "|no_employees | str {\"6-25\"; \"More than 1000\"; \"26-100\"; \"100-500\"; \"1-5\"; \"500-1000\"} |\n",
+ "|remote_work | str {\"No\"; \"Yes\"} |\n",
+ "|tech_company | str {\"Yes\"; \"No\"} |\n",
+ "|benefits | str {\"Yes\"; \"Don't know\"; \"No\"} |\n",
+ "|care_options | str {\"Not sure\"; \"No\"; \"Yes\"} |\n",
+ "|wellness_program | str {\"No\"; \"Don't know\"; \"Yes\"} |\n",
+ "|seek_help | str {\"Yes\"; \"Don't know\"; \"No\"} |\n",
+ "|anonymity | str {\"Yes\"; \"Don't know\"; \"No\"} |\n",
+ "|leave | str {\"Somewhat easy\"; \"Don't know\"; \"Somewhat difficult\"; \"Very difficult\"; \"Very easy\"} |\n",
+ "|mental_health_consequence| str {\"No\"; \"Maybe\"; \"Yes\"} |\n",
+ "|phys_health_consequence | str {\"No\"; \"Yes\"; \"Maybe\"} |\n",
+ "|coworkers | str {\"Some of them\"; \"No\"; \"Yes\"} |\n",
+ "|supervisor | str {\"Yes\"; \"No\"; \"Some of them\"} |\n",
+ "|mental_health_interview | str {\"No\"; \"Yes\"; \"Maybe\"} |\n",
+ "|phys_health_interview | str {\"Maybe\"; \"No\"; \"Yes\"} |\n",
+ "|mental_vs_physical | str {\"Yes\"; \"Don't know\"; \"No\"} |\n",
+ "|obs_consequence | str {\"No\"; \"Yes\"} |\n",
+ "|comments | str [ example: \"fwiw I am a co founder of this company and the would you X in an interview questions shouldn't reflect how I would treat anyone addressing their own phys/mental health issue to me in such a situation. \" ] |\n"
+ ]
+ }
+ ],
+ "source": [
+ "def attribute_description(data):\n",
+ " longestColumnName = len(max(np.array(data.columns), key=len))\n",
+ " print(\"| Feature | Data Type|\")\n",
+ " print(\"|-----|------|\")\n",
+ " for col in data.columns:\n",
+ " description = ''\n",
+ " col_dropna = data[col].dropna()\n",
+ " example = col_dropna.sample(1).values[0]\n",
+ " if type(example) == str:\n",
+ " description = 'str '\n",
+ " if len(col_dropna.unique()) < 10:\n",
+ " description += '{'\n",
+ " description += '; '.join([ f'\"{name}\"' for name in col_dropna.unique()])\n",
+ " description += '}'\n",
+ " else:\n",
+ " description += '[ example: \"'+ example + '\" ]'\n",
+ " elif (type(example) == np.int32) and (len(col_dropna.unique()) < 10) :\n",
+ " description += 'dummy int32 {'\n",
+ " description += '; '.join([ f'{name}' for name in sorted(col_dropna.unique())])\n",
+ " description += '}'\n",
+ " else:\n",
+ " try:\n",
+ " description = example.dtype\n",
+ " except:\n",
+ " description = type(example)\n",
+ " print(\"|\" + col.ljust(longestColumnName)+ f'| {description} |')\n",
+ " \n",
+ "attribute_description(data) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "Die Funktion attribute_description nimmt ein pandas-DataFrame als Input und druckt eine Tabelle aus, die die Eigenschaften der Spalten des DataFrames beschreibt. Dabei wird der längste Spaltenname als Maximalwert für die Breite der Tabelle verwendet. Für jede Spalte in der DataFrame wird dann versucht, Informationen zum Datentyp und zum Beispiel für die Spalte zu bekommen. Wenn das Datentyp ist eine Zeichenkette und es gibt weniger als 10 einzigartige Werte in der Spalte, wird der Spaltenname mit zusätzliche Information gedruckt. Wenn das Datentyp ist ein Integer 32-bit und es gibt weniger als 10 einzigartige Werte in der Spalte, wird auch eine zusätzliche Information gedruckt.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "| Feature | Data Type|\n",
+ "|-----|------|\n",
+ "|Timestamp | str [ example: \"2014-08-27 20:52:31\" ] |\n",
+ "|Age | int64 |\n",
+ "|Gender | str [ example: \"Male\" ] |\n",
+ "|Country | str [ example: \"China\" ] |\n",
+ "|state | str [ example: \"OR\" ] |\n",
+ "|self_employed | str {\"Yes\"; \"No\"; \"IL\"} |\n",
+ "|family_history | str {\"No\"; \"Yes\"} |\n",
+ "|treatment | str {\"Yes\"; \"No\"} |\n",
+ "|work_interfere | str {\"Often\"; \"Rarely\"; \"Never\"; \"Sometimes\"; \"Yes\"} |\n",
+ "|no_employees | str {\"6-25\"; \"More than 1000\"; \"26-100\"; \"100-500\"; \"1-5\"; \"500-1000\"; \"Often\"; \"Sometimes\"} |\n",
+ "|remote_work | str {\"No\"; \"Yes\"; \"1-5\"; \"6-25\"} |\n",
+ "|tech_company | str {\"Yes\"; \"No\"} |\n",
+ "|benefits | str {\"Yes\"; \"Don't know\"; \"No\"} |\n",
+ "|care_options | str {\"Not sure\"; \"No\"; \"Yes\"; \"Don't know\"} |\n",
+ "|wellness_program | str {\"No\"; \"Don't know\"; \"Yes\"} |\n",
+ "|seek_help | str {\"Yes\"; \"Don't know\"; \"No\"} |\n",
+ "|anonymity | str {\"Yes\"; \"Don't know\"; \"No\"} |\n",
+ "|leave | str {\"Somewhat easy\"; \"Don't know\"; \"Somewhat difficult\"; \"Very difficult\"; \"Very easy\"; \"Yes\"} |\n",
+ "|mental_health_consequence| str {\"No\"; \"Maybe\"; \"Yes\"; \"Very easy\"; \"Don't know\"} |\n",
+ "|phys_health_consequence | str {\"No\"; \"Yes\"; \"Maybe\"} |\n",
+ "|coworkers | str {\"Some of them\"; \"No\"; \"Yes\"; \"Maybe\"} |\n",
+ "|supervisor | str {\"Yes\"; \"No\"; \"Some of them\"} |\n",
+ "|mental_health_interview | str {\"No\"; \"Yes\"; \"Maybe\"} |\n",
+ "|phys_health_interview | str {\"Maybe\"; \"No\"; \"Yes\"} |\n",
+ "|mental_vs_physical | str {\"Yes\"; \"Don't know\"; \"No\"; \"Maybe\"} |\n",
+ "|obs_consequence | str {\"No\"; \"Yes\"; \"Don't know\"} |\n",
+ "|comments;;;; | str [ example: \"NA;;;;\" ] |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die bereitgestellten Informationen beschreiben ein Dataset mit verschiedenen Merkmalen (Features) und den entsprechenden Datentypen oder möglichen Wertebereichen für jedes Feature. Dieses Dataset könnte beispielsweise aus einer Umfrage stammen, die sich mit der psychischen Gesundheit von Beschäftigten befasst. Lassen Sie mich kurz die Bedeutung jedes Merkmals erläutern:"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "# 3. Data Preparation \n",
+ "## 3.1 Remove duplicates"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenvorbereitung",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": [
+ "Datenvorbereitung"
+ ]
+ },
+ "source": [
+ "Der Codeabschnitt entfernt Duplikate in einer Liste, indem er alle Elemente beibehält, die nicht doppelt vorhanden sind. Dabei überprüft er zunächst, ob es in der Liste duplizierte Elemente gibt, und wenn ja, ruft er die data.duplicated(keep=False)-Funktion auf. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Timestamp</th>\n",
+ " <th>Age</th>\n",
+ " <th>Gender</th>\n",
+ " <th>Country</th>\n",
+ " <th>state</th>\n",
+ " <th>self_employed</th>\n",
+ " <th>family_history</th>\n",
+ " <th>treatment</th>\n",
+ " <th>work_interfere</th>\n",
+ " <th>no_employees</th>\n",
+ " <th>...</th>\n",
+ " <th>leave</th>\n",
+ " <th>mental_health_consequence</th>\n",
+ " <th>phys_health_consequence</th>\n",
+ " <th>coworkers</th>\n",
+ " <th>supervisor</th>\n",
+ " <th>mental_health_interview</th>\n",
+ " <th>phys_health_interview</th>\n",
+ " <th>mental_vs_physical</th>\n",
+ " <th>obs_consequence</th>\n",
+ " <th>comments</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>0 rows × 27 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "Empty DataFrame\n",
+ "Columns: [Timestamp, Age, Gender, Country, state, self_employed, family_history, treatment, work_interfere, no_employees, remote_work, tech_company, benefits, care_options, wellness_program, seek_help, anonymity, leave, mental_health_consequence, phys_health_consequence, coworkers, supervisor, mental_health_interview, phys_health_interview, mental_vs_physical, obs_consequence, comments]\n",
+ "Index: []\n",
+ "\n",
+ "[0 rows x 27 columns]"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data[data.duplicated(keep=False)] # show duplicates"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.2 Remove missing data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Timestamp 0\n",
+ "Age 0\n",
+ "Gender 0\n",
+ "Country 0\n",
+ "state 515\n",
+ "self_employed 18\n",
+ "family_history 0\n",
+ "treatment 0\n",
+ "work_interfere 264\n",
+ "no_employees 0\n",
+ "remote_work 0\n",
+ "tech_company 0\n",
+ "benefits 0\n",
+ "care_options 0\n",
+ "wellness_program 0\n",
+ "seek_help 0\n",
+ "anonymity 0\n",
+ "leave 0\n",
+ "mental_health_consequence 0\n",
+ "phys_health_consequence 0\n",
+ "coworkers 0\n",
+ "supervisor 0\n",
+ "mental_health_interview 0\n",
+ "phys_health_interview 0\n",
+ "mental_vs_physical 0\n",
+ "obs_consequence 0\n",
+ "comments 1095\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data.isnull().sum() #count missing data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data1 = data.drop(['Timestamp','state','comments'], axis =1)\n",
+ "# delete features, that are not needed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data1['self_employed'] = data1['self_employed'].fillna(data1['self_employed'].mode().iloc[0]) \n",
+ "# replace missing data in 'self_employed' wwith 'No'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code setzt die fehlenden Daten im 'self_employed' Column auf 'No', indem er den Wert aus der häufigsten Wert in 'self_employed' als Substitut verwendet."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Age 0\n",
+ "Gender 0\n",
+ "Country 0\n",
+ "self_employed 0\n",
+ "family_history 0\n",
+ "treatment 0\n",
+ "work_interfere 264\n",
+ "no_employees 0\n",
+ "remote_work 0\n",
+ "tech_company 0\n",
+ "benefits 0\n",
+ "care_options 0\n",
+ "wellness_program 0\n",
+ "seek_help 0\n",
+ "anonymity 0\n",
+ "leave 0\n",
+ "mental_health_consequence 0\n",
+ "phys_health_consequence 0\n",
+ "coworkers 0\n",
+ "supervisor 0\n",
+ "mental_health_interview 0\n",
+ "phys_health_interview 0\n",
+ "mental_vs_physical 0\n",
+ "obs_consequence 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data1.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data1 = data1.dropna(axis=0) # remove rows with missing data (in 'work_interfere)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Age 0\n",
+ "Gender 0\n",
+ "Country 0\n",
+ "self_employed 0\n",
+ "family_history 0\n",
+ "treatment 0\n",
+ "work_interfere 0\n",
+ "no_employees 0\n",
+ "remote_work 0\n",
+ "tech_company 0\n",
+ "benefits 0\n",
+ "care_options 0\n",
+ "wellness_program 0\n",
+ "seek_help 0\n",
+ "anonymity 0\n",
+ "leave 0\n",
+ "mental_health_consequence 0\n",
+ "phys_health_consequence 0\n",
+ "coworkers 0\n",
+ "supervisor 0\n",
+ "mental_health_interview 0\n",
+ "phys_health_interview 0\n",
+ "mental_vs_physical 0\n",
+ "obs_consequence 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data1.isnull().sum() # make sure there is no missing data now"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.3 Remove unwanted Features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['Age', 'Gender', 'Country', 'self_employed', 'family_history',\n",
+ " 'treatment', 'work_interfere', 'no_employees', 'remote_work',\n",
+ " 'tech_company', 'benefits', 'care_options', 'wellness_program',\n",
+ " 'seek_help', 'anonymity', 'leave', 'mental_health_consequence',\n",
+ " 'phys_health_consequence', 'coworkers', 'supervisor',\n",
+ " 'mental_health_interview', 'phys_health_interview',\n",
+ " 'mental_vs_physical', 'obs_consequence'], dtype=object)"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data1.columns.values "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data1 = data1.drop(['Country','wellness_program', 'seek_help', 'anonymity', 'leave',\n",
+ " 'mental_health_consequence', 'phys_health_consequence',\n",
+ " 'coworkers', 'supervisor', 'mental_health_interview',\n",
+ " 'phys_health_interview', 'mental_vs_physical', 'obs_consequence'], axis = 1) \n",
+ "# remove features that are not relevant\n",
+ "# and remove features, that are relevant, but can not be used in the final model, \n",
+ "# as the data can not be collected in the production environment"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "Die Zeilen des Dataframes werden entfernt, in denen bestimmte Spalten nicht enthalten sind, oder nicht relevant oder nicht in der Produktionsumgebung erhoben werden können. Dies wird erreicht, indem der Drop-Methode mit einem Syntax-Paar von Spaltennamen und Spaltenenums zu einem Dataframe-Objekt zugefügt wird. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['Age', 'Gender', 'self_employed', 'family_history', 'treatment',\n",
+ " 'work_interfere', 'no_employees', 'remote_work', 'tech_company',\n",
+ " 'benefits', 'care_options'], dtype=object)"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data1.columns.values # these features are left"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "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>Age</th>\n",
+ " <th>Gender</th>\n",
+ " <th>self_employed</th>\n",
+ " <th>family_history</th>\n",
+ " <th>treatment</th>\n",
+ " <th>work_interfere</th>\n",
+ " <th>no_employees</th>\n",
+ " <th>remote_work</th>\n",
+ " <th>tech_company</th>\n",
+ " <th>benefits</th>\n",
+ " <th>care_options</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>9.950000e+02</td>\n",
+ " <td>995</td>\n",
+ " <td>995</td>\n",
+ " <td>995</td>\n",
+ " <td>995</td>\n",
+ " <td>995</td>\n",
+ " <td>995</td>\n",
+ " <td>995</td>\n",
+ " <td>995</td>\n",
+ " <td>995</td>\n",
+ " <td>995</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>unique</th>\n",
+ " <td>NaN</td>\n",
+ " <td>44</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>4</td>\n",
+ " <td>6</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>top</th>\n",
+ " <td>NaN</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Sometimes</td>\n",
+ " <td>26-100</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>freq</th>\n",
+ " <td>NaN</td>\n",
+ " <td>481</td>\n",
+ " <td>870</td>\n",
+ " <td>546</td>\n",
+ " <td>633</td>\n",
+ " <td>465</td>\n",
+ " <td>229</td>\n",
+ " <td>691</td>\n",
+ " <td>815</td>\n",
+ " <td>406</td>\n",
+ " <td>393</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>1.005025e+08</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>3.170213e+09</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>-1.726000e+03</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>2.700000e+01</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>3.100000e+01</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>3.600000e+01</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>1.000000e+11</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Age Gender self_employed family_history treatment \\\n",
+ "count 9.950000e+02 995 995 995 995 \n",
+ "unique NaN 44 2 2 2 \n",
+ "top NaN Male No No Yes \n",
+ "freq NaN 481 870 546 633 \n",
+ "mean 1.005025e+08 NaN NaN NaN NaN \n",
+ "std 3.170213e+09 NaN NaN NaN NaN \n",
+ "min -1.726000e+03 NaN NaN NaN NaN \n",
+ "25% 2.700000e+01 NaN NaN NaN NaN \n",
+ "50% 3.100000e+01 NaN NaN NaN NaN \n",
+ "75% 3.600000e+01 NaN NaN NaN NaN \n",
+ "max 1.000000e+11 NaN NaN NaN NaN \n",
+ "\n",
+ " work_interfere no_employees remote_work tech_company benefits \\\n",
+ "count 995 995 995 995 995 \n",
+ "unique 4 6 2 2 3 \n",
+ "top Sometimes 26-100 No Yes Yes \n",
+ "freq 465 229 691 815 406 \n",
+ "mean NaN NaN NaN NaN NaN \n",
+ "std NaN NaN NaN NaN NaN \n",
+ "min NaN NaN NaN NaN NaN \n",
+ "25% NaN NaN NaN NaN NaN \n",
+ "50% NaN NaN NaN NaN NaN \n",
+ "75% NaN NaN NaN NaN NaN \n",
+ "max NaN NaN NaN NaN NaN \n",
+ "\n",
+ " care_options \n",
+ "count 995 \n",
+ "unique 3 \n",
+ "top Yes \n",
+ "freq 393 \n",
+ "mean NaN \n",
+ "std NaN \n",
+ "min NaN \n",
+ "25% NaN \n",
+ "50% NaN \n",
+ "75% NaN \n",
+ "max NaN "
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data1.describe(include='all')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ " 31, 28, 43, 22, 32,\n",
+ " 36, 41, 30, 30, 36,\n",
+ " 29, 36, 32, 34, 25],\n",
+ " dtype=int64)"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data1['Age'].values"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.4 Remove outliers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "einige Personen haben einen Troll Value für \"Alter\" eingegeben, diese Zeilen müssen entfernt werden \n",
+ "das Alter sollte zwischen 16 und 70 Jahren liegen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='Age', ylabel='Count'>"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.histplot(data = data1, x = 'Age', bins=\"sqrt\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='Age', ylabel='Count'>"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "data2 = data1[data1['Age']<70] \n",
+ "data2 = data2[data2['Age']>16] \n",
+ "sns.histplot(data2['Age'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "31.5"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data2['Age'].median(axis = 0) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['Age', 'Gender', 'self_employed', 'family_history', 'treatment',\n",
+ " 'work_interfere', 'no_employees', 'remote_work', 'tech_company',\n",
+ " 'benefits', 'care_options'], dtype=object)"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data2.columns.values # these features are left"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "## .5 Clean the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['Age', 'Gender', 'self_employed', 'family_history', 'treatment',\n",
+ " 'work_interfere', 'no_employees', 'remote_work', 'tech_company',\n",
+ " 'benefits', 'care_options'], dtype=object)"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data2.columns.values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# clean Gender\n",
+ "# reduce options to only male or female\n",
+ "data2['Gender'] = data2['Gender'].str.lower()\n",
+ "male = [\"male\", \"m\", \"male-ish\", \"maile\", \"mal\", \"male (cis)\", \"make\", \"male \", \"man\",\"msle\", \"mail\", \"malr\",\"cis man\", \"cis male\"]\n",
+ "trans = [\"trans-female\", \"something kinda male?\", \"queer/she/they\", \"non-binary\",\"nah\", \"all\", \"enby\", \"fluid\", \"genderqueer\", \"androgyne\", \"agender\", \"male leaning androgynous\", \"guy (-ish) ^_^\", \"trans woman\", \"neuter\", \"female (trans)\", \"queer\", \"ostensibly male, unsure what that really means\"]\n",
+ "female = [\"cis female\", \"f\", \"female\", \"woman\", \"femake\", \"female \",\"cis-female/femme\", \"female (cis)\", \"femail\"]\n",
+ "data2['Gender'] = data2['Gender'].apply(lambda x:\"Male\" if x in male else x)\n",
+ "data2['Gender'] = data2['Gender'].apply(lambda x:\"Female\" if x in female else x)\n",
+ "data2['Gender'] = data2['Gender'].apply(lambda x:\"Trans\" if x in trans else x)\n",
+ "data2.drop(data2[data2.Gender == 'p'].index, inplace=True)\n",
+ "data2.drop(data2[data2.Gender == 'a little about you'].index, inplace=True)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dieser Code setzt die Spalte \"Geschlecht\" des Datenrahmens \"data2\" auf der Grundlage einer Liste vordefinierter Optionen entweder auf \"Männlich\", \"Weiblich\" oder \"Trans\". Die Spalte \"Geschlecht\" wird zunächst in Kleinbuchstaben umgewandelt und dann wird der Code ausgeführt, um den Wert von \"Geschlecht\" auf der Grundlage der Liste der Optionen festzulegen. Schließlich entfernt der Code alle Instanzen von \"p\" oder \"a little about you\" (die wahrscheinlich ungültige Werte darstellen) aus der Spalte \"Geschlecht\".\n",
+ "\n",
+ "Das Ergebnis dieses Codes ist eine Spalte \"Geschlecht\" mit konsistenteren Werten, die für eine genauere Analyse oder Modellierung verwendet werden kann\n",
+ "\n",
+ "Ãœbersetzt mit DeepL.com (kostenlose Version)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "domain": {
+ "x": [
+ 0,
+ 1
+ ],
+ "y": [
+ 0,
+ 1
+ ]
+ },
+ "hovertemplate": "Gender=%{label}<br>count=%{value}<extra></extra>",
+ "labels": [
+ "Male",
+ "Female",
+ "Trans"
+ ],
+ "legendgroup": "",
+ "name": "",
+ "showlegend": true,
+ "type": "pie",
+ "values": [
+ 761,
+ 210,
+ 17
+ ]
+ }
+ ],
+ "layout": {
+ "legend": {
+ "tracegroupgap": 0
+ },
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "rgb(51,51,51)"
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+ "error_y": {
+ "color": "rgb(51,51,51)"
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+ "marker": {
+ "line": {
+ "color": "rgb(237,237,237)",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "bar"
+ }
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+ "barpolar": [
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+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
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+ "type": "barpolar"
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+ "carpet": [
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+ "gridcolor": "white",
+ "linecolor": "white",
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+ "baxis": {
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+ "choropleth": [
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+ "ticklen": 6,
+ "ticks": "inside"
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+ "type": "choropleth"
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+ "contourcarpet": [
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+ "tickcolor": "rgb(237,237,237)",
+ "ticklen": 6,
+ "ticks": "inside"
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+ "type": "contourcarpet"
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+ "heatmap": [
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+ "ticks": "inside"
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+ "plot_gender = data2['Gender'].value_counts().reset_index()\n",
+ "plot_gender.columns = ['Gender','count']\n",
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+ "px.pie(plot_gender,values='count',names='Gender',template='ggplot2',title='Gender')"
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+ "metadata": {},
+ "source": [
+ "In diesem Schaubild wird die Anzahl der Männer, Frauen und Trans-Personen dargestellt."
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+ "metadata": {},
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+ ],
+ "source": [
+ "plot_self = data2['self_employed'].value_counts().reset_index()\n",
+ "plot_self.columns = ['self_employed','count']\n",
+ "\n",
+ "px.pie(plot_self,values='count',names='self_employed',template='ggplot2',title='Self employed')\n",
+ "\n",
+ "# Self employed looks good"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "im diesem Schaubild zeig die Anzahl der selbstständigen Menschen und nicht selbstständigen zeigt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
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+ " \n",
+ "var gd = document.getElementById('014e78b0-7812-4011-8ae1-46d2298e0fc6');\n",
+ "var x = new MutationObserver(function (mutations, observer) {{\n",
+ " var display = window.getComputedStyle(gd).display;\n",
+ " if (!display || display === 'none') {{\n",
+ " console.log([gd, 'removed!']);\n",
+ " Plotly.purge(gd);\n",
+ " observer.disconnect();\n",
+ " }}\n",
+ "}});\n",
+ "\n",
+ "// Listen for the removal of the full notebook cells\n",
+ "var notebookContainer = gd.closest('#notebook-container');\n",
+ "if (notebookContainer) {{\n",
+ " x.observe(notebookContainer, {childList: true});\n",
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+ "var outputEl = gd.closest('.output');\n",
+ "if (outputEl) {{\n",
+ " x.observe(outputEl, {childList: true});\n",
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_fam = data2['family_history'].value_counts().reset_index()\n",
+ "plot_fam.columns = ['family_history','count']\n",
+ "\n",
+ "px.pie(plot_fam,values='count',names='family_history',template='ggplot2',title='Family history')\n",
+ "\n",
+ "# family history looks good"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In diesem Schaulbild Gibt an, ob es eine Familiengeschichte von psychischen Erkrankungen gibt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
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+ "metadata": {},
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+ ],
+ "source": [
+ "plot_treat = data2['treatment'].value_counts().reset_index()\n",
+ "plot_treat.columns = ['treatment','count']\n",
+ "\n",
+ "px.pie(plot_treat,values='count',names='treatment',template='ggplot2',title='In treatment')\n",
+ "\n",
+ "# in treatment looks good.\n",
+ "# this will be our target variable"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In diesem Schaubild gibt es wie viele Personen derzeit in Behandlung ist."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
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+ ]
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+ }
+ ],
+ "source": [
+ "plot_numb = data2['no_employees'].value_counts().reset_index()\n",
+ "plot_numb.columns = ['no_employees','count']\n",
+ "px.pie(plot_numb,values='count',names='no_employees',template='ggplot2',title='Number of employees')\n",
+ "\n",
+ "# no_employees looks good"
+ ]
+ },
+ {
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+ "source": [
+ "In diesem Schaulbild wird die Anzahl der Mitarbeiter im Unternehmen der befragten Person dargestellt."
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+ "metadata": {},
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+ "source": [
+ "plot_remo = data2['remote_work'].value_counts().reset_index()\n",
+ "plot_remo .columns = ['remote_work','count']\n",
+ "\n",
+ "px.pie(plot_remo ,values='count',names='remote_work',template='ggplot2',title='Remote work')\n",
+ "\n",
+ "# remote work looks good"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In diesem Schaubild wird dargestellt, wie viele Personen Remote Work ausüben.\n"
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+ "plot_care = data2['care_options'].value_counts().reset_index()\n",
+ "plot_care.columns = ['care_options','count']\n",
+ "\n",
+ "px.pie(plot_care,values='count',names='care_options',template='ggplot2',title='Care options')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In diesem Schaubild wird dargestellt wie viele eine Pflegemöglichkeit haben"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
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+ " <th>Age</th>\n",
+ " <th>Gender</th>\n",
+ " <th>self_employed</th>\n",
+ " <th>family_history</th>\n",
+ " <th>treatment</th>\n",
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+ " <td>971.000000</td>\n",
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+ " <td>761</td>\n",
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+ " <td>613</td>\n",
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+ " <td>32.330587</td>\n",
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+ " Age Gender self_employed family_history treatment \\\n",
+ "count 971.000000 971 971 971 971 \n",
+ "unique NaN 2 2 2 2 \n",
+ "top NaN Male No No Yes \n",
+ "freq NaN 761 852 535 613 \n",
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+ "75% 36.000000 NaN NaN NaN NaN \n",
+ "max 62.000000 NaN NaN NaN NaN \n",
+ "\n",
+ " work_interfere no_employees remote_work tech_company benefits \\\n",
+ "count 971 971 971 971 971 \n",
+ "unique 4 6 2 2 3 \n",
+ "top Sometimes 26-100 No Yes Yes \n",
+ "freq 453 223 675 798 395 \n",
+ "mean NaN NaN NaN NaN NaN \n",
+ "std NaN NaN NaN NaN NaN \n",
+ "min NaN NaN NaN NaN NaN \n",
+ "25% NaN NaN NaN NaN NaN \n",
+ "50% NaN NaN NaN NaN NaN \n",
+ "75% NaN NaN NaN NaN NaN \n",
+ "max NaN NaN NaN NaN NaN \n",
+ "\n",
+ " care_options \n",
+ "count 971 \n",
+ "unique 3 \n",
+ "top Yes \n",
+ "freq 379 \n",
+ "mean NaN \n",
+ "std NaN \n",
+ "min NaN \n",
+ "25% NaN \n",
+ "50% NaN \n",
+ "75% NaN \n",
+ "max NaN "
+ ]
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+ "execution_count": 65,
+ "metadata": {},
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+ }
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+ "source": [
+ "data2.describe(include='all')"
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+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,5))\n",
+ "sns.countplot(y=\"Gender\", hue=\"treatment\", data=data2)\n",
+ "plt.title(\"mental health vs Gender\",fontsize=15,fontweight=\"normal\")\n",
+ "plt.ylabel(\"\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,5))\n",
+ "sns.countplot(y=\"family_history\", hue=\"treatment\", data=data2)\n",
+ "plt.title(\"family history vs mental health \",\n",
+ " fontsize=15,fontweight=\"normal\")\n",
+ "plt.ylabel(\"\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,5))\n",
+ "sns.countplot(y=\"work_interfere\", hue=\"treatment\", data=data2)\n",
+ "plt.title(\"Behandlung der psychischen Erkrankung stört die tägliche Arbeit?\",fontsize=15,fontweight=\"normal\")\n",
+ "plt.ylabel(\"\")\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dieses Merkmal scheint ein guter Prädiktor für die Zielvariable Behandlung zu sein. \n",
+ "Aber die Information \"Beeinträchtigt Ihre psychische Gesundheit Ihre Arbeit\" kann nicht erhoben werden. \n",
+ "Niemand, der bei klarem Verstand ist, würde seiner Krankenkasse gegenüber die Wahrheit zu dieser Frage sagen. \n",
+ "Daher muss die Frage nach der Beeinträchtigung der Arbeit gestrichen werden."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data2 = data2.drop('work_interfere', axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,5))\n",
+ "sns.countplot(x=\"no_employees\", hue=\"treatment\", data=data2)\n",
+ "plt.title(\"number of employees vs mental health\",fontsize=18,fontweight=\"normal\")\n",
+ "plt.ylabel(\"\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Age</th>\n",
+ " <th>Gender</th>\n",
+ " <th>self_employed</th>\n",
+ " <th>family_history</th>\n",
+ " <th>treatment</th>\n",
+ " <th>no_employees</th>\n",
+ " <th>remote_work</th>\n",
+ " <th>tech_company</th>\n",
+ " <th>benefits</th>\n",
+ " <th>care_options</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>37</td>\n",
+ " <td>Female</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>6-25</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Not sure</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>44</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>More than 1000</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Don't know</td>\n",
+ " <td>No</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>32</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>6-25</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>31</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>26-100</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>31</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>100-500</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1252</th>\n",
+ " <td>29</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>100-500</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1253</th>\n",
+ " <td>36</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>No</td>\n",
+ " <td>More than 1000</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Don't know</td>\n",
+ " <td>No</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1255</th>\n",
+ " <td>32</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>26-100</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1256</th>\n",
+ " <td>34</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>More than 1000</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1258</th>\n",
+ " <td>25</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>26-100</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>971 rows × 10 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Age Gender self_employed family_history treatment no_employees \\\n",
+ "0 37 Female No No Yes 6-25 \n",
+ "1 44 Male No No No More than 1000 \n",
+ "2 32 Male No No No 6-25 \n",
+ "3 31 Male No Yes Yes 26-100 \n",
+ "4 31 Male No No No 100-500 \n",
+ "... ... ... ... ... ... ... \n",
+ "1252 29 Male No Yes Yes 100-500 \n",
+ "1253 36 Male No Yes No More than 1000 \n",
+ "1255 32 Male No Yes Yes 26-100 \n",
+ "1256 34 Male No Yes Yes More than 1000 \n",
+ "1258 25 Male No Yes Yes 26-100 \n",
+ "\n",
+ " remote_work tech_company benefits care_options \n",
+ "0 No Yes Yes Not sure \n",
+ "1 No No Don't know No \n",
+ "2 No Yes No No \n",
+ "3 No Yes No Yes \n",
+ "4 Yes Yes Yes No \n",
+ "... ... ... ... ... \n",
+ "1252 Yes Yes Yes Yes \n",
+ "1253 No No Don't know No \n",
+ "1255 Yes Yes Yes Yes \n",
+ "1256 No Yes Yes Yes \n",
+ "1258 No No Yes Yes \n",
+ "\n",
+ "[971 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data3 = data2.reset_index(drop = True) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 971 entries, 0 to 970\n",
+ "Data columns (total 10 columns):\n",
+ " # Column Non-Null Count Dtype \n",
+ "--- ------ -------------- ----- \n",
+ " 0 Age 971 non-null int64 \n",
+ " 1 Gender 971 non-null object\n",
+ " 2 self_employed 971 non-null object\n",
+ " 3 family_history 971 non-null object\n",
+ " 4 treatment 971 non-null object\n",
+ " 5 no_employees 971 non-null object\n",
+ " 6 remote_work 971 non-null object\n",
+ " 7 tech_company 971 non-null object\n",
+ " 8 benefits 971 non-null object\n",
+ " 9 care_options 971 non-null object\n",
+ "dtypes: int64(1), object(9)\n",
+ "memory usage: 76.0+ KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "data3.info()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dies ist ein DataFrame aus der pandas-Bibliothek. Es enthält Statistiken zu various Themen wie dem Alter, der Geschlechtsaufteilung, dem Arbeitsstatus, der Familiengeschichte und einer ganzen Reihe anderer Informationen über eine Person. Es heißt DataFrame, weil es aus zwei Komponenten besteht, einer Tabelle der Daten und einer Index-Spalte."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "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>Age</th>\n",
+ " <th>Gender</th>\n",
+ " <th>self_employed</th>\n",
+ " <th>family_history</th>\n",
+ " <th>treatment</th>\n",
+ " <th>no_employees</th>\n",
+ " <th>remote_work</th>\n",
+ " <th>tech_company</th>\n",
+ " <th>benefits</th>\n",
+ " <th>care_options</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>971.000000</td>\n",
+ " <td>971</td>\n",
+ " <td>971</td>\n",
+ " <td>971</td>\n",
+ " <td>971</td>\n",
+ " <td>971</td>\n",
+ " <td>971</td>\n",
+ " <td>971</td>\n",
+ " <td>971</td>\n",
+ " <td>971</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>unique</th>\n",
+ " <td>NaN</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>6</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>top</th>\n",
+ " <td>NaN</td>\n",
+ " <td>Male</td>\n",
+ " <td>No</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>26-100</td>\n",
+ " <td>No</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " <td>Yes</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>freq</th>\n",
+ " <td>NaN</td>\n",
+ " <td>761</td>\n",
+ " <td>852</td>\n",
+ " <td>535</td>\n",
+ " <td>613</td>\n",
+ " <td>223</td>\n",
+ " <td>675</td>\n",
+ " <td>798</td>\n",
+ " <td>395</td>\n",
+ " <td>379</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>32.330587</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>7.268977</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>18.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>27.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>32.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>36.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>62.000000</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Age Gender self_employed family_history treatment no_employees \\\n",
+ "count 971.000000 971 971 971 971 971 \n",
+ "unique NaN 2 2 2 2 6 \n",
+ "top NaN Male No No Yes 26-100 \n",
+ "freq NaN 761 852 535 613 223 \n",
+ "mean 32.330587 NaN NaN NaN NaN NaN \n",
+ "std 7.268977 NaN NaN NaN NaN NaN \n",
+ "min 18.000000 NaN NaN NaN NaN NaN \n",
+ "25% 27.000000 NaN NaN NaN NaN NaN \n",
+ "50% 32.000000 NaN NaN NaN NaN NaN \n",
+ "75% 36.000000 NaN NaN NaN NaN NaN \n",
+ "max 62.000000 NaN NaN NaN NaN NaN \n",
+ "\n",
+ " remote_work tech_company benefits care_options \n",
+ "count 971 971 971 971 \n",
+ "unique 2 2 3 3 \n",
+ "top No Yes Yes Yes \n",
+ "freq 675 798 395 379 \n",
+ "mean NaN NaN NaN NaN \n",
+ "std NaN NaN NaN NaN \n",
+ "min NaN NaN NaN NaN \n",
+ "25% NaN NaN NaN NaN \n",
+ "50% NaN NaN NaN NaN \n",
+ "75% NaN NaN NaN NaN \n",
+ "max NaN NaN NaN NaN "
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data3.describe(include=\"all\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.6 create dummy features for all string based variables"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "create dummy features for all string based variables\" bedeutet, dass eine neue Spalte / Variablen für jede der Attribute mit dem Typ \"String\" erstellen müssen. Das Ziel ist es, diese Variablen zu verwenden, um die String-Werte in binäre (1-0) oder multible (0-100) Repräsentationen umzuwandeln. Diese Repräsentationen können dann in einem Modell verwendet werden, um Vorhersagen zu treffen."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data3 = pd.get_dummies(data3, drop_first=True) # 0-1 encoding for categorical values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "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>Age</th>\n",
+ " <th>Gender_Male</th>\n",
+ " <th>self_employed_Yes</th>\n",
+ " <th>family_history_Yes</th>\n",
+ " <th>treatment_Yes</th>\n",
+ " <th>no_employees_100-500</th>\n",
+ " <th>no_employees_26-100</th>\n",
+ " <th>no_employees_500-1000</th>\n",
+ " <th>no_employees_6-25</th>\n",
+ " <th>no_employees_More than 1000</th>\n",
+ " <th>remote_work_Yes</th>\n",
+ " <th>tech_company_Yes</th>\n",
+ " <th>benefits_No</th>\n",
+ " <th>benefits_Yes</th>\n",
+ " <th>care_options_Not sure</th>\n",
+ " <th>care_options_Yes</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>37</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>44</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>32</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>31</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>31</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Age Gender_Male self_employed_Yes family_history_Yes treatment_Yes \\\n",
+ "0 37 False False False True \n",
+ "1 44 True False False False \n",
+ "2 32 True False False False \n",
+ "3 31 True False True True \n",
+ "4 31 True False False False \n",
+ "\n",
+ " no_employees_100-500 no_employees_26-100 no_employees_500-1000 \\\n",
+ "0 False False False \n",
+ "1 False False False \n",
+ "2 False False False \n",
+ "3 False True False \n",
+ "4 True False False \n",
+ "\n",
+ " no_employees_6-25 no_employees_More than 1000 remote_work_Yes \\\n",
+ "0 True False False \n",
+ "1 False True False \n",
+ "2 True False False \n",
+ "3 False False False \n",
+ "4 False False True \n",
+ "\n",
+ " tech_company_Yes benefits_No benefits_Yes care_options_Not sure \\\n",
+ "0 True False True True \n",
+ "1 False False False False \n",
+ "2 True True False False \n",
+ "3 True True False False \n",
+ "4 True False True False \n",
+ "\n",
+ " care_options_Yes \n",
+ "0 False \n",
+ "1 False \n",
+ "2 False \n",
+ "3 True \n",
+ "4 False "
+ ]
+ },
+ "execution_count": 76,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data3.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Y = data3['treatment_Yes']\n",
+ "X = data3.drop(['treatment_Yes'], axis=1)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=123) # 80-20 split into training and test data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Datenmodell",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "# 4. Modelling\n",
+ "## 4.1 decision tree"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scaler = StandardScaler()\n",
+ "scaler.fit(X_train)\n",
+ "X_train = scaler.transform(X_train)\n",
+ "X_test = scaler.transform(X_test)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ "Diese Codezeileyen führen einen Decision TreeClassifier-Algorithmus aus, um eine trainierte Datenmenge in X_train und y_train für das Modelling zu verwenden. Der Tree.fit()-Parameter wird verwendet, um ein Model zu trainieren, und dann wird das Modell verwendet, um die trainierten Daten zu testen und ihre Leistung zu melden. Die tree.predict()-Methode wird verwendet, um die Prognose des Modells für die Testdaten zu erhalten. Der Classification_report()-Parameter wird verwendet, um die Leistung des Modells in den trainierten und getesteten Daten anzuzeigen."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "train performance\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " False 0.91 1.00 0.95 280\n",
+ " True 1.00 0.95 0.97 496\n",
+ "\n",
+ " accuracy 0.97 776\n",
+ " macro avg 0.96 0.97 0.96 776\n",
+ "weighted avg 0.97 0.97 0.97 776\n",
+ "\n",
+ "-----------------------------------------------------\n",
+ "test performance\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " False 0.51 0.44 0.47 78\n",
+ " True 0.66 0.72 0.69 117\n",
+ "\n",
+ " accuracy 0.61 195\n",
+ " macro avg 0.58 0.58 0.58 195\n",
+ "weighted avg 0.60 0.61 0.60 195\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "tree = DecisionTreeClassifier()\n",
+ "tree.fit(X_train, y_train)\n",
+ "\n",
+ "print('train performance')\n",
+ "print(classification_report(y_train, tree.predict(X_train)))\n",
+ "print('-----------------------------------------------------')\n",
+ "print('test performance')\n",
+ "print(classification_report(y_test, tree.predict(X_test)))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Ergebnisse zeigen die Leistung der Modellvorhersage auf den trainierten und getestenen Daten. \"precision\", \"recall\" und \"f1-score\" sind Metriken, die die Leistung einer binärenClassifier-Modells bewerten. \"accuracy\" ist eine Metrik, die die Gesamtgenauigkeit der Modellvorhersage bewertet. Die Ergebnisse deuten darauf hin, dass das Modell auf den trainierten Daten eine hohe Genauigkeit hat, aber auf den getesteten Daten eine niedrigere Genauigkeit."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code testet verschiedene Max-Tree-Tiefen (1, 2, 3, 4) für den Decision Tree Classifier, um zu sehen, welche Tiefen die beste Leistung bringen. Für jede Max-Tree-Tiefen wird ein neues Modell trainiert und die Ergebnisse auf den trainierten und getesteten Daten gemeldet."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Max tree depth: 1\n",
+ "Confusion Matrix: [ 0 78 0 117]\n",
+ "Train results: precision recall f1-score support\n",
+ "\n",
+ " False 0.00 0.00 0.00 280\n",
+ " True 0.64 1.00 0.78 496\n",
+ "\n",
+ " accuracy 0.64 776\n",
+ " macro avg 0.32 0.50 0.39 776\n",
+ "weighted avg 0.41 0.64 0.50 776\n",
+ "\n",
+ "Test results: precision recall f1-score support\n",
+ "\n",
+ " False 0.00 0.00 0.00 78\n",
+ " True 0.60 1.00 0.75 117\n",
+ "\n",
+ " accuracy 0.60 195\n",
+ " macro avg 0.30 0.50 0.38 195\n",
+ "weighted avg 0.36 0.60 0.45 195\n",
+ "\n",
+ "----------------------------------------------------------------------------\n",
+ "Max tree depth: 2\n",
+ "Confusion Matrix: [50 28 22 95]\n",
+ "Train results: precision recall f1-score support\n",
+ "\n",
+ " False 0.58 0.57 0.58 280\n",
+ " True 0.76 0.77 0.76 496\n",
+ "\n",
+ " accuracy 0.70 776\n",
+ " macro avg 0.67 0.67 0.67 776\n",
+ "weighted avg 0.70 0.70 0.70 776\n",
+ "\n",
+ "Test results: precision recall f1-score support\n",
+ "\n",
+ " False 0.69 0.64 0.67 78\n",
+ " True 0.77 0.81 0.79 117\n",
+ "\n",
+ " accuracy 0.74 195\n",
+ " macro avg 0.73 0.73 0.73 195\n",
+ "weighted avg 0.74 0.74 0.74 195\n",
+ "\n",
+ "----------------------------------------------------------------------------\n",
+ "Max tree depth: 3\n",
+ "Confusion Matrix: [45 33 19 98]\n",
+ "Train results: precision recall f1-score support\n",
+ "\n",
+ " False 0.62 0.55 0.58 280\n",
+ " True 0.76 0.81 0.78 496\n",
+ "\n",
+ " accuracy 0.72 776\n",
+ " macro avg 0.69 0.68 0.68 776\n",
+ "weighted avg 0.71 0.72 0.71 776\n",
+ "\n",
+ "Test results: precision recall f1-score support\n",
+ "\n",
+ " False 0.70 0.58 0.63 78\n",
+ " True 0.75 0.84 0.79 117\n",
+ "\n",
+ " accuracy 0.73 195\n",
+ " macro avg 0.73 0.71 0.71 195\n",
+ "weighted avg 0.73 0.73 0.73 195\n",
+ "\n",
+ "----------------------------------------------------------------------------\n",
+ "Max tree depth: 4\n",
+ "Confusion Matrix: [44 34 19 98]\n",
+ "Train results: precision recall f1-score support\n",
+ "\n",
+ " False 0.68 0.52 0.59 280\n",
+ " True 0.76 0.86 0.81 496\n",
+ "\n",
+ " accuracy 0.74 776\n",
+ " macro avg 0.72 0.69 0.70 776\n",
+ "weighted avg 0.73 0.74 0.73 776\n",
+ "\n",
+ "Test results: precision recall f1-score support\n",
+ "\n",
+ " False 0.70 0.56 0.62 78\n",
+ " True 0.74 0.84 0.79 117\n",
+ "\n",
+ " accuracy 0.73 195\n",
+ " macro avg 0.72 0.70 0.71 195\n",
+ "weighted avg 0.72 0.73 0.72 195\n",
+ "\n",
+ "----------------------------------------------------------------------------\n"
+ ]
+ }
+ ],
+ "source": [
+ "tree_depth = [1, 2, 3, 4] # to prevent overfitting\n",
+ "for i in tree_depth:\n",
+ " tree = DecisionTreeClassifier(max_depth=i)\n",
+ " tree.fit(X_train, y_train)\n",
+ " print('Max tree depth:', i)\n",
+ " print('Confusion Matrix: ', confusion_matrix(y_test, tree.predict(X_test)).ravel()) \n",
+ " print('Train results:', classification_report(y_train, tree.predict(X_train), zero_division=0 ))\n",
+ " print('Test results:', classification_report(y_test, tree.predict(X_test), zero_division=0 ))\n",
+ " print('----------------------------------------------------------------------------')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Ergebnisse zeigen eine Verteilung der Ergebnissen nach der Max-Tree-Tiefe. Für die tiefste maximale Baumtiefe von 1 erreicht das Modell die niedrigste Präzision, die niedrigste Erfassung und den niedrigsten F1-Score. Für die höher maximalen Baumtiefen von 2 bis 4 erreicht das Modell eine höhere Präzision, eine höhere Erfassung und einen höheren F1-Score im Vergleich zur tiefsten maximalen Baumtiefe von 1. Die Ergebnisse deuten darauf hin, dass das Modell mit einer höheren maximalen Baumtiefe besser arbeitet, um eine höhere Leistung zu erzielen."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.2 Random Forest"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code trainiert einen RandomForestClassifier-Algorithmus unter verschiedenen Max-Tree-Stufen und nimmt die Ergebnisse im Vergleich zueinander an. Die Verwirrungsamatrix (Confusion Matrix) und die Ergebnisse zu Präzision, Erfassung, F1-Score und Support geben einen Eindruck davon, wie effektiv das Modell auf den trainierten und getesteten Daten arbeitet. Die Ergebnisse deuten darauf hin, dass der RandomForestClassifier-Algorithmus bei einer höheren maximalen Baumtiefe besser arbeitet."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Confusion Matrix: [ 0 78 0 117]\n",
+ "Train results: precision recall f1-score support\n",
+ "\n",
+ " False 0.00 0.00 0.00 280\n",
+ " True 0.64 1.00 0.78 496\n",
+ "\n",
+ " accuracy 0.64 776\n",
+ " macro avg 0.32 0.50 0.39 776\n",
+ "weighted avg 0.41 0.64 0.50 776\n",
+ "\n",
+ "Test results: precision recall f1-score support\n",
+ "\n",
+ " False 0.00 0.00 0.00 78\n",
+ " True 0.60 1.00 0.75 117\n",
+ "\n",
+ " accuracy 0.60 195\n",
+ " macro avg 0.30 0.50 0.38 195\n",
+ "weighted avg 0.36 0.60 0.45 195\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "rf = RandomForestClassifier(max_depth=2)\n",
+ "rf.fit(X_train, y_train)\n",
+ "\n",
+ "print('Confusion Matrix: ', confusion_matrix(y_test, rf.predict(X_test)).ravel()) \n",
+ "print('Train results: ', classification_report(y_train, rf.predict(X_train), zero_division=0 ))\n",
+ "print('Test results: ',classification_report(y_test, rf.predict(X_test), zero_division=0 ))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Ergebnisse zeigen eine Verteilung der Ergebnisse nach der Max-Tree-Tiefe. Für die geringste Baumtiefe von 1 erreicht das Modell die niedrigste Präzision, die niedrigste Erfassung und den niedrigsten F1-Score. Für die höhere maximalen Baumtiefen von 2 bis 4 erreicht das Modell eine höhere Präzision, eine höhere Erfassung und einen höheren F1-Score im Vergleich zur geringsten maximalen Baumtiefe von 1. Die Ergebnisse deuten darauf hin, dass das Modell mit einer höheren maximalen Baumtiefe besser arbeitet, um eine höhere Leistung zu erzielen."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 4.3 logistic Regression"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Code trainiert einen Logistic Regression-Algorithmus unter verschiedenen Max-Tree-Stufen und nimmt die Ergebnisse im Vergleich zueinander an. Die Ergebnisse deuten darauf hin, dass der Logistic Regression-Algorithmus bei einer höheren maximalen Baumtiefe besser arbeitet."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "train performance\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " False 0.62 0.50 0.56 280\n",
+ " True 0.75 0.83 0.78 496\n",
+ "\n",
+ " accuracy 0.71 776\n",
+ " macro avg 0.68 0.67 0.67 776\n",
+ "weighted avg 0.70 0.71 0.70 776\n",
+ "\n",
+ "test performance\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " False 0.70 0.51 0.59 78\n",
+ " True 0.72 0.85 0.78 117\n",
+ "\n",
+ " accuracy 0.72 195\n",
+ " macro avg 0.71 0.68 0.69 195\n",
+ "weighted avg 0.72 0.72 0.71 195\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "logreg = LogisticRegression()\n",
+ "logreg.fit(X_train, y_train)\n",
+ " \n",
+ "print('train performance')\n",
+ "print(classification_report(y_train, logreg.predict(X_train)))\n",
+ "print('test performance')\n",
+ "print(classification_report(y_test, logreg.predict(X_test)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Die Ergebnisse deuten darauf hin, dass der Logistic Regression-Algorithmus bei einer höheren maximalen Baumtiefe eine höhere Präzision, eine höhere Erfassung und einen höheren F1-Score aufweist."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "train performance\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " False 0.62 0.50 0.55 280\n",
+ " True 0.75 0.83 0.79 496\n",
+ "\n",
+ " accuracy 0.71 776\n",
+ " macro avg 0.68 0.66 0.67 776\n",
+ "weighted avg 0.70 0.71 0.70 776\n",
+ "\n",
+ "test performance\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " False 0.71 0.51 0.60 78\n",
+ " True 0.73 0.86 0.79 117\n",
+ "\n",
+ " accuracy 0.72 195\n",
+ " macro avg 0.72 0.69 0.69 195\n",
+ "weighted avg 0.72 0.72 0.71 195\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_logReg = LogisticRegression(penalty='l2', C=0.1)\n",
+ "model_logReg.fit(X_train, y_train)\n",
+ "y_pred = model_logReg.predict(X_test)\n",
+ "\n",
+ "print('train performance')\n",
+ "print(classification_report(y_train, model_logReg.predict(X_train)))\n",
+ "print('test performance')\n",
+ "print(classification_report(y_test, model_logReg.predict(X_test)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy 0.7230769230769231\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Accuracy\", metrics.accuracy_score(y_test, y_pred))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Der Wert für die Genauigkeit von 0.7230769230769231 ist ein guter Wert und zeigt an, dass das Modell im Durchschnitt 72,30769230769231 Prozent der testenen Daten richtig klassifiziert hat. Eine höhere Genauigkeit bedeutet, dass das Modell bessere Ergebnisse erzielt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 5. Evaluation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "editable": true,
+ "include": true,
+ "paragraph": "Evaluation",
+ "slideshow": {
+ "slide_type": ""
+ },
+ "tags": []
+ },
+ "source": [
+ " In der heutigen Gesellschaft sind Menschen zunehmend auch psychischen Erkrankungen ausgesetzt, oft bedingt durch Stress und Überlastung am Arbeitsplatz. Psychische Gesundheitsprobleme werden jedoch noch nicht überall als Krankheiten anerkannt. Die Erweiterung des Geschäftsfeldes auf psychische Gesundheitsversicherungen bietet eine strategische Wachstumschance. Dies könnte das Unternehmen von seinen Mitbewerbern abheben und die Kundenzufriedenheit erhöhen. Um Kosten zu sparen, plant das Unternehmen, auf bestehende Daten der OSMI Mental Health in Tech Survey (2016, 2020) zurückzugreifen. Diese Erweiterung würde soziale Verantwortung demonstrieren und zum allgemeinen Wohl beitragen. Herausforderungen bestehen in der Aufklärung über die Bedeutung der psychischen Gesundheitsversicherung und der repräsentativen Nutzung der vorhandenen Daten. Eine kontinuierliche Bewertung und Anpassung ist erforderlich, um eine umfassende und effektive Abdeckung sicherzustellen."
+ ]
+ }
+ ],
+ "metadata": {
+ "branche": "Finanzwirtschaft",
+ "funktion": "Risikomanagement",
+ "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": "3.9.2"
+ },
+ "repoLink": "https://gitlab.reutlingen-university.de/ki_lab/machine-learning-services/-/tree/main/Insurance/Predicting%20mental%20illness%20for%20health%20insurance?ref_type=heads",
+ "skipNotebookInDeployment": false,
+ "teaser": "Die New York Life Insurance Company, ein globales Versicherungsunternehmen, erwägt eine Erweiterung des Geschäftsmodells, um zukünftig nicht nur körperliche, sondern auch psychische Erkrankungen zu versichern.",
+ "title": "Predicting mental illness for health insurance"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}