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-{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "BusinessUnderstanding",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "# Vorhersage von psychischen Erkrankungen für die Krankenversicherung\n",
- "# 1. Business Understanding\n",
- "Welche Personengruppe, bestimmt durch Alter, Geschlecht, Vorerkrankungen und berufliche Merkmale, muss im Verhältnis eine höhere Versicherungsprämie zahlen. Damit bei einem allgemeinen Schadensfall und Arbeitsausfall eine ausreichende Deckung gewährleistet ist, damit die Versicherung die zusätzlichen Behandlungskosten für psychische Erkrankungen finanzieren kann? \n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 2. Daten und Datenverständnis \n",
- "## 2.1. Import von relevanten Modulen "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "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"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.2. Daten einlesen "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "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",
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- "</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",
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- " <td>No</td>\n",
- " <td>Some of them</td>\n",
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- " <td>Yes</td>\n",
- " <td>No</td>\n",
- " <td>NA;;;;</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",
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- " <td>Don't know</td>\n",
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- " <td>No</td>\n",
- " <td>No</td>\n",
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- " <td>No</td>\n",
- " <td>NA;;;;</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",
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- " <td>No</td>\n",
- " <td>NA;;;;</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",
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- " <td>26-100</td>\n",
- " <td>...</td>\n",
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- " <td>Yes</td>\n",
- " <td>Some of them</td>\n",
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- " <td>Maybe</td>\n",
- " <td>No</td>\n",
- " <td>Yes</td>\n",
- " <td>NA;;;;</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",
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- " </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 NA;;;; \n",
- "1 Don't know No NA;;;; \n",
- "2 No No NA;;;; \n",
- "3 No Yes NA;;;; \n",
- "4 Don't know No NA;;;; \n",
- "\n",
- "[5 rows x 27 columns]"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data = pd.read_csv('https://storage.googleapis.com/ml-service-repository-datastorage/Predicting_clicks_on_online_advertising_by_Facebook_data.csv') # read data\n",
- "data.head(5)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.3. Deskriptive Analyse "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "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 745 non-null object\n",
- " 5 self_employed 1240 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",
- " 20 coworkers 1259 non-null object\n",
- " 21 supervisor 1259 non-null object\n",
- " 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;;;; 1259 non-null object\n",
- "dtypes: int64(1), object(26)\n",
- "memory usage: 265.7+ KB\n"
- ]
- }
- ],
- "source": [
- "data.info()"
- ]
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- " <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 745 \n",
- "unique 1246 NaN 49 49 47 \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 1240 1259 1259 995 1259 \n",
- "unique 3 2 2 5 8 \n",
- "top No No Yes Sometimes 6-25 \n",
- "freq 1094 767 636 464 289 \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 ... 6 5 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 4 3 3 3 \n",
- "top Some of them Yes No Maybe \n",
- "freq 774 516 1008 556 \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 1259 \n",
- "unique 4 3 161 \n",
- "top Don't know No NA;;;; \n",
- "freq 575 1074 1095 \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": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data.describe(include='all') #Alle Daten statistisch anschauen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "| Feature | Data Type|\n",
- "|-----|------|\n",
- "|Timestamp | str [ example: \"2014-08-28 10:00:48\" ] |\n",
- "|Age | int64 |\n",
- "|Gender | str [ example: \"male\" ] |\n",
- "|Country | str [ example: \"Mexico\" ] |\n",
- "|state | str [ example: \"TN\" ] |\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;;;;\" ] |\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) "
- ]
- },
- {
- "attachments": {},
- "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;;;;\" ] |"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 3. Datenaufbereitung \n",
- "## 3.1 Duplikate entfernen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "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": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data[data.duplicated(keep=False)] # show duplicates"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 3.2 Fehlende Daten entfernen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Timestamp 0\n",
- "Age 0\n",
- "Gender 0\n",
- "Country 0\n",
- "state 514\n",
- "self_employed 19\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;;;; 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data.isnull().sum() #count missing data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "data1 = data.drop(['Timestamp','state','comments;;;;'], axis =1)\n",
- "# delete features, that are not needed"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "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": "code",
- "execution_count": 10,
- "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": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data1.isnull().sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "data1 = data1.dropna(axis=0) # remove rows with missing data (in 'work_interfere)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "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": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data1.isnull().sum() # make sure there is no missing data now"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.3 Unerwünschte Merkmale entfernen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "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": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data1.columns.values "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "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": "code",
- "execution_count": 15,
- "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": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data1.columns.values # these features are left"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>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>3</td>\n",
- " <td>2</td>\n",
- " <td>2</td>\n",
- " <td>5</td>\n",
- " <td>8</td>\n",
- " <td>4</td>\n",
- " <td>2</td>\n",
- " <td>3</td>\n",
- " <td>4</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>632</td>\n",
- " <td>464</td>\n",
- " <td>229</td>\n",
- " <td>690</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 3 2 2 \n",
- "top NaN Male No No Yes \n",
- "freq NaN 481 870 546 632 \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 5 8 4 2 3 \n",
- "top Sometimes 26-100 No Yes Yes \n",
- "freq 464 229 690 815 406 \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 4 \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": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data1.describe(include='all')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
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- " dtype=int64)"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data1['Age'].values"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.4 Ausreißer entfernen"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "editable": true,
- "include": true,
- "paragraph": "DataUnderstanding",
- "slideshow": {
- "slide_type": ""
- },
- "tags": []
- },
- "source": [
- "einige Personen haben einen Trollwert für \"Alter\" eingegeben, diese Zeilen müssen entfernt werden \n",
- "das Alter sollte zwischen 16 und 70 Jahren liegen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='Age', ylabel='Count'>"
- ]
- },
- "execution_count": 18,
- "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.histplot(data = data1, x = 'Age', bins=\"sqrt\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:xlabel='Age', ylabel='Count'>"
- ]
- },
- "execution_count": 19,
- "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": [
- "data2 = data1[data1['Age']<70] \n",
- "data2 = data2[data2['Age']>16] \n",
- "sns.histplot(data2['Age'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "31.5"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data2['Age'].median(axis = 0) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "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": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data2.columns.values # these features are left"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.5 Daten bereinigen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "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": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data2.columns.values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "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": "code",
- "execution_count": 24,
- "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",
- "ostensibly male"
- ],
- "legendgroup": "",
- "name": "",
- "showlegend": true,
- "type": "pie",
- "values": [
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- " Age Gender self_employed family_history treatment \\\n",
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CwmrNnj1fDz/8gOrXb6AXX5wqSRo69DH17Rut9u3DlJubqxEjntYNN9ykJk2CtW3bV5o79x8qX76C1q//UAsXzlPHjp00atR4xcfHaebMuTpy5LCOHDmk9u2f1ejRz2v69KmaNWuGFi1aKh8fX/Xt20Pff79LderU08svT9LMmXMVEHC9TpxI0yOPDFSDBrdIkrZv/0aLF7+rWrVqKz4+TkuXvqXx4yepR48oZWScLpVIkzw41IZOXakT6dnuHgOw0tK4AcoUoQbgytCsWQtJUk5OjrZv/0ZnzpxxvQ8sJ+esfv75J4WH36Vx4ybqo4/WKTX1oL7/fpdycnKK3Z+Pj4/+8pcwSdL119+o4OBm8vP75QJPzZr+OnPmjL7/fqdOnjypMWOeK/La5OQkSVJQUGPVqlVbkhQY2EiffvqfUj9vyYNDDQAAeIaKFStKkgoLnTLGaN68f6pChQqSpNOnT6tcuXLas+dHjRkzXH373qfWrduoRYvbNGPGtGL35+vrW+SjMnx8Lswhp7NQderU1euvv+naduJEmqpWraaPPlqr8uXLu7b/sq+y+aP0/DIBAACwjre3twoKCops8/OrpCZNmuqdd96WJGVmZurxxwfpiy8+1Y4d3ygoqLH6979fLVrcps8/36jCwsL/7svngn1dSpMmTZWaelDbt38jSUpK2qP+/XvpxIm0Pzz3/4IragAAwDphYZ30xBMPKSfnbJHtL7wwWfHxcYqJ6af8/HzdeWdXdenSTadOndTGjZ/o/vv7yMfHV61a3a4zZzJ09my26tWrL29vbw0eHKNJk6aW6PjVqlXTlClxevXVWcrLy5MxhXr++Rd13XW//+efQkJaa9y4EYqP99WwYSMv+/zPcxhjyuZanZvxHjXg4pbGDVBaWqa7x7iAv39lK+dC6WB9Pdf/srZHj+7XddfVKeWJ7PXb8/XycvzuL0By6xMAAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAsRagBAABYis9RAwAA1qlcpYIqlPct9f1eaX/rmFADAADWqVDeV9Ejl5T6fkvyt45nzozVqVMnNXlynGvbV19t1t/+NlVvvrlUFSv6lfpcF8OtTwAAgF957LGntGfPj/rii88k/fLH4KdPn6oxY57/UyNN4ooaAABAERUrVtTIkeM0deqLatXqdi1YME/t24fpmmuu0eOPP6Tc3HO69tqqGjFirK6//ga9887bWrt2jby8HGrcuIlGjhxXarMQagAAAL9x++2hCg1tq5dfnqT9+/fptdcWasiQwYqNjdd1112nLVsSFRs7RTNmzNbbby/SypXr5OXlpZkzY5WWdlz+/rVKZQ5CDQAAoBhPPvmMeve+Ry+/PF3Hjx/V4cOpGj36Wdfj2dnZ8vHxUXBwMz38cIw6dOioqKg+pRZpEqEGAABQLD+/SqpUqbICAq5XVlaWrr/+Bi1atFSS5HQ6lZ5+SpI0deoMff/9Lm3evEnDhw/VhAkvqWXLVqUyA79MAAAAcAl16tTVmTNntGPHt5KkNWv+rYkTxyk9PV0DBvxV9es31MMPP6bbbw9VcnJSqR2XK2oAAMA653LztTRuQJns93KUK1dOL700TbNmTVdeXp4qVvTT+PGTVK1aNfXoEaXBg2NUvnwF1a59nbp3jyy1eQk1AABgncwz5y75eWd/huXLV7u+Dg5uptdfX3zBc/r1G6B+/Uo/KiVufQIAAFiLUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUAAABL8fEcAADAOtWuLSefcuVLfb8FeblKz8j73eccOXJYffrcq/j4ubr99jau7X/9a6TmzJmvgIDrS32uiyHUAACAdXzKldfXcQ+X+n5bjVwg6fdDTZJ8fHwUGztFixe/o4oV/Up9jpLi1icAAMBv1Kzpr9tvD9WcOa9c8Njixf/U/ff3UUxMP82ZEy+n01lmcxBqAAAAxXjyyWf01VeJ2rp1s2tbYuKX+uKLz7Rw4Vv65z+X6NChg1q58l9lNgOhBgAAUAw/v0oaNWq8YmOn6OzZbEnSN99s1Z13dlX58hXk4+OjiIh79fXXW8tsBkINAADgIlq3blPkFmhhoSnyuDGS01lQZscn1AAAAH7H+VugJ06kqVWrEK1f/6Fyc8+poKBACQn/1m23hZTZsfmtTwAAYJ2CvNz//oZm6e/3jzp/C/TZZ59Uu3YdlJmZqYceipHTWaDQ0Lbq3btfqc95HqEGAACs88tnnV36YzTKQkDA9Vq+fHWRba1bt9EXX2yTJA0c+LAGDiz9jw4pDrc+AQAALEWoAQAAWIpQAwAAsBShBgAA3MoYc+kneYDLOU9CDQAAuI2PTzllZ5/x+Fgzxig7+4x8fMr9odfxW58AAMBtqlXzV3p6mrKyTrt7lDLn41NO1ar5/7HXlNEsAAAAl+Tt7aOaNQPcPYa1uPUJAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWOqSn6OWmpqqu+++Ww0aNCiyfd68eQoIKN3PPUlNTVVMTIw++eSTUt0vAADAlahEH3hbq1YtrVq1qqxnAQAAwK9c9l8mOHHihCZMmKCjR4/K4XBo+PDhateunebMmaPDhw9rz549OnnypJ555hlt3rxZO3bsUKNGjRQfHy+n06mJEycqKSlJJ06cUL169TR37twS7R8AAOBqUaJQO378uHr06OH6PjIyUt9//7169+6t8PBwHT9+XNHR0Vq5cqUk6aefftKyZcv0zTff6MEHH9Tq1atVt25dde/eXXv27FFmZqZ8fX317rvvqrCwUA8++KA+/fRTNWnSxHWMKVOmFLv/SpUqlejEZo/pWfKfgsUKC/Ll5ePr7jHgYc7l5rt7BABACVz2rc/Q0FDt3btXs2fPliQVFBTo4MGDkqS//OUv8vHx0fXXXy9/f381bNhQklS7dm1lZGQoNDRUVatW1ZIlS7R3716lpKTo7NmzRfa/adOmYvffuHHjEp3YrnmjlHfmZImea7NWIxcoLS3T3WNYxd+/Mj8TAMBV4bJvfRYWFurNN99U1apVJUnHjh1TzZo1tX79evn6/v8VIB+fCw+xYcMGzZ49WzExMYqKilJ6erqMMSXaPwAAwNXisj+eo02bNlq6dKkk6eeff9a9996rnJycEr02MTFR3bp1U+/evVWzZk1t3bpVTqez1PYPAADgCS77itr48eM1YcIERUZGSpLi4uJK/P6xPn366LnnntO6detUrlw5tWjRQqmpqaW2fwAAAE/gML+95+gheI+a5+I9ap6LtfVsrK/nYm0vn5eXQzVqXPxCFH+ZAAAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAsRagBAABYilADAACwFKEGAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWIpQAwAAsBShBgAAYClCDQAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAsRagBAABYilADAACwFKEGAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWIpQAwAAsBShBgAAYClCDQAAwFI+7h6grDR9LNbdI5SKgrxcd48AAADcxGND7eTJLBUWGnePAQAAcNm49QkAAGApQg0AAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAsRagBAABYilADAACwFKEGAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWIpQAwAAsBShBgAAYClCDQAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYAAGAphzHGuHsIAAAA25zLzVfmmXNlegwvL4dq1Kh00cd9yvTobjR06kqdSM929xgAAOAKtTRugDJVtqF2Kdz6BAAAsBShBgAAYClCDQAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAsRagBAABYilADAACwFKEGAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWIpQAwAAsBShBgAAYClCDQAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAsRagBAABYilADAACwFKEGAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWIpQAwAAsFSZhFpqaqqCgoI0YcKEItt3796toKAgrVix4qKv7dy5s1JTU8tiLAAAgCtKmV1Rq1q1qj7//HM5nU7XtoSEBFWvXr2sDgkAAOBRfMpqx35+fmrUqJG2bt2qNm3aSJK+/PJLtWvXTpL09ttva9WqVcrJyZHD4dArr7yiBg0auF7vdDoVFxenr776Sk6nU1FRURo4cGBZjQsAAGCdMgs1SerWrZs+/PBDtWnTRjt37lRQUJCMMcrKytInn3yit956SxUqVNCsWbO0dOlSPf/8867XLlu2TJL0/vvvKy8vTw899JCCg4MVEhJSomPPHtOzLE4JAGCBwoJ8efn4unsMeLhzufnuHqFsQ61Tp0565ZVXVFhYqLVr16pbt25KSEhQpUqVNGPGDK1Zs0YpKSn6/PPP1bhx4yKvTUxM1O7du7V582ZJ0tmzZ7Vnz54Sh9queaOUd+ZkqZ8TAMD9Wo1coLS0THePgf/y96/MepSRMg21SpUqqVGjRvr666+1efNmDR8+XAkJCTpy5Ij69eun+++/X2FhYapZs6Z2795d5LVOp1MjRoxQly5dJEmnTp1SxYoVy3JcAAAAq5T5x3N069ZNM2bMUHBwsHx8funCihUrqk6dOho4cKCaN2+uzz77rMgvHUhSmzZttGzZMuXn5ys7O1vR0dHasWNHWY8LAABgjTK9oib9cvtz3Lhxevrpp13bfH19VVhYqO7du6tcuXJq1qyZkpKSiryuf//+2r9/v3r16qWCggJFRUUpNDS0rMcFAACwhsMYY9w9RFngPWoA4Ll4j5pdeI/a5fPycqhGjUoXf/xPnAUAAAB/AKEGAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWIpQAwAAsBShBgAAYClCDQAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAsRagBAABYilADAACwFKEGAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALEWoAQAAWIpQAwAAsBShBgAAYClCDQAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBShBoAAIClCDUAAABLEWoAAACWItQAAAAsRagBAABYilADAACwlMMYY9w9BAAAf0RBXq7SM/LcPQb+y9+/stLSMt09xhXJy8uhGjUqXfRxnz9xlj/VyZNZKiykQT0R/0HwXKytZ2N9gT+OW58AAACWItQAAAAsRagBAABYilADAACwFKEGAABgKUINAADAUoQaAACApQg1AAAASxFqAAAAliLUAAAALOWxf0LKy8vh7hFQhlhfz8XaejbW13OxtpfnUj83/ig7AACApbj1CQAAYClCDQAAwFKEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAEAAFiKUAMAALAUoQYAAGApQg0AAMBSHhdqq1evVvfu3XXXXXdpyZIl7h4HlykrK0v33HOPUlNTJUmbNm1SZGSkunTpovj4eNfzdu/erd69e6tr164aN26cCgoK3DUySmDu3LmKiIhQRESE4uLiJLG2nmTWrFnq3r27IiIi9MYbb0hifT1NbGysRo8eLenia3j48GENGDBAd999tx5//HFlZ2e7c+Qrn/EgR48eNZ06dTLp6ekmOzvbREZGmqSkJHePhT9o+/bt5p577jFNmjQxBw8eNDk5OaZjx47mwIEDJj8/3wwaNMhs3LjRGGNMRESE+fbbb40xxowZM8YsWbLEjZPj93z55ZemX79+Jjc31+Tl5ZmYmBizevVq1tZDbNmyxfTv39/k5+ebnJwc06lTJ7N7927W14Ns2rTJhIaGmlGjRhljLr6GjzzyiPnggw+MMcbMnTvXxMXFuWVeT+FRV9Q2bdqkNm3aqGrVqqpYsaK6du2qdevWuXss/EHLli3TCy+8oFq1akmSdu7cqTp16uimm26Sj4+PIiMjtW7dOh06dEjnzp1TixYtJElRUVGst8X8/f01evRolStXTr6+vmrQoIFSUlJYWw/RunVrLV68WD4+Pjp58qScTqfOnDnD+nqI06dPKz4+Xo899pgkXXQN8/PztXXrVnXt2rXIdlw+jwq148ePy9/f3/V9rVq1dOzYMTdOhMsxZcoUhYSEuL6/2Lr+dru/vz/rbbFbbrnF9R/1lJQUJSQkyOFwsLYexNfXV7Nnz1ZERITatm3Lv7seZMKECRo2bJiqVKki6cL/Lp9fw/T0dFWqVEk+Pj5FtuPyeVSoGWMu2OZwONwwCUrTxdaV9b4yJSUladCgQRo1apRuvvnmCx5nba9sQ4cOVWJioo4cOaKUlJQLHmd9rzzvvfeeAgIC1LZtW9c2/rv85/Fx9wClqXbt2tq2bZvr++PHj7tun+HKVbt2bZ04ccL1/fl1/e32tLQ01ttyX3/9tYYOHaqxY8cqIiJCX331FWvrIZKTk5WXl6fGjRvrmmuuUZcuXbRu3Tp5e3u7nsP6XpkSEhKUlpamHj16KCMjQ2fPnpXD4Sh2DatXr66srCw5nU55e3uztqXAo66otWvXTomJiTp16pRycnL00UcfKSwszN1j4X/UvHlz7du3T/v375fT6dQHH3ygsLAw3XDDDSpfvry+/vprSdLKlStZb4sdOXJEQ4YM0fTp0xURESGJtfUkqampGj9+vPLy8pSXl6cNGzaof//+rK8HeOONN/TBBx9o1apVGjp0qDp37qypU6cWu4a+vr4KCQlRQkJCke24fB53RW3YsGGKiYlRfn6+/vrXv6pZs2buHgv/o/Lly2vatGl66qmnlJubq44dO+ruu++WJE2fPl3jx49Xdna2br31VsXExLh5WlzMwoULlZubq2nTprm29e/fn7X1EB07dtSOHTvUs2dPeXt7q0uXLoqIiFD16tVZXw91sTV84YUXNHr0aP39739XQECAZs6c6eZJr2wOU9wNZQAAALidR936BAAA8CSEGgAAgKUINQAAAEsRagAAAJYi1AAAACxFqAFAGRk0aJBOnTrl7jEAXMEINQAoI19++aW7RwBwhSPUAFyVli9froiICEVGRiomJkZHjhzRu+++q3vuuUf33nuvBg0apH379kmSRo8erYULF7pe++vvO3furDlz5ig6OlqdOnVSXFycJGnMmDGSpAcffFBHjhz5k88OgKfwqL9MAAAl8eOPP2r69Ol6//33FRAQoEWLFmngwIEqLCzUu+++q+rVq2vFihUaMmSI1qxZc8n9nT17VkuXLtWxY8d011136b777tPUqVO1YsUKvfnmm6pevfqfcFYAPBFX1ABcdRITE9W+fXsFBARIkgYOHKjw8HB1797dFVVRUVE6duyYUlNTL7m/8PBwSb/8GbsaNWooIyOj7IYHcFUh1ABcdby9veVwOFzfnzt3rtggM8aooKBADodDv/5re/n5+UWeV758edfXv30uAPwvCDUAV53Q0FAlJibq+PHjkqR33nlHGzduVEJCguu3NP/1r3+patWqqlOnjqpVq6bvvvtOknTq1Clt27atRMfx9vZWQUFB2ZwEgKsC71EDcNUJCgrSiBEj9PDDD0uS/P399fHHH2v9+vV68MEHVVhYqOrVq2v+/Pny8vLSAw88oOeee05du3bVjTfeqNatW5foOHfddZeio6P12muvKTAwsCxPCYCHchiu0QMAAFiJW58AAACWItQAAAAsRagBAABYilADAACwFKEGAABgKUINAADAUoQaAACApf4P2TLXzDsyuI4AAAAASUVORK5CYII=",
- "text/plain": [
- "<Figure size 720x360 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": 38,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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OHwUFhcjLq+iRiXAFAACK5fTpFHl4eKhKlWry8nLIw8PDrfU4HJ5yOu2HK2OMzp1L0+nTKbr11hpFfh3bggAAoFhycrJUuXKwHA5vtwerkuTh4SF//1vkdOYU63WEKwAAUExGHh43R4S4nvB4c3QGAADgBiFcAQAAK0aPfkJnzpyxMtbRo7/omWfGWRnrWr7+epMWLlxgbTzCFQAAsOKbb7ZaG+v48WM6dOigtfGuZs+e3UpLO2ttPH5bEAAA/GbTp0+RJI0aNUJJSQfUpk177d+/T8OHP6GGDcM0a1aCkpOPKy/PqfbtoxUXN0SS9M47b+irrzYoJydHmZmZeuKJPykyMkrx8S8qJSVFY8Y8qXHj/qJRox5Ts2b3ateunXI6nXryyae1cuVyHTyYpLvvbqjnn58mT09P7dz5nf7+90RlZWXKw8NTQ4YMV6tWD2j16o/15Zf/Tx4enjpy5JC8vb01efIUZWZmaeXK5crPz5O/f4BGjHjiN/ei1IWr4OAAd5dQZoSEBLq7hDKFfhUdvSoe+lV0v7VXWdm5Sk/LslQNbPrLX57X6tUfa+7c1zRs2CDVq1dfU6fOkCSNGjVSf/jDw4qMjFJ2drbGjXtaNWvWUlhYI23fvk2vvvoP+fpW0Lp1a7Ro0QK1bt1WEyZM1uzZCZo161UdO3ZUx479osjIMZo48VnNnDlDc+a8orfeWiKHw1t9+3bX7t07VadOXU2fPkWzZr2qGjVu08mTKRo+fLDq179TkrRjx3/0zjvvq2rVapo9O0FLlvxTkydPUffuvXT27BkrwUoqheFq1IwVOnn6nLvLAACUQksSBihdhKuyoEmTeyRJmZmZ2rHjP0pLS3O9rykz87x++ulHtW/fQc8884LWrv1MR44c1u7dO5WZmVnoeA6HQ61aRUmSbrvtdjVq1ET+/r/ekLn11hClpaVp9+7vlZqaqkmT/lzgtfv375MkNWhwt6pWrSZJCg29Sxs3/j/r1y2VwnAFAADKPj8/P0lSfn6ejDFasOANVahQQZJ05swZ+fj4aO/eHzRp0lj94Q/91aJFhO655/d65ZWXCh3P27vgZ2o5HJdHmLy8fNWpc4def/1t17GTJ1NUuXKQ1q79VL6+vq7jv45VMl/2zBvaAQCAFV5eXnI6nQWO+fsHKCyssd57711JUnp6uh57bIg2bdqo7777jxo0uFv9+g3UPff8Xl99tcH1NTZeXo7LxrqWsLDGOnLksHbs+I8kad++verXr6dOnkwpdt2/BXeuAACAFVFRbfX440OVmXm+wPHnn39Rs2cnKC6ur3Jzc/Xggx0VHd1Zp06lasOGLzRw4ENyOLzVrFlzpaWd1fnz51S3bj15eXnp0UfjNGXKjCLNHxQUpGnTEjRv3hzl5OTImHw9++xUVa9+9a+uuffeFnrmmXGaPdtbo0ePv+7rv8DDGFMy98SuE++5AgBcyZKEAUpJSXd3GSUuJCSwVF/n8eMHVb16HXeX4VJS3y14QWHX6+npccVfwmNbEAAAwCLCFQAAgEWEKwAAAIsIVwAAABYRrgAAACwiXAEAAFjE51wBAAArAm+poAq+3tbHLWvfKUm4AgAAVlTw9dbD4xdbH7co3yk5a1a8Tp1K1YsvJriObdv2b7388gy9/fYS+fn5W6/rStgWBAAAZd7IkU9p794ftGnTl5J+/cLomTNnaNKkZ29osJK4cwUAAMoBPz8/jR//jGbMmKpmzZpr4cIFioyMUsWKFfXYY0OVnZ2lSpUqa9y4v+i222rqvffe1aefrpKnp4fuvjtM48c/Y60WwhUAACgXmjdvqZYt79P06VN08OABzZ+/SE888aji42erevXq2rp1i+Ljp+mVV+bq3Xff0ooVn8nT01OzZsUrJeWEQkKqWqmDcAUAAMqNJ5/8k3r37qrp02fqxInjOnr0iCZOHON6/ty5c3I4HGrUqImGDYvTAw+0Vq9eD1kLVhLhCgAAlCP+/gEKCAhUjRq3KSMjQ7fdVlNvvbVEkpSXl6fTp09JkmbMeEW7d+/Uv//9tcaOHaXnnvurmjZtZqUG3tAOAADKpTp17lBaWpq+++6/kqRVqz7SCy88o9OnT2vAgD6qV+93GjZspJo3b6n9+/dZm5c7VwAAwIqs7FwtSRhQIuNeDx8fH/31ry9pzpyZysnJkZ+fvyZPnqKgoCB1795Ljz4aJ1/fCqpWrbq6dIm1Vq+HMcZYG82CUTNW6OTpc+4uAwBQCi1JGKCUlHR3l1HiQkICS/V1Hj9+UNWr13F3GS4Oh6eczvwSG7+w6/X09FBwcECh57MtCAAAYBHhCgAAwCLCFQAAgEWEKwAAAIsIVwAAABYRrgAAACzic64AAIAVQZV85PDxtT6uMydbp8/mXPO8Y8eO6qGHumn27FfVvHmE63ifPrFKTHxNNWrcZr22whCuAACAFQ4fX32bMMz6uM3GL5R07XAlSQ6HQ/Hx0/TOO+/Jz8/fei1FwbYgAAAoN269NUTNm7dUYuLfLnvunXfe0MCBDykurq8SE2crLy+vRGogXAEAgHLlySf/pG3btuibb/7tOrZly2Zt2vSlFi36p954Y7F++eWwVqxYViLzE64AAEC54u8foAkTJis+fprOn//1K/X+859v9OCDHeXrW0EOh0MxMd307bfflMj8hCsAAFDutGgRUWB7MD+/4FcpGyPl5TlLZG7CFQAAKJcubA+ePJmiZs3u1bp1a5SdnSWn06nVqz/S739/b4nMy28LAgAAK5w52f/7m332x70eF7YHx4x5Uvff/4DS09M1dGic8vKcatnyPvXu3ddypb/yMMaYa59244yasUInT59zdxkAgFJoScIApaSku7uMEhcSEliqr/P48YOqXr2Ou8twcTg85XTml9j4hV2vp6eHgoMDCj2fbUEAAACLCFcAAAAWEa4AAAAsIlwBAIBi8pAxJfcep9Lket6aTrgCAADF4uNTQWfOnJTTmXtd4aOsMMbo3Lk0ORw+xXodH8UAAACKJSgoRBkZZ3XqVLLy80vm+/mKw9PTU/n5JXMnzeHwUVBQSPFeUyKVAACAcsvDw0OBgZUVGFjZ3aVIKn0fXcG2IAAAgEWEKwAAAIsIVwAAABYRrgAAACwiXAEAAFhEuAIAALCIcAUAAGBRscPVkSNH1KBBA23evLnA8Xbt2unIkSPWCgMAACiLruvOlbe3t5599lllZGTYrgcAAKBMu65wVbVqVd1///2Kj4+/7LkFCxaoS5cuio2N1UsvvaS8PPd/LD4AAMCNct1ffzNx4kTFxsZq8+bNatWqlSRp48aN+uKLL7R8+XI5HA499dRTeu+99zRgwIAijzt3Uo/rLQkAcBMICQm0Mo4zJ1unz+ZYGQu42HWHq4CAAP31r3/Vs88+q48++kiStHXrVsXExKhChQqSpN69e2vFihXFClc7F0xQTlrq9ZYFAECRNBu/UBLhCvb9pt8WjIyMLLA9WNg3Ujudzt8yBQAAQJnymz+KYeLEidq0aZNOnDihiIgIrVq1SllZWXI6nVq2bJkiIiJs1AkAAFAm/OZwdWF7MDc3V23atFGbNm3Uu3dvxcTEqGbNmho4cKCNOgEAAMoED2OMcXcRF+M9VwCAG6HZ+IVKSUl3dxmFCgkJLLW1lUbu6Jenp4eCgwMKf+6GVgIAAFDOEa4AAAAsIlwBAABYRLgCAACwiHAFAABgEeEKAADAIsIVAACARYQrAAAAiwhXAAAAFhGuAAAALCJcAQAAWES4AgAAsIhwBQAAYBHhCgAAwCLCFQAAgEWEKwAAAIsIVwAAABYRrgAAACwiXAEAAFhEuAIAALCIcAUAAGAR4QoAAMAiwhUAAIBFhCsAAACLCFcAAAAWEa4AAAAsIlwBAABYRLgCAACwiHAFAABgEeEKAADAIsIVAACARYQrAAAAiwhXAAAAFhGuAAAALCJcAQAAWES4AgAAsIhwBQAAYBHhCgAAwCLCFQAAgEWEKwAAAIsIVwAAABYRrgAAACwiXAEAAFhEuAIAALCIcAUAAGAR4QoAAMAiwhUAAIBFhCsAAACLCFcAAAAWOdxdwKUaj4x3dwkAgJuAMyfb3SWgnCp14So1NUP5+cbdZZR6ISGBSklJd3cZZQb9Kjp6VTz0q+joFW4WbAsCAABYRLgCAACwiHAFAABgEeEKAADAIsIVAACARYQrAAAAiwhXAAAAFhGuAAAALCJcAQAAWES4AgAAsIhwBQAAYBHhCgAAwCLCFQAAgEWEKwAAAIsIVwAAABYRrgAAACwiXAEAAFhEuAIAALCIcAUAAGAR4QoAAMAiwhUAAIBFhCsAAACLCFcAAAAWEa4AAAAsIlwBAABY5GGMMe4uAgAAwIas7Fylp2WV+Dyenh4KDg4o9DlHic9eTKNmrNDJ0+fcXQYAACiDliQMULpKPlxdDduCAAAAFhGuAAAALCJcAQAAWES4AgAAsIhwBQAAYBHhCgAAwCLCFQAAgEWEKwAAAIsIVwAAABYRrgAAACwiXAEAAFhEuAIAALCIcAUAAGAR4QoAAMAiwhUAAIBFhCsAAACLCFcAAAAWEa4AAAAsIlwBAABYRLgCAACwiHAFAABgEeEKAADAIsIVAACARYQrAAAAiwhXAAAAFhGuAAAALCJcAQAAWES4AgAAsIhwBQAAYBHhCgAAwCLCFQAAgEWEKwAAAIsIVwAAABYRrgAAACwiXAEAAFhEuAIAALCIcAUAAGAR4QoAAMAiwhUAAIBFhCsAAACLCFcAAAAWEa4AAAAsIlwBAABYRLgCAACwiHAFAABgEeEKAADAIsIVAACARYQrAAAAiwhXAAAAFl01XE2dOlWjRo0qcGzTpk1q3769MjIySrQwAACAsuiq4Wrs2LHatWuXvvjiC0nS+fPn9cILL2j69OkKCAi4IQUCAACUJY6rPenv768XX3xRf/nLXxQREaG5c+eqXbt2qlixovr376+srCwFBQVpypQpqlWrlt588019+OGH8vT0VJMmTTR16tQbdR0AAAClgocxxlzrpMmTJys9PV0///yzlixZogEDBmjBggW67bbb9NVXX2nRokVauHChIiMj9dVXX8nLy0tTpkzR448/rmrVqt2I60AZlu/MlafD291lAADKgazsXKWnZZX4PJ6eHgoOLnwXr0jhKiMjQ23atNG8efMUFBSkvn37qnbt2gWeX79+vR577DEdPXpU7du3V6dOnRQaGlrsYncumKCctNRivw5lV7PxC5WSkl6ic4SEBJb4HOUFvSoe+lV09Kro6FXxuKNfVwtXV90WvCAgIEC33HKLatasqYyMDN1+++1auXKlJCkvL08nT56UJM2fP187duzQl19+qWHDhmnmzJlq0aKFpcsAAAAo/Yr9UQz16tXT2bNntX37dknSsmXL9Oc//1mnTp1S586dFRoaqqefflqtWrXS3r17rRcMAABQmhXpztXFfHx8NGfOHE2bNk3Z2dkKCAhQfHy8qlSpon79+qlPnz6qWLGiatSooZ49e5ZEzQAAAKVWkcPVhY9jkKSmTZtq6dKll50zePBgDR482EphAAAAZRGf0A4AAGAR4QoAAMAiwhUAAIBFhCsAAACLCFcAAAAWEa4AAAAsIlwBAABYRLgCAACwiHAFAABgEeEKAADAIsIVAACARYQrAAAAiwhXAAAAFhGuAAAALCJcAQAAWES4AgAAsIhwBQAAYBHhCgAAwCLCFQAAgEWEKwAAAIsIVwAAABYRrgAAACwiXAEAAFhEuAIAALCIcAUAAGAR4QoAAMAiwhUAAIBFhCsAAACLCFcAAAAWEa4AAAAsIlwBAABYRLgCAACwiHAFAABgEeEKAADAIsIVAACARYQrAAAAiwhXAAAAFhGuAAAALCJcAQAAWES4AgAAsIhwBQAAYBHhCgAAwCLCFQAAgEWEKwAAAIsIVwAAABYRrgAAACwiXAEAAFhEuAIAALCIcAUAAGCRw90FXKrxyHh3l4AbzJmT7e4SAACwptSFq9TUDOXnG3eXUeqFhAQqJSXd3WUAAIBLsC0IAABgEeEKAADAIsIVAACARYQrAAAAiwhXAAAAFhGuAAAALCJcAQAAWES4AgAAsIhwBQAAYBHhCgAAwKJS9/U3np4e7i6hzKBXxUO/io5eFQ/9Kjp6VXT0qnhudL+uNp+HMYYv8gMAALCEbUEAAACLCFcAAAAWEa4AAAAsIlwBAABYRLgCAACwiHAFAABgEeEKAADAIsIVAACARYQrAAAAiwhXAAAAFpWKcPXxxx+rS5cu6tChgxYvXuzuckqduLg4xcTEqHv37urevbu+++47elaIjIwMde3aVUeOHJEkff3114qNjVV0dLRmz57tOm/Pnj3q3bu3OnbsqGeeeUZOp9NdJbvNpb2aNGmSoqOjXWvs888/l3TlHt5MXn31VcXExCgmJkYJCQmSWFtXUlivWFtXNmfOHHXp0kUxMTF68803JbG2rqSwXpXqtWXc7Pjx46Zt27bm9OnT5ty5cyY2Ntbs27fP3WWVGvn5+aZVq1YmNzfXdYyeXW7Hjh2ma9euJiwszBw+fNhkZmaa1q1bm0OHDpnc3FwzZMgQs2HDBmOMMTExMea///2vMcaYSZMmmcWLF7ux8hvv0l4ZY0zXrl1NcnJygfOu1sObxebNm03fvn1Ndna2ycnJMXFxcebjjz9mbRWisF6tXbuWtXUFW7duNf369TO5ubkmMzPTtG3b1uzZs4e1VYjCerV///5Svbbcfufq66+/VkREhCpXriw/Pz917NhRn332mbvLKjV+/vlneXh46NFHH1W3bt307rvv0rNCfPDBB3r++edVtWpVSdL333+vOnXqqFatWnI4HIqNjdVnn32mX375RVlZWbrnnnskSb169brpendpr86fP6+jR4/q2WefVWxsrObOnav8/Pwr9vBmEhISookTJ8rHx0fe3t6qX7++kpKSWFuFKKxXR48eZW1dQYsWLfTOO+/I4XAoNTVVeXl5SktLY20VorBe+fr6luq15bjhM17ixIkTCgkJcT2uWrWqvv/+ezdWVLqkpaXpvvvu0wsvvKCsrCzFxcWpc+fO9OwS06ZNK/C4sHWVnJx82fGQkBAlJyffsDpLg0t7lZqaqoiICE2dOlV+fn4aMWKEli5dKj8/v0J7eDO58847Xf+flJSk1atXa9CgQaytQhTWqyVLlmjbtm2srSvw9vbW3Llz9cYbb6hTp078vXUVl/YqLy+vVP+95fY7V8aYy455eHi4oZLSqWnTpkpISJCfn5+qVKmiPn36aO7cuZedR88KutK6Yr1drlatWpo3b56Cg4NVsWJFDRo0SBs3bqRXF9m3b5+GDBmiCRMmqHbt2pc9z9r6Pxf3ql69eqytaxg1apS2bNmiY8eOKSkp6bLnWVv/5+JebdmypVSvLbeHq2rVqunkyZOuxydOnHBtV0Davn27tmzZ4npsjFHNmjXp2TVcaV1dejwlJeWm793evXu1Zs0a12NjjBwOB382/9e3336rwYMHa+zYserZsydr6you7RVr68r279+vPXv2SJIqVqyo6Ohobd26lbVViMJ6tXr16lK9ttweru6//35t2bJFp06dUmZmptauXauoqCh3l1VqpKenKyEhQdnZ2crIyNCHH36ol19+mZ5dQ3h4uA4cOKCDBw8qLy9Pn3zyiaKiolSzZk35+vrq22+/lSStWLHipu+dMUbTp0/X2bNnlZubq/fff18dOnS4Yg9vJseOHdMTTzyhmTNnKiYmRhJr60oK6xVr68qOHDmiyZMnKycnRzk5OVq/fr369evH2ipEYb1q3rx5qV5bbn/PVbVq1TR69GjFxcUpNzdXffr0UZMmTdxdVqnRtm1bfffdd+rRo4fy8/P18MMPq1mzZvTsGnx9ffXSSy/pqaeeUnZ2tlq3bq1OnTpJkmbOnKnJkyfr3LlzatiwoeLi4txcrXvdddddGj58uPr37y+n06no6Gh17dpVkq7Yw5vFokWLlJ2drZdeesl1rF+/fqytQlypV6ytwrVu3dr1d7uXl5eio6MVExOjKlWqsLYuUVivnnzySQUFBZXateVhCtugBAAAwHVx+7YgAABAeUK4AgAAsIhwBQAAYBHhCgAAwCLCFQAAgEWEKwC4yJAhQ3Tq1Cl3lwGgDCNcAcBFNm/e7O4SAJRxhCsAZcbSpUsVExOj2NhYxcXF6dixY3r//ffVtWtXdevWTUOGDNGBAwckSRMnTtSiRYtcr734cbt27ZSYmKiHH35Ybdu2VUJCgiRp0qRJkqRHHnlEx44du8FXB6C8cPsntANAUfzwww+aOXOmPvzwQ9WoUUNvvfWWBg8erPz8fL3//vuqUqWKli9frieeeEKrVq265njnz5/XkiVLlJycrA4dOqh///6aMWOGli9frrfffltVqlS5AVcFoDzizhWAMmHLli2KjIxUjRo1JEmDBw9W+/bt1aVLF1cQ6tWrl5KTk3XkyJFrjte+fXtJv34FV3BwsM6ePVtyxQO4qRCuAJQJXl5e8vDwcD3OysoqNEQZY+R0OuXh4aGLv90rNze3wHm+vr6u/7/0XAD4LQhXAMqEli1basuWLTpx4oQk6b333tOGDRu0evVq12/3LVu2TJUrV1adOnUUFBSkXbt2SZJOnTql7du3F2keLy8vOZ3OkrkIADcF3nMFoExo0KCBxo0bp2HDhkmSQkJC9Pnnn2vdunV65JFHlJ+frypVqui1116Tp6enBg0apD//+c/q2LGjbr/9drVo0aJI83To0EEPP/yw5s+fr9DQ0JK8JADllIfhXjgAAIA1bAsCAABYRLgCAACwiHAFAABgEeEKAADAIsIVAACARYQrAAAAiwhXAAAAFv1/sEuaWkG2+6kAAAAASUVORK5CYII=",
- "text/plain": [
- "<Figure size 720x360 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": 39,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 720x360 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"
- ]
- },
- {
- "attachments": {},
- "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": 40,
- "metadata": {},
- "outputs": [],
- "source": [
- "data2 = data2.drop('work_interfere', axis=1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 720x360 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": 42,
- "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": 42,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {},
- "outputs": [],
- "source": [
- "data3 = data2.reset_index(drop = True) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "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": "code",
- "execution_count": 45,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
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- "<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": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data3.describe(include=\"all\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 2.6 Dummy-Merkmale für alle stringbasierten Variablen erstellen"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [],
- "source": [
- "data3 = pd.get_dummies(data3, drop_first=True) # 0-1 encoding for categorical values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "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>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
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- " <tr>\n",
- " <th>1</th>\n",
- " <td>44</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>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",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>32</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>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>31</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>31</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>0</td>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</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 0 0 0 1 \n",
- "1 44 1 0 0 0 \n",
- "2 32 1 0 0 0 \n",
- "3 31 1 0 1 1 \n",
- "4 31 1 0 0 0 \n",
- "\n",
- " no_employees_100-500 no_employees_26-100 no_employees_500-1000 \\\n",
- "0 0 0 0 \n",
- "1 0 0 0 \n",
- "2 0 0 0 \n",
- "3 0 1 0 \n",
- "4 1 0 0 \n",
- "\n",
- " no_employees_6-25 no_employees_More than 1000 remote_work_Yes \\\n",
- "0 1 0 0 \n",
- "1 0 1 0 \n",
- "2 1 0 0 \n",
- "3 0 0 0 \n",
- "4 0 0 1 \n",
- "\n",
- " tech_company_Yes benefits_No benefits_Yes care_options_Not sure \\\n",
- "0 1 0 1 1 \n",
- "1 0 0 0 0 \n",
- "2 1 1 0 0 \n",
- "3 1 1 0 0 \n",
- "4 1 0 1 0 \n",
- "\n",
- " care_options_Yes \n",
- "0 0 \n",
- "1 0 \n",
- "2 0 \n",
- "3 1 \n",
- "4 0 "
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data3.head()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {},
- "outputs": [],
- "source": [
- "Y = data3['treatment_Yes']\n",
- "X = data3.drop(['treatment_Yes'], axis=1)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "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"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# 4. Modellierung und Evaluation \n",
- "## Decision tree"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "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": "code",
- "execution_count": 52,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "train performance\n",
- " precision recall f1-score support\n",
- "\n",
- " 0 0.91 1.00 0.95 280\n",
- " 1 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",
- " 0 0.47 0.44 0.45 78\n",
- " 1 0.64 0.68 0.66 117\n",
- "\n",
- " accuracy 0.58 195\n",
- " macro avg 0.56 0.56 0.56 195\n",
- "weighted avg 0.57 0.58 0.58 195\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": "code",
- "execution_count": 54,
- "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",
- " 0 0.00 0.00 0.00 280\n",
- " 1 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",
- " 0 0.00 0.00 0.00 78\n",
- " 1 0.60 1.00 0.75 117\n",
- "\n",
- " accuracy 0.60 195\n",
- " macro avg 0.30 0.50 0.37 195\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",
- " 0 0.58 0.57 0.58 280\n",
- " 1 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",
- " 0 0.69 0.64 0.67 78\n",
- " 1 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",
- " 0 0.62 0.55 0.58 280\n",
- " 1 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",
- " 0 0.70 0.58 0.63 78\n",
- " 1 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",
- " 0 0.68 0.52 0.59 280\n",
- " 1 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",
- " 0 0.70 0.56 0.62 78\n",
- " 1 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('----------------------------------------------------------------------------')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Random Forest"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Confusion Matrix: [ 0 78 1 116]\n",
- "Train results: precision recall f1-score support\n",
- "\n",
- " 0 1.00 0.00 0.01 280\n",
- " 1 0.64 1.00 0.78 496\n",
- "\n",
- " accuracy 0.64 776\n",
- " macro avg 0.82 0.50 0.39 776\n",
- "weighted avg 0.77 0.64 0.50 776\n",
- "\n",
- "Test results: precision recall f1-score support\n",
- "\n",
- " 0 0.00 0.00 0.00 78\n",
- " 1 0.60 0.99 0.75 117\n",
- "\n",
- " accuracy 0.59 195\n",
- " macro avg 0.30 0.50 0.37 195\n",
- "weighted avg 0.36 0.59 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 ))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Logistische Regression"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "train performance\n",
- " precision recall f1-score support\n",
- "\n",
- " 0 0.62 0.50 0.56 280\n",
- " 1 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",
- " 0 0.69 0.51 0.59 78\n",
- " 1 0.72 0.85 0.78 117\n",
- "\n",
- " accuracy 0.71 195\n",
- " macro avg 0.71 0.68 0.68 195\n",
- "weighted avg 0.71 0.71 0.70 195\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": "code",
- "execution_count": 57,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "train performance\n",
- " precision recall f1-score support\n",
- "\n",
- " 0 0.62 0.50 0.55 280\n",
- " 1 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",
- " 0 0.71 0.51 0.60 78\n",
- " 1 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": 58,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy 0.7230769230769231\n"
- ]
- }
- ],
- "source": [
- "print(\"Accuracy\", metrics.accuracy_score(y_test, y_pred))"
- ]
- }
- ],
- "metadata": {
- "category": "Insurance",
- "interpreter": {
- "hash": "aab7ff84f4433dd8b68de441cd3c658d57659112bcb62d3bd6aa325045009f13"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.3"
- },
- "skipNotebookInDeployment": false,
- "title": "Predicting mental illness for health insurance"
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}