reutlingen-university/1_data_science/5_lernfall/main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72dba059",
   "metadata": {},
   "source": [
    "# Lernfall 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4105df59",
   "metadata": {},
   "source": [
    "## Datensatz laden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d7a53682",
   "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>Vertragsnummer</th>\n",
       "      <th>Jahr</th>\n",
       "      <th>Vertragsalter</th>\n",
       "      <th>Status</th>\n",
       "      <th>Marktzins</th>\n",
       "      <th>Tarifzins</th>\n",
       "      <th>Guthaben</th>\n",
       "      <th>Sparbeitrag</th>\n",
       "      <th>Beruf</th>\n",
       "      <th>Alter</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>gekuendigt</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3933.85</td>\n",
       "      <td>3619.0</td>\n",
       "      <td>angestellt</td>\n",
       "      <td>48</td>\n",
       "      <td>0.154465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>aktiv</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>523.93</td>\n",
       "      <td>482.0</td>\n",
       "      <td>angestellt</td>\n",
       "      <td>28</td>\n",
       "      <td>0.154465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>aktiv</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7281.81</td>\n",
       "      <td>6699.0</td>\n",
       "      <td>selbstaendig</td>\n",
       "      <td>22</td>\n",
       "      <td>0.231475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9997</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>aktiv</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8905.79</td>\n",
       "      <td>8193.0</td>\n",
       "      <td>angestellt</td>\n",
       "      <td>26</td>\n",
       "      <td>0.154465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9996</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>aktiv</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8566.65</td>\n",
       "      <td>7881.0</td>\n",
       "      <td>angestellt</td>\n",
       "      <td>30</td>\n",
       "      <td>0.154465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Vertragsnummer  Jahr  Vertragsalter      Status  Marktzins  Tarifzins  \\\n",
       "0           10000     1              1  gekuendigt        8.7        8.7   \n",
       "1            9999     1              1       aktiv        8.7        8.7   \n",
       "2            9998     1              1       aktiv        8.7        8.7   \n",
       "3            9997     1              1       aktiv        8.7        8.7   \n",
       "4            9996     1              1       aktiv        8.7        8.7   \n",
       "\n",
       "   Guthaben  Sparbeitrag         Beruf  Alter         p  \n",
       "0   3933.85       3619.0    angestellt     48  0.154465  \n",
       "1    523.93        482.0    angestellt     28  0.154465  \n",
       "2   7281.81       6699.0  selbstaendig     22  0.231475  \n",
       "3   8905.79       8193.0    angestellt     26  0.154465  \n",
       "4   8566.65       7881.0    angestellt     30  0.154465  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('../3_lernfall/VertragsdatenABCAG.csv', sep=',', decimal='.') # Daten einlesen\n",
    "\n",
    "# Erste 5 Zeilen anzeigen\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3ee9779",
   "metadata": {},
   "source": [
    "## Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ef9e13fc",
   "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>Vertragsnummer</th>\n",
       "      <th>Jahr</th>\n",
       "      <th>Vertragsalter</th>\n",
       "      <th>Marktzins</th>\n",
       "      <th>Tarifzins</th>\n",
       "      <th>Guthaben</th>\n",
       "      <th>Sparbeitrag</th>\n",
       "      <th>Alter</th>\n",
       "      <th>p</th>\n",
       "      <th>Beruf_arbeitslos</th>\n",
       "      <th>Beruf_selbstaendig</th>\n",
       "      <th>Status_aktiv</th>\n",
       "      <th>Status_gekuendigt</th>\n",
       "      <th>Status_verstorben</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>3933.85</td>\n",
       "      <td>3619.0</td>\n",
       "      <td>48</td>\n",
       "      <td>0.154465</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>523.93</td>\n",
       "      <td>482.0</td>\n",
       "      <td>28</td>\n",
       "      <td>0.154465</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7281.81</td>\n",
       "      <td>6699.0</td>\n",
       "      <td>22</td>\n",
       "      <td>0.231475</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9997</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8905.79</td>\n",
       "      <td>8193.0</td>\n",
       "      <td>26</td>\n",
       "      <td>0.154465</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9996</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8566.65</td>\n",
       "      <td>7881.0</td>\n",
       "      <td>30</td>\n",
       "      <td>0.154465</td>\n",
       "      <td>0</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": [
       "   Vertragsnummer  Jahr  Vertragsalter  Marktzins  Tarifzins  Guthaben  \\\n",
       "0           10000     1              1        8.7        8.7   3933.85   \n",
       "1            9999     1              1        8.7        8.7    523.93   \n",
       "2            9998     1              1        8.7        8.7   7281.81   \n",
       "3            9997     1              1        8.7        8.7   8905.79   \n",
       "4            9996     1              1        8.7        8.7   8566.65   \n",
       "\n",
       "   Sparbeitrag  Alter         p  Beruf_arbeitslos  Beruf_selbstaendig  \\\n",
       "0       3619.0     48  0.154465                 0                   0   \n",
       "1        482.0     28  0.154465                 0                   0   \n",
       "2       6699.0     22  0.231475                 0                   1   \n",
       "3       8193.0     26  0.154465                 0                   0   \n",
       "4       7881.0     30  0.154465                 0                   0   \n",
       "\n",
       "   Status_aktiv  Status_gekuendigt  Status_verstorben  \n",
       "0             0                  1                  0  \n",
       "1             1                  0                  0  \n",
       "2             1                  0                  0  \n",
       "3             1                  0                  0  \n",
       "4             1                  0                  0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One hot encoding für die Spalten \"Beruf\" und \"Status\" als 0 und 1\n",
    "data = pd.get_dummies(data, columns=['Beruf', 'Status'], drop_first=True)\n",
    "\n",
    "# False und True in der Spalte \"Beruf...\" und \"Status...\" in 0 und 1 umwandeln\n",
    "for column in data.columns:\n",
    "    if column.startswith('Beruf_') or column.startswith('Status_'):\n",
    "        data[column] = data[column].astype(int)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a24941",
   "metadata": {},
   "source": [
    "## Korrelationen darstellen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "81f1e895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pearson-Korrelation berechnen: Zielvariable ist \"Status_gekuendigt\"\n",
    "# \"Vertragsnummer\", \"Status_aktiv\", \"Status_verstorben\" und \"p\" sind nicht relevant für die Korrelation\n",
    "correlation_matrix = data.corr(method='pearson')\n",
    "correlation_with_target = correlation_matrix['Status_gekuendigt'].drop(['Status_gekuendigt', 'Vertragsnummer', 'Status_aktiv', 'Status_verstorben', 'p'])\n",
    "correlation_with_target = correlation_with_target.sort_values(ascending=False)\n",
    "\n",
    "# Korrelationen als Balkenplot darstellen\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=correlation_with_target.index, y=correlation_with_target.values)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Korrelationen mit der Zielvariable \"Status_gekuendigt\"')\n",
    "plt.ylabel('Korrelation')\n",
    "plt.xlabel('Merkmale')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76057188",
   "metadata": {},
   "source": [
    "## Train und Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "15f15627",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "data = data.drop(['Vertragsnummer', 'Status_aktiv', 'Status_verstorben', 'p'], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.drop(['Status_gekuendigt'], axis=1), data['Status_gekuendigt'], test_size=0.2, random_state=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3181f66d",
   "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>Jahr</th>\n",
       "      <th>Vertragsalter</th>\n",
       "      <th>Marktzins</th>\n",
       "      <th>Tarifzins</th>\n",
       "      <th>Guthaben</th>\n",
       "      <th>Sparbeitrag</th>\n",
       "      <th>Alter</th>\n",
       "      <th>Beruf_arbeitslos</th>\n",
       "      <th>Beruf_selbstaendig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>338034</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>75251.18</td>\n",
       "      <td>6369.0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144215</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>13919.79</td>\n",
       "      <td>6908.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44395</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>8135.75</td>\n",
       "      <td>3758.0</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3642</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>8.7</td>\n",
       "      <td>5546.96</td>\n",
       "      <td>5103.0</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234429</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9457.25</td>\n",
       "      <td>9050.0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Jahr  Vertragsalter  Marktzins  Tarifzins  Guthaben  Sparbeitrag  \\\n",
       "338034    10              9        5.3        5.4  75251.18       6369.0   \n",
       "144215     6              2        1.3        0.5  13919.79       6908.0   \n",
       "44395      3              2        3.1        5.4   8135.75       3758.0   \n",
       "3642       1              1        8.7        8.7   5546.96       5103.0   \n",
       "234429     8              1        4.5        4.5   9457.25       9050.0   \n",
       "\n",
       "        Alter  Beruf_arbeitslos  Beruf_selbstaendig  \n",
       "338034     55                 0                   0  \n",
       "144215     40                 0                   0  \n",
       "44395      36                 1                   0  \n",
       "3642       58                 0                   0  \n",
       "234429     26                 0                   0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "081a8c31",
   "metadata": {},
   "source": [
    "## Multikolinearität mit VIF prüfen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "59a84f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Multikolinearität mit VIF prüfen\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# VIF für alle Merkmale berechnen\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data['feature'] = X_train.columns\n",
    "vif_data['VIF'] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])]\n",
    "vif_data = vif_data.sort_values(by='VIF', ascending=False)\n",
    "\n",
    "# VIF-Werte mit Balkenplot darstellen\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=vif_data['feature'], y=vif_data['VIF'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('VIF-Werte der Merkmale')\n",
    "plt.ylabel('VIF')\n",
    "plt.xlabel('Merkmale')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4b9dc3a",
   "metadata": {},
   "source": [
    "## Löschen der Variablen mit hohem VIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8cf50a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loeschen von Merkmalen mit hohem VIF: > 10\n",
    "for feature in vif_data['feature'][vif_data['VIF'] > 10]:\n",
    "    X_train = X_train.drop(feature, axis=1)\n",
    "    X_test = X_test.drop(feature, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0360323",
   "metadata": {},
   "source": [
    "## Logistisches Regressionsmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "71580cbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yann\\anaconda3\\envs\\hft_ml\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.11743200814325243\n",
      "R^2 Score: 0.2643793012365582\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Vorhersagen auf dem Testdatensatz\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Modellbewertung\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f'Mean Squared Error: {mse}')\n",
    "print(f'R^2 Score: {r2}')\n",
    "\n",
    "# Feature Importances\n",
    "importances = model.coef_[0]\n",
    "feature_importances = pd.DataFrame({'Feature': X_train.columns, 'Importance': importances})\n",
    "feature_importances = feature_importances.sort_values(by='Importance', ascending=False)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=feature_importances['Feature'], y=feature_importances['Importance'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Feature Importances')\n",
    "plt.ylabel('Importance')\n",
    "plt.xlabel('Features')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80ac4986",
   "metadata": {},
   "source": [
    "## Backwards Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "df4f5ff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature \"Alter\" mit p-value 0.1767 wird entfernt\n",
      "Mean Squared Error (Selected Features): 0.11867930208885893\n",
      "R^2 Score (Selected Features): 0.256565969434285\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yann\\anaconda3\\envs\\hft_ml\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Backwards Selection der Variablen\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Funktion zur Durchführung der Backward Selection für logistische Regression\n",
    "def backward_selection(X, y, threshold_in=0.05, threshold_out=0.05):\n",
    "    initial_features = X.columns.tolist()\n",
    "    while len(initial_features) > 0:\n",
    "        X_with_intercept = sm.add_constant(X[initial_features])\n",
    "        p_values = sm.Logit(y, X_with_intercept).fit(disp=0).pvalues[1:]\n",
    "        max_p_value = p_values.max()\n",
    "        if max_p_value > threshold_out:\n",
    "            excluded_feature = p_values.idxmax()\n",
    "            print(f'Feature \"{excluded_feature}\" mit p-value {max_p_value:.4f} wird entfernt')\n",
    "            initial_features.remove(excluded_feature)\n",
    "        else:\n",
    "            break\n",
    "    return X[initial_features]\n",
    "\n",
    "# Backward Selection durchführen\n",
    "X_train_selected = backward_selection(X_train, y_train)\n",
    "X_test_selected = X_test[X_train_selected.columns]\n",
    "\n",
    "# Modell mit den ausgewählten Merkmalen trainieren\n",
    "model_selected = LogisticRegression()\n",
    "model_selected.fit(X_train_selected, y_train)\n",
    "\n",
    "# Vorhersagen auf dem Testdatensatz\n",
    "y_pred_selected = model_selected.predict(X_test_selected)\n",
    "\n",
    "# Modellbewertung\n",
    "mse_selected = mean_squared_error(y_test, y_pred_selected)\n",
    "r2_selected = r2_score(y_test, y_pred_selected)\n",
    "print(f'Mean Squared Error (Selected Features): {mse_selected}')\n",
    "print(f'R^2 Score (Selected Features): {r2_selected}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b5760f4",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ebbed45e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Konfusionsmatrix des Modells\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, y_pred_selected)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.title('Konfusionsmatrix des Modells')\n",
    "plt.xlabel('Vorhergesagte Klasse')\n",
    "plt.ylabel('Echte Klasse')\n",
    "plt.xticks(ticks=[0.5, 1.5], labels=['Nicht Gekündigt', 'Gekündigt'])\n",
    "plt.yticks(ticks=[0.5, 1.5], labels=['Nicht Gekündigt', 'Gekündigt'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b02f580",
   "metadata": {},
   "source": [
    "Das Modell hat wenig Probleme \"nicht gekündigte\" Verträge vorherzusagen aber hat Schwierigkeiten bei der Klassifizierung von \"Gekündigten Verträgen\" (untere Reihe)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a396e9ba",
   "metadata": {},
   "source": [
    "## ROC Kurve und AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e7f7976b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC-Kurve und AUC\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "y_pred_proba = model_selected.predict_proba(X_test_selected)[:, 1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.figure(figsize=(8, 6))\n",
    "RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc).plot()\n",
    "plt.plot([0, 1], [0, 1], 'k--', label='Zufallskurve')\n",
    "plt.title(f'ROC-Kurve (AUC = {roc_auc:.2f})')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65cd1881",
   "metadata": {},
   "source": [
    "Das Modell kann in 90 % der Fälle einen zufällig gewählten positiven Fall (z. B. \"gekündigt\") von einem zufällig gewählten negativen Fall (\"nicht gekündigt\") korrekt unterscheiden"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hft_ml",
   "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}