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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "72dba059",
+   "metadata": {},
+   "source": [
+    "# Lernfall 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4105df59",
+   "metadata": {},
+   "source": [
+    "## Datensatz laden"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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": 25,
+     "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": 26,
+   "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": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# One hot encoding für die Spalten \"Beruf\" und \"Status\"\n",
+    "data = pd.get_dummies(data, columns=['Beruf', 'Status'], drop_first=True)\n",
+    "\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6a24941",
+   "metadata": {},
+   "source": [
+    "## Korrelationen darstellen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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": 28,
+   "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": "markdown",
+   "id": "081a8c31",
+   "metadata": {},
+   "source": [
+    "## Multikolinearität mit VIF prüfen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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": 30,
+   "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": 31,
+   "id": "71580cbd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\yann.MSI\\anaconda3\\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": 32,
+   "id": "df4f5ff2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1819: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1+np.exp(-X))\n",
+      "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1872: RuntimeWarning: divide by zero encountered in log\n",
+      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Feature \"Alter\" mit p-value 0.1767 wird entfernt\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1819: RuntimeWarning: overflow encountered in exp\n",
+      "  return 1/(1+np.exp(-X))\n",
+      "c:\\Users\\yann.MSI\\anaconda3\\lib\\site-packages\\statsmodels\\discrete\\discrete_model.py:1872: RuntimeWarning: divide by zero encountered in log\n",
+      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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.MSI\\anaconda3\\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": 34,
+   "id": "ebbed45e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XdeHCBR09evSBzwUgdeCJBYBUYcCAAfLw8FD//v1ljNGMGTM0fPhw1ahRQ88++6x69OghHx8fjR07Vrt27dKMGTOS7a3Nd8agT5w4UXnz5lXRokW1efNmTZ8+3alfTEyMnn32WTVv3lwFCxZUUFCQtmzZoqVLl97zX6ElqUaNGqpZs6Z69+6ty5cvq0KFCtq5c6cGDhyoYsWKqVWrVi7XXKZMGdWtW1dFihRRaGio9u7dq2nTpjl++EwOdevW1dChQzVw4EBVrlxZ+/fv15AhQ5Q7d26nlZGCgoKUM2dOfffdd6pWrZrCwsKUPn16l98+Hh8fr2bNmslms2n69Ony9PTU5MmTFRERoaZNm2rdunXy8fFJ9NjQ0FD16NFDw4YNU/v27fXCCy/o+PHjGjRoUILhQV27dtXcuXNVqVIldevWTUWKFJHdbtexY8e0fPlyvfnmmypTpowGDBigEydOqFq1asqWLZsuXbqkjz/+WN7e3qpcubJL9xYREaEFCxbct4+Hh4dGjBihFi1aqG7dunr11Vd169YtjRw5UpcuXXIsEyxJQ4cOVa1atVSjRg29+eabio+P1/vvv6+AgADHal6SVKFCBb3yyit6+eWXtXXrVlWqVEkBAQGKjo52LO/csWPHJN9HvXr19NRTT6lkyZLKkCGD/vjjD40ePVo5c+bUE0884dJnAiD1IVgASDXefvtteXh46K233pLdbtfMmTP1448/auDAgYqMjJTdblfRokW1cOFC1a1bN1mv/eGHH0r6aznNq1evqmrVqlq0aJHTD8d+fn4qU6aMpk2bpqNHjyo2NlY5cuRQ79691atXr3ue22azacGCBRo0aJAmTZqkd955R+nTp1erVq307rvvujwcRZKqVq2qhQsXatSoUbp+/bqyZs2q1q1b66233nL5XPfy1ltv6fr165owYYJGjBihQoUKady4cZo/f36CSb8TJkxQz549Vb9+fd26dUtt2rRJ9L0g9zNw4ECtXbtWK1ascISB0NBQzZw5U5UqVVKvXr00evToex4/ZMgQBQQEaOzYsZo2bZoKFiyocePGJXi/Q0BAgNauXav33ntPX3zxhaKiouTv768cOXKoevXqjj/zMmXKaOvWrerdu7fOnTuntGnTqmTJkvrxxx9VuHBhl+4tqZo3b66AgAANHz5cTZs2laenp8qWLavVq1erfPnyjn53ltR9++231bRpU2XKlEmdOnXSjRs3HG9Bv2P8+PEqW7asxo8fr7Fjx8putytLliyqUKGCSpcu7VJ9zz77rObOnauvvvpKly9fVqZMmVSjRg31799f3t7eyfIZAEi5bObu56kAAAAA8ACYYwEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACx7LF+Q51+ss7tLAAAkwcUtn7q7BADAP/BLYmLgiQUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsMztwaJq1aq6dOlSgvbLly+ratWqj74gAAAAAC5ze7BYs2aNbt++naD95s2bWrt2rRsqAgAAAOAqL3ddeOfOnY7f79mzR6dPn3Zsx8fHa+nSpcqaNas7SgMAAADgIrcFi4iICNlsNtlstkSHPPn7+2vMmDFuqAwAAACAq9wWLKKiomSMUZ48ebR582ZlyJDBsc/Hx0fh4eHy9PR0V3kAAAAAXOC2YJEzZ05Jkt1ud1cJAAAAAJKJ24LFHQsXLky03Wazyc/PT/ny5VPu3LkfcVUAAAAAXOH2YNGwYUPZbDYZY5za77TZbDZVrFhRCxYsUGhoqJuqBAAAAHA/bl9udsWKFSpVqpRWrFihmJgYxcTEaMWKFSpdurQWLVqkn3/+WefPn1ePHj3cXSoAAACAe3D7E4s33nhDX3zxhcqXL+9oq1atmvz8/PTKK69o9+7dGj16tNq2bevGKgEAAADcj9ufWBw+fFjBwcEJ2oODg3XkyBFJ0hNPPKE///zzUZcGAAAAIIncHixKlCihnj176ty5c462c+fOqVevXipVqpQk6eDBg8qWLZu7SgQAAADwD9w+FGrChAlq0KCBsmXLpuzZs8tms+nYsWPKkyePvvvuO0nS1atX1b9/fzdXCgAAAOBebObvyzG5gTFGy5Yt04EDB2SMUcGCBVWjRg15eDzYAxX/Yp2TuUIAwMNwccun7i4BAPAP/JL4KCJFBIvkRrAAgNSBYAEAKV9Sg4VbhkJ98skneuWVV+Tn56dPPvnkvn27dOnyiKoCAAAA8KDc8sQid+7c2rp1q9KlS3fft2rbbDbHylCu4IkFAKQOPLEAgJQvRT+xiIqKSvT3AAAAAFInty83CwAAACD1c8sTi+7duye570cfffQQKwEAAACQHNwSLLZv3+60vW3bNsXHx6tAgQKSpAMHDsjT01MlSpRwR3kAAAAAXOSWYLF69WrH7z/66CMFBQVpypQpCg0NlSRdvHhRL7/8sp555hl3lAcAAADARW5/j0XWrFm1fPlyFS5c2Kl9165d+s9//qNTp065fE5WhQKA1IFVoQAg5UvqqlBun7x9+fJlnTlzJkH72bNndeXKFTdUBAAAAMBVbg8WjRo10ssvv6w5c+boxIkTOnHihObMmaN27dqpcePG7i4PAAAAQBK4ZY7F3caNG6cePXqoZcuWio2NlSR5eXmpXbt2GjlypJurAwAAAJAUbp9jcce1a9d0+PBhGWOUL18+BQQEPPC5mGMBAKkDcywAIOVL0W/eTkxAQICKFCni7jIAAAAAPAC3B4tr167pvffe06pVq3T27FnZ7Xan/UeOHHFTZQAAAACSyu3Bon379vrpp5/UqlUrZc6cWTabzd0lAQAAAHCR24PFkiVLtHjxYlWoUMHdpQAAAAB4QG5fbjY0NFRhYWHuLgMAAACABW4PFkOHDtWAAQN0/fp1d5cCAAAA4AG5fSjUhx9+qMOHDytjxozKlSuXvL29nfb/+uuvbqoMAAAAQFK5PVg0bNjQ3SUAAAAAsCjFvCAvOfGCPABIHXhBHgCkfEl9QZ7b51gAAAAASP3cPhTKw8Pjvu+uiI+Pf4TVAAAAAHgQbgkWVatW1bx585Q2bVrNnz/faV9sbKy2b9+uKVOmaPDgwe4oDwAAAICL3BIsihYt6lj9qUGDBgn2N2nSRIULF9asWbPUrl27R10eAAAAABe5ZfL25cuXFRwcfN8+hw8fVpEiRXTt2jWXz8/kbQBIHZi8DQApX4qevB0aGqqzZ8/ec/+NGzc0ZswYZcuW7RFWBQAAAOBBuWUo1I8//qiwsDBJf4WMuydvG2N05coVpUmTRl9//bU7ygMAAADgIrcEi8qVKzt+P3r0aKd9Hh4eypAhg8qUKaPQ0NBHXBkAAACAB+H25WaLFSumIkWKJLpvwYIFvJkbAAAASAXc/oK8mjVr6siRIwna586dqxYtWrihIgAAAACucnuw6Nixo6pVq6bo6GhH26xZs9S6dWtNnjzZfYUBAAAASDK3D4UaMGCAzp8/r+rVq2vt2rVaunSp2rdvr2nTpun55593d3kAAAAAksDtwUKSPv74Y7Vq1Uply5bVyZMnNWPGjERfnAcAAAAgZXJLsFi4cGGCtoYNG+qnn35Ss2bNZLPZHH3q16//qMsDLHvr1dp6+7XaTm2n/7ys3DX6Jeg75q2X1L5JRfUcOUefTl/jaPfx9tJ73RvphZol5O/nrdWbD6jru7N08uwlR59e7WrquWcKq0j+bLodF6fMlXolOH/2TKEa1edFVSmdXzduxmr20q3q89F8xcbFJ9v9AsDjYtvWLZo8cYL27tmlc+fOadQnn6lqteqO/devXdPoUR9q9Y8rFXPpkrJkzarmLVrpxZeaO/oMGTRAv2zaoHNnzypNmjQqGlFMXbv3UO48eR19urz+mvbv26cLF84rODhEZcqVU9fuPRQenvGR3i+QnNwSLO630tPEiRM1ceJESZLNZlN8PD/8IHXafeiU6rw2xrEdb0/4kvt6VYqo1NO5dOqusHDHyJ7Pq06lp9S67yRduHRN73VvpLmfvKbyzd+X/f/P5ePtqXkrtuuXnVFq07BcgnN4eNg075OO+vPiFVV7eZTC0gboqyGtZLPZ1P39b5PvZgHgMXHjxnUVKFBADRo11ptd/5tg/8j3h2vL5l/07nsjlSVrVm1cv17vDhusDOHherbqXwGkUKHCqlO3njJlzqzLMTH6/LMxeq1DO/2wfJU8PT0lSaVKl1X7V15T+gwZdPbMGX30wQj16PaGpn4z85HeL5Cc3BIs7Ha7Oy4LPFJx8XadOX/lnvuzZAjRqD4vqF6nzzR/TEenfcGBfopsWE7t3p6q1b/slyS1fXuqDi4ZqqplCmrlxr2SpGHjfpAktaxXJtFrVC/3pJ7Mk0lPPPeZos/FSJL6fDRfXwxuqYGffq8r125avk8AeJxUfKayKj5T+Z77f/tth+o1aKhSpf/63m3yYlPN+XaWdu/a5QgWTV5s6uifNWs2de7SVS80bqBTJ08qe44ckqRWbSIdfbJkyaq27Tqoa5fXFRsbK29v74dwZ8DD5/ZVoe528yY/5ODxkS9HBh1Z/o72Lhqkqe+9rFxZ0zn22Ww2TRjWWqOmrNLeI6cTHFvsyRzy8fZyBAhJij4Xo92HT6ls0dxJrqFMkdzaffiUI1RI0ooNe+Tn661iT2Z/wDsDgH+vYsWL66fVP+rMmTMyxmjzL5v0x9Eola9QMdH+169f13fz5ylrtmzKlClTon1iLl3S4sXfq2hEMUIFUjW3B4v4+HgNHTpUWbNmVWBgoOOdFv3799eECRPcXB3wYLbsOqr2/aepXqfP1GnoDGVMF6zVk99UWEiAJOnNl2soLt6uz2asSfT4TOmCdet2rC5dueHUfvb8FWVMF5zkOjKmC9bZvz01uXTlhm7djlWm9Ek/DwDgL336vq08efPpP1UrqWTEU+r0anv16z9QxUuUdOo3a8Y3KluymMqVKqb169dq/JeT5O3j49Rn1IcjVaZkhCpVKKPT0dH6+NOxj/JWgGTn9mDxzjvvaPLkyRoxYoR87vof7umnn9ZXX331j8ffunVLly9fdvpl7MzLgHstX79HC1bt0O5Dp7T6l/1q9N/PJf01ZKnYk9n1erMqemXg1y6f12azKeFMjfsziRxgs9kSbQcA3N/0b6Zp584d+vjTzzVj9ly92bOP3h06WJs2bnDqV7tufc2aO18Tp3ytHDlyquebXXXr1i2nPpFt22nWnPka9+VEeXh46O2+vWX4ckYq5vblZqdOnaovvvhC1apV02uvveZoL1KkiPbt2/ePxw8fPlyDBw92avPMWEremUsne63Ag7p+87Z2HzqlvDkyyG63KzwsUAd+GOLY7+Xlqfe6N1bnFs+qYJ2BOn3+snx9vJU2yN/pqUWGsEBt+i3hm+rv5cz5yyr1dE6ntrRB/vLx9tKZ85et3xgA/IvcvHlTn4wepVGffKpKlatIkvIXKKj9+/dqyqQJKluuvKNvUFCQgoKClDNnLhUpUlQVy5fWjytX6Lk6dR19QkPDFBoaply5citPnrz6T7XK2vnbDhWNKPaobw1IFm5/YnHy5Enly5cvQbvdbldsbOw/Ht+3b1/FxMQ4/fLKWOJhlAo8MB9vLxXMnVGn/4zR9MVbVOrF4Srz0nuOX6fOXtKoqStVr9NnkqTte4/pdmycqpUt6DhHpvTBKpw3izb9FpXk6/6yM0qF82ZxGvZUvdyTunkrVtv3Hk++GwSAf4G4uDjFxcXKw8Pm1O7h4Sn7Pz1pMEa3b9++z+6/jr9fHyClc/sTi8KFC2vt2rXKmdP5X1W//fZbFSv2z4nd19dXvr6+Tm02D89krRFw1fBujbT45991PPqiwsMC1bt9LQUF+Omb73/RhZhruhBzzal/bFy8zvx5WQf/OCtJunz1piYv2Kj3ujfW+ZhruhhzXcO7NdKuQ6f04y//e5KXPVOoQoPTKHvmUHl6eKhI/qySpMPHz+najdtauXGv9h45rQnDWqvfqAUKDUmj4d0aadL8DawIBQCJuH7tmo4dO+bYPnnihPbt3auQkBBlzpJFJUuV1kcfjJSvr58yZ8mibVu2aNHCBerRq48k6cTx41q29AeVK19BoaFhOnv2jCZN+FK+vn6qWOmv1aZ+37lTu37fqWLFSyg4JFgnjh/X2E8/UfbsOXhagVTN7cFi4MCBatWqlU6ePCm73a558+Zp//79mjp1qhYtWuTu8oAHkjVjWk0d/rLSpQ3QnxevavPvR1W5zYc6Fn0xyefo9cFcxcfb9fX77eTv663Vm/frlTemOd5hIUn9O9ZRq/plHdu/zOorSfpP+4+1dttB2e1Gjbt8rtF9m+rHSd1149b/XpAHAEho9+5dav9ya8f2ByOGS5LqN2ikoe++p/dHfqSPR3+kvr176HJMjDJnyaLOXbrphabNJEk+vj76ddtWfT1tii7HXFa69OlUokRJTf1mhtKl+2t1QD8/X61auVyffzZGN25cV/oMGVSh4jN6/4NRTvNNgdTGZlLALKFly5bp3Xff1bZt22S321W8eHENGDBA//nPfx7ofP7FOidzhQCAh+Hilk/dXQIA4B/4JfFRRIoIFsmNYAEAqQPBAgBSvqQGC7cPhbrjypUrTkuseXh4KDAw0I0VAQAAAEgqt60KtWPHDtWpU8exnSVLFoWGhjp+pU2bVlu2bHFXeQAAAABc4LYnFmPGjFHFihWd2qZNm6asWbPKGKOJEyfqk08+0bRp09xUIQAAAICkcluwWL9+vSIjI53aypYtqzx58kiS/P399eKLL7qhMgAAAACucttQqOPHjytHjhyO7SFDhih9+vSO7cyZM+vMmTPuKA0AAACAi9wWLHx9fXXixAnHdrdu3RQc/L+3Ax8/flxp0qRxR2kAAAAAXOS2YFGsWDEtWLDgnvvnzZuXpDdvAwAAAHA/t82x6NSpk1566SXlypVLHTt2lIfHXxknPj5eY8eO1ZgxYzR9+nR3lQcAAADABW59QV7v3r01cuRIBQUFKU+ePLLZbDp8+LCuXr2q7t27a+TIkQ90Xl6QBwCpAy/IA4CUL9W8eXvTpk2aMWOGDh48KEl64okn1KxZM5UtW/aBz0mwAIDUgWABAClfqnnzdtmyZS2FCAAAAADu57bJ2wAAAAAeHwQLAAAAAJYRLAAAAABYRrAAAAAAYJnbg0XVqlV16dKlBO2XL19W1apVH31BAAAAAFzm9mCxZs0a3b59O0H7zZs3tXbtWjdUBAAAAMBVbltudufOnY7f79mzR6dPn3Zsx8fHa+nSpcqaNas7SgMAAADgIrcFi4iICNlsNtlstkSHPPn7+2vMmDFuqAwAAACAq9wWLKKiomSMUZ48ebR582ZlyJDBsc/Hx0fh4eHy9PR0V3kAAAAAXOC2YJEzZ05Jkt1ud1cJAAAAAJKJ24LF3Q4cOKA1a9bo7NmzCYLGgAED3FQVAAAAgKRye7D48ssv1bFjR6VPn16ZMmWSzWZz7LPZbAQLAAAAIBVwe7AYNmyY3nnnHfXu3dvdpQAAAAB4QG5/j8XFixf1wgsvuLsMAAAAABa4PVi88MILWr58ubvLAAAAAGCBW4ZCffLJJ47f58uXT/3799emTZv09NNPy9vb26lvly5dHnV5AAAAAFxkM8aYR33R3LlzJ6mfzWbTkSNHXD6/f7HOLh8DAHj0Lm751N0lAAD+gV8SH0W45YlFVFSUOy4LAAAA4CFx+xwLAAAAAKmf24NFkyZN9N577yVoHzlyJKtFAQAAAKmE24PFTz/9pDp16iRor1Wrln7++Wc3VAQAAADAVW4PFlevXpWPj0+Cdm9vb12+fNkNFQEAAABwlduDxVNPPaVZs2YlaJ85c6YKFSrkhooAAAAAuMotq0LdrX///nr++ed1+PBhVa1aVZK0atUqzZgxQ99++62bqwMAAACQFG4PFvXr19eCBQv07rvvas6cOfL391eRIkW0cuVKVa5c2d3lAQAAAEgCt7wg72HjBXkAkDrwgjwASPmS+oI8t8+xAAAAAJD6uWUoVFhYmA4cOKD06dMrNDRUNpvtnn0vXLjwCCsDAAAA8CDcEixGjRqloKAgSdLo0aPdUQIAAACAZMQcCwCA2zDHAgBSPuZYAAAAAHhk3LbcrIeHx33nVkiSzWZTXFzcI6oIAAAAwINyW7CYP3/+Pfdt2LBBY8aM0WM4SgsAAAB4LLktWDRo0CBB2759+9S3b199//33atGihYYOHeqGygAAAAC4KkXMsTh16pQ6dOigIkWKKC4uTjt27NCUKVOUI0cOd5cGAAAAIAncGixiYmLUu3dv5cuXT7t379aqVav0/fff66mnnnJnWQAAAABc5LahUCNGjND777+vTJkyacaMGYkOjQIAAACQOrjtPRYeHh7y9/dX9erV5enpec9+8+bNc/ncvMcCAFIH3mMBAClfUt9j4bYnFq1bt/7H5WYBAAAApA5uCxaTJ09216UBAAAAJLMUsSoUAAAAgNSNYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAsgcOFrdv39b+/fsVFxeXnPUAAAAASIVcDhbXr19Xu3btlCZNGhUuXFjHjh2TJHXp0kXvvfdeshcIAAAAIOVzOVj07dtXv/32m9asWSM/Pz9He/Xq1TVr1qxkLQ4AAABA6uDl6gELFizQrFmzVLZsWdlsNkd7oUKFdPjw4WQtDgAAAEDq4PITi3Pnzik8PDxB+7Vr15yCBgAAAIB/D5eDRalSpbR48WLH9p0w8eWXX6pcuXLJVxkAAACAVMPloVDDhw9XrVq1tGfPHsXFxenjjz/W7t27tXHjRv30008Po0YAAAAAKZzLTyzKly+v9evX6/r168qbN6+WL1+ujBkzauPGjSpRosTDqBEAAABACmczxhh3F5Hc/It1dncJAIAkuLjlU3eXAAD4B35JHOPk8hOLX3/9Vb///rtj+7vvvlPDhg3Vr18/3b5929XTAQAAAHgMuBwsXn31VR04cECSdOTIETVt2lRp0qTRt99+q169eiV7gQAAAABSPpeDxYEDBxQRESFJ+vbbb1W5cmVNnz5dkydP1ty5c5O7PgAAAACpgMvBwhgju90uSVq5cqVq164tScqePbv+/PPP5K0OAAAAQKrgcrAoWbKkhg0bpmnTpumnn35SnTp1JElRUVHKmDFjshcIAAAAIOVzOViMHj1av/76qzp37qy33npL+fLlkyTNmTNH5cuXT/YCAQAAAKR8ybbc7M2bN+Xp6Slvb+/kOJ0lLDcLAKkDy80CQMr30JabPX78uE6cOOHY3rx5s7p27aqpU6emiFABAAAA4NFzOVg0b95cq1evliSdPn1aNWrU0ObNm9WvXz8NGTIk2QsEAAAAkPK5HCx27dql0qVLS5Jmz56tp556Shs2bHAsOQsAAADg38flYBEbGytfX19Jfy03W79+fUlSwYIFFR0dnbzVAQAAAEgVXA4WhQsX1rhx47R27VqtWLFCtWrVkiSdOnVK6dKlS/YCAQAAAKR8LgeL999/X+PHj1eVKlXUrFkzFS1aVJK0cOFCxxApAAAAAP8uD7TcbHx8vC5fvqzQ0FBH29GjR5UmTRqFh4cna4EPguVmASB1YLlZAEj5krrcbBK7OfP09HQKFZKUK1euBzkVAAAAgMfAAwWLOXPmaPbs2Tp27Jhu377ttO/XX39NlsIAAAAApB4uz7H45JNP9PLLLys8PFzbt29X6dKllS5dOh05ckTPPffcw6gRAAAAQArncrAYO3asvvjiC3366afy8fFRr169tGLFCnXp0kUxMTEPo0YAAAAAKZzLweLYsWMqX768JMnf319XrlyRJLVq1UozZsxI3uoAAAAApAouB4tMmTLp/PnzkqScOXNq06ZNkqSoqCg9wAJTAAAAAB4DLgeLqlWr6vvvv5cktWvXTt26dVONGjXUtGlTNWrUKNkLBAAAAJDyufweC7vdLrvdLi+vvxaUmj17ttatW6d8+fLptddek4+Pz0Mp1BW8xwIAUgfeYwEAKV9S32PxQC/IS+kIFgCQOhAsACDlS9YX5O3cuTPJFy5SpEiS+wIAAAB4PCQpWERERMhms/3j5Gybzab4+PhkKQwAAABA6pGkYBEVFfWw6wAAAACQiiUpWOTMmVMnT55U1qxZ79vvm2++Uc6cOZOlMAAAAACpR5KXm61Ro4YuXrx4z/3Tp0/Xyy+/nCxFAQAAAEhdkhwswsPDVatWLV27di3BvpkzZ6pNmzZ6//33k7U4AAAAAKlDkoPFokWLFB8frwYNGig2NtbRPnv2bLVu3VrDhw9Xt27dHkqRAAAAAFK2JAeLwMBALVmyRCdPntRLL70kY4y+/fZbtWzZUkOHDlWPHj0eZp0AAAAAUrAkvu7iLxkyZNDy5ctVsWJFVa9eXevWrdPAgQPVu3fvh1UfAAAAgFQgycHi7pfkjRw5Uq1bt1ajRo1Ur149p328IA8AAAD497GZf3rr3f/z8PBwvCTv7pfl/f33KeEFef7FOru7BABAElzc8qm7SwAA/AO/JD6KSPITC16SBwAAAOBekvzEIjWJjrnt7hIAAEkQF//Y/RUEAI+d7GG+SeqX5FWhAAAAAOBeCBYAAAAALCNYAAAAALCMYAEAAADAsgcKFnFxcVq5cqXGjx+vK1euSJJOnTqlq1evJmtxAAAAAFIHl968LUl//PGHatWqpWPHjunWrVuqUaOGgoKCNGLECN28eVPjxo17GHUCAAAASMFcfmLxxhtvqGTJkrp48aL8/f0d7Y0aNdKqVauStTgAAAAAqYPLTyzWrVun9evXy8fHx6k9Z86cOnnyZLIVBgAAACD1cPmJhd1uV3x8fIL2EydOKCgoKFmKAgAAAJC6uBwsatSoodGjRzu2bTabrl69qoEDB6p27drJWRsAAACAVMJmjDGuHHDq1Ck9++yz8vT01MGDB1WyZEkdPHhQ6dOn188//6zw8PCHVWuSRcfcdncJAIAkiIt36a8gAIAbZA/zTVI/l4OFJN24cUMzZ87Utm3bZLfbVbx4cbVo0cJpMrc7ESwAIHUgWABAyvfQgsXPP/+s8uXLy8vLed53XFycNmzYoEqVKrlyuoeCYAEAqQPBAgBSvocWLDw9PRUdHZ1gyNP58+cVHh6e6MTuR41gAQCpA8ECAFK+pAYLlydvG2Nks9kStJ8/f14BAQGung4AAADAYyDJ77Fo3LixpL9WgYqMjJSv7/+SS3x8vHbu3Kny5csnf4UAAAAAUrwkB4uQkBBJfz2xCAoKcpqo7ePjo7Jly6pDhw7JXyEAAACAFC/JwWLSpEmSpFy5cqlHjx4MewIAAADg8EDLzaZ0TN4GgNSBydsAkPI9tMnbZ86cUatWrZQlSxZ5eXnJ09PT6RcAAACAf58kD4W6IzIyUseOHVP//v2VOXPmRFeIAgAAAPDv4vJQqKCgIK1du1YREREPqSTrGAoFAKkDQ6EAIOV7aEOhsmfPrsdwWgYAAAAAC1wOFqNHj1afPn109OjRh1AOAAAAgNQoSXMsQkNDneZSXLt2TXnz5lWaNGnk7e3t1PfChQvJWyEAAACAFC9JwWL06NEPuQwAAAAAqRnvsQAAuA2TtwEg5Xtok7d/+OEHLVu2LEH78uXLtWTJEldPBwAAAOAx4HKw6NOnj+Lj4xO02+129enTJ1mKAgAAAJC6uBwsDh48qEKFCiVoL1iwoA4dOpQsRQEAAABIXVwOFiEhITpy5EiC9kOHDikgICBZigIAAACQurgcLOrXr6+uXbvq8OHDjrZDhw7pzTffVP369ZO1OAAAAACpg8urQsXExKhWrVraunWrsmXLJkk6ceKEnnnmGc2bN09p06Z9GHW6hFWhACB1YFUoAEj5kroq1AMtN2uM0YoVK/Tbb7/J399fRYoUUaVKlVwu8mEhWABA6kCwAICU76EGi5SOYAEAqQPBAgBSvmR/j0Xt2rUVExPj2H7nnXd06dIlx/b58+cTXS0KAAAAwOMvyU8sPD09FR0drfDwcElScHCwduzYoTx58kiSzpw5oyxZsiT6jotHjScWAJA68MQCAFK+ZH9i8ff88RiOoAIAAADwgFxebhYAAAAA/i7JwcJms8lmsyVoAwAAAACvpHY0xigyMlK+vn+Nsbp586Zee+01x9u2b9269XAqBAAAAJDiJXny9ssvv5ykE06aNMlSQcmBydsAkDoweRsAUj7eYwEASPEIFgCQ8iX7qlAAAAAAcC8ECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgmduDxc8//6y4uLgE7XFxcfr555/dUBEAAAAAV9mMMcadBXh6eio6Olrh4eFO7efPn1d4eLji4+NdPmd0zO3kKg8A8BDFxbv1ryAAQBJkD/NNUj+3P7EwxshmsyVoP3/+vAICAtxQEQAAAABXebnrwo0bN5Yk2Ww2RUZGytf3f0koPj5eO3fuVPny5d1VHgAAAAAXuC1YhISESPrriUVQUJD8/f0d+3x8fFS2bFl16NDBXeUBAAAAcIHbgsWkSZMkSbly5VKPHj0Y9gQAAACkYm6fvP0wMHkbAFIHJm8DQMqX1MnbbnticUexYsUSnbxts9nk5+enfPnyKTIyUs8++6wbqgMAAACQFG5fFapWrVo6cuSIAgIC9Oyzz6pKlSoKDAzU4cOHVapUKUVHR6t69er67rvv3F0qAAAAgHtw+1CoDh06KEeOHOrfv79T+7Bhw/THH3/oyy+/1MCBA7V48WJt3bo1SedkKBQApA4MhQKAlC+pQ6HcHixCQkK0bds25cuXz6n90KFDKlGihGJiYrRv3z6VKlVKV65cSdI5CRYAkDoQLAAg5Us1L8jz8/PThg0bErRv2LBBfn5+kiS73e70ngsAAAAAKYvbJ2//97//1WuvvaZt27apVKlSstls2rx5s7766iv169dPkrRs2TIVK1bMzZUCAAAAuBe3D4WSpG+++Uaffvqp9u/fL0kqUKCA/vvf/6p58+aSpBs3bjhWiUoKhkIBQOrAUCgASPlSzRyLh4FgAQCpA8ECAFK+VDPHAgAAAEDq55Y5FmFhYTpw4IDSp0+v0NDQRF+Qd8eFCxceYWUAAAAAHoRbgsWoUaMUFBQkSRo9erQ7SgAAAACQjJhjAQBwG+ZYAEDKl9Q5Fm55YnH58uUk9w0ODn6IlQAAAABIDm4JFmnTpr3vvIq7xcfHP+RqAAAAAFjllmCxevVqx++PHj2qPn36KDIyUuXKlZMkbdy4UVOmTNHw4cPdUR4AAAAAF7l9jkW1atXUvn17NWvWzKl9+vTp+uKLL7RmzRqXz8kcCwBIHZhjAQApX6p5j8XGjRtVsmTJBO0lS5bU5s2b3VARAAAAAFe5PVhkz55d48aNS9A+fvx4Zc+e3Q0VAQAAAHCVW+ZY3G3UqFF6/vnntWzZMpUtW1aStGnTJh0+fFhz5851c3UAAAAAksLtcywk6cSJExo7dqz27dsnY4wKFSqk11577YGfWDDHAgBSB+ZYAEDKl9Q5FikiWCQ3ggUApA4ECwBI+VL0C/L+7tKlS9q8ebPOnj0ru93utK9169ZuqgpIXufOntH4T0dp84Z1unXrlrLlyKlebw9WgScLJ+j74fDB+n7+HL3erZdeaNYqwX5jjHp37ajNG9dr6IjReqZKNce+pg1q6kz0Kaf+zVq31auduyX/TQHAY6ZFo1o6c/pUgvb6jZuqS8+3nNpGvTdEi7+bo45v9NTzLzl/V+/5/TdNHP+J9u3+XZ5e3sr7RAEN/2isfP38JEn9e/5Xhw7u16WLFxQUFKzipcqqfaeuSp8h/OHdHPCQuT1YfP/992rRooWuXbumoKAgpxfn2Ww2ggUeC1cux6hzh9YqVqKU3v/4c6UNDdOpE8cVGJTwzfJr16zSnl2/3/cvlzkzpt33JZNtX31ddRo0cWz7p0lj7QYA4F/is4nTnf6RM+rwIfV+4xVVqvYfp37rf/pR+/b8rnTpE35X7/n9N/Xp1lHNWrdT5+595eXtrSMH98vm8b81c4oWL61mbdorXboM+vPcWY0f86GG9HtTn3w57eHdHPCQuT1YvPnmm2rbtq3effddpeGHHzympk+dqPDwTOozYJijLXOWrAn6nTt7Rh9/8K5Gfjxefbq/nui5Dh3Yr9nTp2rc5Jl6vvazifbxTxOgdOnTJ0/xAPAvkjY0zGl75tQJypI1u4oW+9/S+H+ePaMxH76r90aP01tvdk5wjrEfj1CjF5qrWet2jrZs2XM69Wly19PojJmz6KXWbTWwd1fFxcXKy8s7uW4HeKTcvtzsyZMn1aVLF0IFHmsb1q5RgScLaWCf7mpYs7Lat3xBixbMcepjt9v17sB+eqnly8qdN1+i57l584aG9u+lN3r2u29wmDF1oupXr6h2LZpo2sQvFBsbm5y3AwD/CrGxsVq5bLFq1W3oeEpst9v13pB+erFFpHLlSfhdffHCee3b/bvShoWpS4dWalK7irp3fFm///brPa9zOSZGq5b9oEJPRxAqkKq5/YlFzZo1tXXrVuXJk8fdpQAPzamTJ/TdvNl6sXlrtXy5g/bu/l2ffPievL19VLNOfUl/hQFPL08937TFPc/z2agRKvx0hCpWrnrPPk2attATBZ9UUFCw9u7epS/HfqzoUyfV6+3ByX5fAPA4W//Tj7p69Yr+U6eBo23mtIny9PRSoxcT/66OPnVCkjT1q8/16n/fVN4nCmjFku/V678d9OU385yeXHz52Sh9N2eGbt68qSefKqJhH3z6cG8IeMjcHizq1Kmjnj17as+ePXr66afl7e2c1OvXr3/f42/duqVbt279rc0mX9+kzV4HHgVjt6vAk4XVodMbkqQnCjypo0cO67u5s1SzTn3t37tbc2Z+rS+nzb7n3In1P6/Wr1s368tp3973Wi80/9+8pLxPFFBQcLAG9umuVzt3U0jatMl2TwDwuFuyaL5Kl63gmPN2YN8ezZ/9jT6fPOue39XG/tdKZ3UbNlGtug0l/fWdv33rL1r6/QK1//+/ByTpxRaReq5eI505Ha2pE8bp/SFv6Z0PPr3vHDogJXN7sOjQoYMkaciQIQn22Ww2xcfH3/f44cOHa/Bg53+J7d77bfXo2z/5igQsSpc+g3LmzuvUljNXHv28eqUkaeeOX3Xp4gW9WP9/kwPt8fH6/OMPNGfm15r13TL9unWzTp04rrrVyjudZ2Cf7no6org+Hjcp0WsXeqqIJOnkiWMECwBIojPRp7R9yyYNHD7K0fb7jm26dPGCmjeq6Wizx8dr/JgPNW/WN/pm/lKF/f8w1b9/5+fIlUdnz0Q7tYWkDVVI2lBly5FLOXLlVrMG/9HeXTtV6OmiD/HOgIfH7cHi78vLuqpv377q3r27U9uFmyR9pCxPFYnQ8T+OOrUdP3ZUGTNlliT957l6KlG6rNP+Xl1eU43n6uq5eg0lSc1bt1OdBo2d+rRt1livd+ul8hUr3/PaB/fvk/RXuAEAJM3SxQuUNjRMZcs/42ir/lw9FS/l/F3dp2tHVX+urmr9/3CpTJmzKl368ATf+SeO/aHS5Src83p33ip2O5Z3cSH1cnuwsMrX1zfBsKdrhv8pkbK80Ly1Xm/XSl9P+lJVqtfUvt2/a9GCuXqz3wBJUkjatAmeJnh6eSksXXrlyJlbkpQuffpEJ2yHZ8ykzFmzSZJ279yhPbt2KqJkaQUGBGrfnl36bPRIVahUxRFiAAD3Z7fbtWzxd6pRu748vf73o1JISFqFhKR16uvl5aWwsHTK/v/f1TabTS+2aKMpX32uvE/kV94nCmr5Dwt1/I8oDXz3Q0nSvt2/a9+eXXqqaDEFBQUr+tQJTf7yM2XJml2FnuJpBVIvtweLxIZA3W3AgAGPqBLg4SlY6CkNHTFaX44drSkTxilzlqzq3L2XatSqm6zX8fbx0Y8rl2nyV+MUG3tbGTNlVp0Gz6tZ65eT9ToA8Dj7dcsmnT0dref+f46Eq55/qZVu376tzz8eqSuXY5QnXwG9/8l4ZcmWXZLk4+urdT+t1JSvxurmzRtKly69SpatoLeHjJCPj08y3gnwaNmMufPw7dH5+eefVb58eXl5ealYsWJO+2JjYxUVFSUvLy/lzZtXv/567+XZ7iU6hicWAJAaxMU/8r+CAAAuyh6WtEWR3PLEokqVKjp9+rTCw8O1ffv2BPsvX76syMhINWrUyA3VAQAAAHCVW55YxMTEKCQk5L59du3apbp16+ro0aMun58nFgCQOvDEAgBSvqQ+sXDLm7cHDx6sa9eu3bfPpUuXFBMT84gqAgAAAGCFW4ZCbd++XbGxsZKkTz75xGmfMUbR0dGaNm2aatWq5Y7yAAAAALjILUOh7pY7d26nbQ8PD2XIkEFVq1ZV3759FRQU5PI5GQoFAKkDQ6EAIOVL0ZO373bkyJF7vrr+xo0bj7gaAAAAAA/CLXMs7ta5c+dE269du6bnnnvuEVcDAAAA4EG4PVgsX75cb7/9tlPb1atXVatWLcXHx7upKgAAAACucPtQqOXLl6tixYpKly6dunXrpitXrqhmzZry8vLSkiVL3F0eAAAAgCRwe7DInTu3li1bpipVqsjDw0MzZ86Ur6+vFi9erICAAHeXBwAAACAJ3L4q1B2bNm1S9erVVaZMGS1atEj+/v4PfC5WhQKA1IFVoQAg5UvRq0IVK1Ys0ZWgfH19derUKVWoUMHR9uuvvz7K0gAAAAA8ALcEi4YNG7rjsgAAAAAekhQzFCo5MRQKAFIHhkIBQMqX1KFQbl9uVpIuXbqkr776Sn379tWFCxck/TUE6uTJk26uDAAAAEBSuH1VqJ07d6p69eoKCQnR0aNH1aFDB4WFhWn+/Pn6448/NHXqVHeXCAAAAOAfuP2JRffu3RUZGamDBw/Kz8/P0f7cc8/p559/dmNlAAAAAJLK7cFiy5YtevXVVxO0Z82aVadPn3ZDRQAAAABc5fZg4efnp8uXLydo379/vzJkyOCGigAAAAC4yu3BokGDBhoyZIhiY2MlSTabTceOHVOfPn30/PPPu7k6AAAAAEnh9uVmL1++rNq1a2v37t26cuWKsmTJotOnT6ts2bJasmSJAgICXD4ny80CQOrAcrMAkPIldblZtweLO1avXq1t27bJbrerePHiql69+gOfi2ABAKkDwQIAUr4U/x6LGzduaNGiRY7t5cuX69SpUzp9+rR++OEH9erVSzdv3nRXeQAAAABc4Lb3WEydOlWLFi1S3bp1JUmffvqpChcuLH9/f0nSvn37lDlzZnXr1s1dJQIAAABIIrc9sfjmm2/Utm1bp7bp06dr9erVWr16tUaOHKnZs2e7qToAAAAArnBbsDhw4IDy58/v2Pbz85OHx//KKV26tPbs2eOO0gAAAAC4yG1DoWJiYuTl9b/Lnzt3zmm/3W7XrVu3HnVZAAAAAB6A255YZMuWTbt27brn/p07dypbtmyPsCIAAAAAD8ptwaJ27doaMGBAois/3bhxQ4MHD1adOnXcUBkAAAAAV7ntPRZnzpxRRESEfHx81LlzZ+XPn182m0379u3Tp59+qri4OG3fvl0ZM2Z0+dy8xwIAUgfeYwEAKV+qeEFeVFSUOnbsqBUrVuhOGTabTTVq1NDYsWOVJ0+eBzovwQIAUgeCBQCkfKkiWNxx4cIFHTp0SJKUL18+hYWFWTofwQIAUgeCBQCkfKkqWCQ3ggUApA4ECwBI+ZIaLNw2eRsAAADA44NgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwzGaMMe4uAsD93bp1S8OHD1ffvn3l6+vr7nIAAInguxr/dgQLIBW4fPmyQkJCFBMTo+DgYHeXAwBIBN/V+LdjKBQAAAAAywgWAAAAACwjWAAAAACwjGABpAK+vr4aOHAgkwEBIAXjuxr/dkzeBgAAAGAZTywAAAAAWEawAAAAAGAZwQL/SpMnT1batGmT3D9XrlwaPXr0Q6vnQdlsNi1YsOCe+9esWSObzaZLly6pa9euqlKlSrJct0qVKuratatjO6V+PgDgqkGDBikiIuK+fe58B166dEk2m01r1qyxfN27v68l1/+eAlICggUeK5GRkbLZbHrvvfec2hcsWCCbzebYbtq0qQ4cOJBs1z169KhsNpt27NiRpP5z585V1apVFRoaqjRp0qhAgQJq27attm/fnmw1SVL58uUVHR2tkJAQDR06VPPmzUvW89+xZcsWvfLKK0nu7+rnBQBJcfr0ab3xxhvKly+f/Pz8lDFjRlWsWFHjxo3T9evXk+068+bN09ChQxUSEqLo6GiVL18+2c59x4P8PRUZGamGDRsmey1AUhEs8Njx8/PT+++/r4sXL96zj7+/v8LDwx9hVf/Tu3dvNW3aVBEREVq4cKF2796tL774Qnnz5lW/fv2S9Vo+Pj7KlCmTbDabgoKCFBYWlqznvyNDhgxKkybNQzk3ACTFkSNHVKxYMS1fvlzvvvuutm/frpUrV6pbt276/vvvtXLlymS7VlhYmIKCgmSz2ZQpUyb5+Pgk27nvcOffU8CDIljgsVO9enVlypRJw4cPv2efxB4xL1y4UCVLlpSfn5/Sp0+vxo0bO+2/fv262rZtq6CgIOXIkUNffPGFY1/u3LklScWKFZPNZrvnkKNNmzZpxIgR+uijj/TRRx/pmWeeUe7cuVW5cmW99dZb+uGHH5z6f//99ypRooT8/PyUJ08eDR48WHFxcfe8ryFDhihjxozasWNHovf49yc3dx75T5s2Tbly5VJISIheeuklXblyxdHn2rVrat26tQIDA5U5c2Z9+OGHCa7796FQ+/btU8WKFeXn56dChQpp5cqVTsO2kvp5AUBSderUSV5eXtq6datefPFFPfnkk3r66af1/PPPa/HixapXr54kKSYmRq+88orCw8MVHBysqlWr6rfffrvneaOiopQvXz517NhRdrs90aGfERERGjRokGPbZrPpq6++UqNGjZQmTRo98cQTWrhwodMxP/zwg/Lnzy9/f389++yzOnr0qNP+xL7Dhw0bpvDwcAUFBal9+/bq06ePY9jWoEGDNGXKFH333Xey2WzJNkQLcAXBAo8dT09PvfvuuxozZoxOnDiRpGMWL16sxo0bq06dOtq+fbtWrVqlkiVLOvX58MMPVbJkSW3fvl2dOnVSx44dtW/fPknS5s2bJUkrV65UdHT0PYcczZgxQ4GBgerUqVOi++/+oX/ZsmVq2bKlunTpoj179mj8+PGaPHmy3nnnnQTHGWP0xhtvaMKECVq3bt0/jg++2+HDh7VgwQItWrRIixYt0k8//eQ0lKxnz55avXq15s+fr+XLl2vNmjXatm3bPc9nt9vVsGFDpUmTRr/88ou++OILvfXWW059kvp5AUBSnD9/XsuXL9frr7+ugICARPvYbDYZY1SnTh2dPn1aP/zwg7Zt26bixYurWrVqunDhQoJjdu3apQoVKuiFF17Q559/Lg+PpP/YNHjwYL344ovauXOnateurRYtWjiucfz4cTVu3Fi1a9fWjh07HCHhfr755hu98847ev/997Vt2zblyJFDn3/+uWN/jx499OKLL6pWrVqKjo5+aEO0gPsywGOkTZs2pkGDBsYYY8qWLWvatm1rjDFm/vz55u7/3CdNmmRCQkIc2+XKlTMtWrS453lz5sxpWrZs6di22+0mPDzcfP7558YYY6Kioowks3379vvWV6tWLVOkSBGntg8//NAEBAQ4fl26dMkYY8wzzzxj3n33Xae+06ZNM5kzZ3ZsSzLffvutadmypSlYsKA5fvz4Pe8xsc9h4MCBJk2aNOby5cuOtp49e5oyZcoYY4y5cuWK8fHxMTNnznTsP3/+vPH39zdvvPGG0+czatQoY4wxS5YsMV5eXiY6Otqxf8WKFUaSmT9/vkufFwAkxaZNm4wkM2/ePKf2dOnSOb5be/XqZVatWmWCg4PNzZs3nfrlzZvXjB8/3hjz1/di0aJFzYYNG0xYWJgZOXKkU9+7v+/uKFq0qBk4cKBjW5J5++23HdtXr141NpvNLFmyxBhjTN++fc2TTz5p7Ha7o0/v3r2NJHPx4kVjTMLv8DJlypjXX3/d6boVKlQwRYsWdWzf/Xcg4A48scBj6/3339eUKVO0Z8+ef+y7Y8cOVatW7b59ihQp4vj9nXG1Z8+edbmuu59KSFLbtm21Y8cOjR8/XteuXZP5/3dWbtu2TUOGDFFgYKDjV4cOHRQdHe00CbFbt27auHGj1q5dq2zZsrlcT65cuRQUFOTYzpw5s+O+Dh8+rNu3b6tcuXKO/WFhYSpQoMA9z7d//35lz55dmTJlcrSVLl3a5boAwFV//37dvHmzduzYocKFC+vWrVvatm2brl69qnTp0jl9t0ZFRenw4cOO444dO6bq1avr7bffVo8ePR6olrv/zggICFBQUJDju3Xv3r0qW7asU713f88mZv/+/Qm+S/luRUrj5e4CgIelUqVKqlmzpvr166fIyMj79vX39//H83l7eztt22w22e12l2p64okntG7dOsXGxjrOlzZtWqVNmzbBsC273a7BgwcnmOsh/TVB/Y4aNWpoxowZWrZsmVq0aOFo9/DwcISUO2JjY126r78fnxTGmAR/uQPAw5QvXz7ZbDbH8NQ78uTJI+l/3/F2u12ZM2dOdO7B3fMZMmTIoCxZsmjmzJlq166dgoODHfvc9d165xx3e9DzAA8LTyzwWHvvvff0/fffa8OGDfftV6RIEa1ateqBr3NnRZD4+Pj79mvWrJmuXr2qsWPH/uM5ixcvrv379ytfvnwJft09zrd+/fqaPn262rdvr5kzZzraM2TIoCtXrujatWuONleXd82XL5+8vb21adMmR9vFixfvuwRiwYIFdezYMZ05c8bRtmXLFqc+Sf28ACAp0qVLpxo1aujTTz91+s77u+LFi+v06dPy8vJK8L2aPn16Rz9/f38tWrRIfn5+qlmzptOCFhkyZFB0dLRj+/Lly4qKinKp3kKFCjl9r0pKsP13BQoUcMxPu2Pr1q1O2z4+Pnyvwq0IFnisPf3002rRooXGjBlz334DBw7UjBkzNHDgQO3du1e///67RowYkeTrhIeHy9/fX0uXLtWZM2cUExOTaL9y5crpzTff1Jtvvqnu3btr3bp1+uOPP7Rp0yZNmDBBNpvNERoGDBigqVOnatCgQdq9e7f27t2rWbNm6e23305w3kaNGmnatGl6+eWXNWfOHElSmTJllCZNGvXr10+HDh3S9OnTNXny5CTfkyQFBgaqXbt26tmzp1atWqVdu3YpMjLyvhMYa9Soobx586pNmzbauXOn1q9f75i8fedf25L6eQFAUo0dO1ZxcXEqWbKkZs2apb1792r//v36+uuvtW/fPnl6eqp69eoqV66cGjZsqGXLluno0aPasGGD3n777QQ/pAcEBGjx4sXy8vLSc889p6tXr0qSqlatqmnTpmnt2rXatWuX2rRpI09PT5dqfe2113T48GF1795d+/fvT9L383//+19NmDBBU6ZM0cGDBzVs2DDt3LnT6SlGrly5tHPnTu3fv19//vlnok9SgIeJYIHH3tChQ//xcXGVKlX07bffauHChYqIiFDVqlX1yy+/JPkaXl5e+uSTTzR+/HhlyZJFDRo0uGffDz74QNOnT9f27dtVt25dPfHEE3rhhRdkt9u1ceNGxyP3mjVratGiRVqxYoVKlSqlsmXL6qOPPlLOnDkTPW+TJk00ZcoUtWrVSvPmzVNYWJi+/vpr/fDDD3r66ac1Y8YMp+UQk2rkyJGqVKmS6tevr+rVq6tixYoqUaLEPft7enpqwYIFunr1qkqVKqX27ds7wtCdIVyufF4AkBR58+bV9u3bVb16dfXt21dFixZVyZIlNWbMGPXo0UNDhw6VzWbTDz/8oEqVKqlt27bKnz+/XnrpJR09elQZM2ZMcM7AwEAtWbJExhjVrl1b165dU9++fVWpUiXVrVtXtWvXVsOGDZU3b16Xas2RI4fmzp2r77//XkWLFtW4ceP07rvv3veYFi1aqG/fvurRo4eKFy+uqKgoRUZGOg2N7dChgwoUKKCSJUsqQ4YMWr9+vUt1AVbZDAP0ADxk69evV8WKFXXo0CGX/wIGACSuRo0aypQpk6ZNm+buUgBJTN4G8BDMnz9fgYGBeuKJJ3To0CG98cYbqlChAqECAB7Q9evXNW7cONWsWVOenp6aMWOGVq5cqRUrVri7NMCBYAEg2V25ckW9evXS8ePHlT59elWvXj3RN3YDAJLmzjCuYcOG6datWypQoIDmzp2r6tWru7s0wIGhUAAAAAAsY/I2AAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAJCKTJ48WWnTpnV3Gf8aR48elc1m044dO9xdCgCkeAQLAEhm9erVu+fa8hs3bpTNZtOvv/76iKt6fERGRqphw4aWz5NYaLhy5YqqVKmiggUL6vjx45avAQD/JgQLAEhm7dq1048//qg//vgjwb6JEycqIiJCxYsXd/m8t2/fTo7yEhUbG/vQzp1anDt3Ts8++6yuXr2qdevWKXv27O4uCQBSFYIFACSzunXrKjw8XJMnT3Zqv379umbNmqV27dpJkubOnavChQvL19dXuXLlSvB28ly5cmnYsGGKjIxUSEiIOnTo4Ni3bNkyPfnkkwoMDFStWrUUHR3tdOykSZP05JNPys/PTwULFtTYsWMd++78S/3s2bNVpUoV+fn56euvv1ZcXJy6dOmitGnTKl26dOrdu7fatGnj9HTAGKMRI0YoT5488vf3V9GiRTVnzhzH/osXL6pFixbKkCGD/P399cQTT2jSpEmO/b1791b+/PmVJk0a5cmTR/37908QaoYNG6bw8HAFBQWpffv26tOnjyIiIiRJgwYN0pQpU/Tdd9/JZrPJZrNpzZo1kqSTJ0+qadOmCg0NVbp06dSgQQMdPXo0SX9mx48f1zPPPKOgoCCtXr1a6dOnT7RffHy82rVrp9y5c8vf318FChTQxx9/7NRnzZo1Kl26tAICApQ2bVpVqFDBETJ/++03PfvsswoKClJwcLBKlCihrVu3Oo7dsGGDKlWqJH9/f2XPnl1dunTRtWvXknQPAOB2BgCQ7Hr27Gly5cpl7Ha7o23y5MnG19fXXLhwwWzdutV4eHiYIUOGmP3795tJkyYZf39/M2nSJEf/nDlzmuDgYDNy5Ehz8OBBc/DgQTNp0iTj7e1tqlevbrZs2WK2bdtmnnzySdO8eXPHcV988YXJnDmzmTt3rjly5IiZO3euCQsLM5MnTzbGGBMVFWUkmVy5cjn6nDx50gwbNsyEhYWZefPmmb1795rXXnvNBAcHmwYNGjjO3a9fP1OwYEGzdOlSc/jwYTNp0iTj6+tr1qxZY4wx5vXXXzcRERFmy5YtJioqyqxYscIsXLjQcfzQoUPN+vXrTVRUlFm4cKHJmDGjef/99x37v/76a+Pn52cmTpxo9u/fbwYPHmyCg4NN0aJFjTHGXLlyxbz44oumVq1aJjo62kRHR5tbt26Za9eumSeeeMK0bdvW7Ny50+zZs8c0b97cFChQwNy6dSvRP6M7n8PMmTNN9uzZTYMGDczNmzcT7bN9+3ZjjDG3b982AwYMMJs3bzZHjhwxX3/9tUmTJo2ZNWuWMcaY2NhYExISYnr06GEOHTpk9uzZYyZPnmz++OMPY4wxhQsXNi1btjR79+41Bw4cMLNnzzY7duwwxhizc+dOExgYaEaNGmUOHDhg1q9fb4oVK2YiIyP/8b83AEgJCBYA8BDs3bvXSDI//vijo61SpUqmWbNmxhhjmjdvbmrUqOF0TM+ePU2hQoUc2zlz5jQNGzZ06jNp0iQjyRw6dMjR9tlnn5mMGTM6trNnz26mT5/udNzQoUNNuXLljDH/+2F59OjRTn0yZsxoRo4c6diOi4szOXLkcASLq1evGj8/P7Nhwwan49q1a+e4r3r16pmXX375Pp+MsxEjRpgSJUo4tsuUKWNef/11pz4VKlRwBAtjjGnTpo1T2DHGmAkTJpgCBQo4Bblbt24Zf39/s2zZskSvfedz8PHxMVWqVDFxcXH37HMnWCSmU6dO5vnnnzfGGHP+/HkjyRG0/i4oKMgR8P6uVatW5pVXXnFqW7t2rfHw8DA3bty45/UBIKVgKBQAPAQFCxZU+fLlNXHiREnS4cOHtXbtWrVt21aStHfvXlWoUMHpmAoVKujgwYOKj493tJUsWTLBudOkSaO8efM6tjNnzqyzZ89K+muewPHjx9WuXTsFBgY6fg0bNkyHDx92Os/d546JidGZM2dUunRpR5unp6dKlCjh2N6zZ49u3rypGjVqOJ176tSpjnN37NhRM2fOVEREhHr16qUNGzY4XXPOnDmqWLGiMmXKpMDAQPXv31/Hjh1z7N+/f79TDZISbCdm27ZtOnTokIKCghx1hYWF6ebNmwnu++8aNGigdevWae7cuf94HUkaN26cSpYsqQwZMigwMFBffvml4x7CwsIUGRmpmjVrql69evr444+dhql1795d7du3V/Xq1fXee+851bZt2zZNnjzZ6bOtWbOm7Ha7oqKiklQbALiTl7sLAIDHVbt27dS5c2d99tlnmjRpknLmzKlq1apJ+muugs1mc+pvjElwjoCAgARt3t7eTts2m81xrN1ulyR9+eWXKlOmjFM/T0/Pfzz3/Wq6c+7Fixcra9asTv18fX0lSc8995z++OMPLV68WCtXrlS1atX0+uuv64MPPtCmTZv00ksvafDgwapZs6ZCQkI0c+bMBHNLkvK5/J3dbleJEiX0zTffJNiXIUOG+x7br18/FSlSRC1atJAxRk2bNr1n39mzZ6tbt2768MMPVa5cOQUFBWnkyJH65ZdfHH0mTZqkLl26aOnSpZo1a5befvttrVixQmXLltWgQYPUvHlzLV68WEuWLNHAgQM1c+ZMNWrUSHa7Xa+++qq6dOmS4Lo5cuT4x88AANyNYAEAD8mLL76oN954Q9OnT9eUKVPUoUMHxw/NhQoV0rp165z6b9iwQfnz508QAFyRMWNGZc2aVUeOHFGLFi2SfFxISIgyZsyozZs365lnnpH010Tl7du3OyZOFypUSL6+vjp27JgqV658z3NlyJBBkZGRioyM1DPPPKOePXvqgw8+0Pr165UzZ0699dZbjr5/XzmrQIEC2rx5s1q1auVou3tysyT5+Pg4PdWRpOLFi2vWrFkKDw9XcHBwku/7jrffflteXl5q0aKF7Ha7mjVrlmi/tWvXqnz58urUqZOjLbEnIsWKFVOxYsXUt29flStXTtOnT1fZsmUlSfnz51f+/PnVrVs3NWvWTJMmTVKjRo1UvHhx7d69W/ny5XO5fgBICQgWAPCQBAYGqmnTpurXr59iYmIUGRnp2Pfmm2+qVKlSGjp0qJo2baqNGzfq008/dVq96UENGjRIXbp0UXBwsJ577jndunVLW7du1cWLF9W9e/d7Hvff//5Xw4cPV758+VSwYEGNGTNGFy9edIShoKAg9ejRQ926dZPdblfFihV1+fJlbdiwQYGBgWrTpo0GDBigEiVKqHDhwrp165YWLVqkJ598UpKUL18+HTt2TDNnzlSpUqW0ePFizZ8/P0ENHTp0UMmSJVW+fHnNmjVLO3fuVJ48eRx9cuXKpWXLlmn//v1Kly6dQkJC1KJFC40cOVINGjTQkCFDlC1bNh07dkzz5s1Tz549lS1btn/83Pr06SNPT0+1atVKdrs90WCWL18+TZ06VcuWLVPu3Lk1bdo0bdmyRblz55YkRUVF6YsvvlD9+vWVJUsW7d+/XwcOHFDr1q1148YN9ezZU02aNFHu3Ll14sQJbdmyRc8//7ykv1bMKlu2rF5//XV16NBBAQEB2rt3r1asWKExY8b8Y/0A4HbunOABAI+7DRs2GEnmP//5T4J9c+bMMYUKFTLe3t4mR44cThOnjflr8vaoUaOc2iZNmmRCQkKc2ubPn2/+/nX+zTffmIiICOPj42NCQ0NNpUqVzLx584wx956QHBsbazp37myCg4NNaGio6d27t3nhhRfMSy+95Ohjt9vNxx9/bAoUKGC8vb1NhgwZTM2aNc1PP/1kjPlrkviTTz5p/P39TVhYmGnQoIE5cuSI4/iePXuadOnSmcDAQNO0aVMzatSoBPczZMgQkz59ehMYGGjatm1runTpYsqWLevYf/bsWVOjRg0TGBhoJJnVq1cbY4yJjo42rVu3NunTpze+vr4mT548pkOHDiYmJibBZ3+/z+HDDz80np6eZurUqQn63Lx500RGRpqQkBCTNm1a07FjR9OnTx/H5PLTp0+bhg0bmsyZMxsfHx+TM2dOM2DAABMfH29u3bplXnrpJZM9e3bj4+NjsmTJYjp37uw0MXvz5s2OewsICDBFihQx77zzTqL1A0BKYzMmCYNXAQD/Ona7XU8++aRefPFFDR061G111KhRQ5kyZdK0adPcVgMA4J8xFAoAIOmv+Q7Lly9X5cqVdevWLX366aeKiopS8+bNH1kN169f17hx41SzZk15enpqxowZWrlypVasWPHIagAAPBiCBQBAkuTh4aHJkyerR48eMsboqaee0sqVKx1zJB4Fm82mH374QcOGDdOtW7dUoEABzZ07V9WrV39kNQAAHgxDoQAAAABYxgvyAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABY9n/wFwAy5fDmUwAAAABJRU5ErkJggg==",
+      "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": 37,
+   "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('Falsche Positive Rate')\n",
+    "plt.ylabel('Wahre 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": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}