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{
"cells": [
{
"cell_type": "markdown",
"id": "5ae37cf6",
"metadata": {},
"source": [
"# Lernfall 3"
]
},
{
"cell_type": "markdown",
"id": "8933b8ed",
"metadata": {},
"source": [
"## Datensatz laden"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "77a4a87d",
"metadata": {},
"outputs": [
{
"data": {
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"<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": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"data = pd.read_csv('VertragsdatenABCAG.csv', sep=',', decimal='.') # Daten einlesen\n",
"\n",
"# Erste 5 Zeilen anzeigen\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"id": "a7f17d3f",
"metadata": {},
"source": [
"## Transformieren der Daten in numerische Form"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "9bc5ef44",
"metadata": {},
"outputs": [
{
"data": {
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"<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": 52,
"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": "cd160830",
"metadata": {},
"source": [
"## Korrelationen darstellen"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "13eb3384",
"metadata": {},
"outputs": [
{
"data": {
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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": "49fc8beb",
"metadata": {},
"source": [
"## Datenaufteilung in Training und Test"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "dca7a8df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Anzahl der Trainingsdaten: 279004\n",
"Anzahl der Testdaten: 69751\n"
]
}
],
"source": [
"# Daten in Trainings- und Testdaten aufteilen\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(data, data['Status_gekuendigt'], test_size=0.2, random_state=11)\n",
"print(f\"Anzahl der Trainingsdaten: {len(X_train)}\")\n",
"print(f\"Anzahl der Testdaten: {len(X_test)}\")"
]
},
{
"cell_type": "markdown",
"id": "50b9306d",
"metadata": {},
"source": [
"## Erstellung von Einfachregressionsmodellen"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "4edb7ff2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Status_gekuendigt R-squared: 0.055\n",
"Model: OLS Adj. R-squared: 0.055\n",
"Method: Least Squares F-statistic: 1.618e+04\n",
"Date: Wed, 09 Apr 2025 Prob (F-statistic): 0.00\n",
"Time: 11:54:44 Log-Likelihood: -1.3165e+05\n",
"No. Observations: 279004 AIC: 2.633e+05\n",
"Df Residuals: 279002 BIC: 2.633e+05\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 0.0562 0.001 41.979 0.000 0.054 0.059\n",
"Marktzins 0.0405 0.000 127.196 0.000 0.040 0.041\n",
"==============================================================================\n",
"Omnibus: 52590.458 Durbin-Watson: 1.998\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 89166.145\n",
"Skew: 1.379 Prob(JB): 0.00\n",
"Kurtosis: 3.250 Cond. No. 7.97\n",
"==============================================================================\n",
"\n",
"Notes:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"Mean Squared Error (MSE): 0.15083167048286217\n",
"R^2: 0.05515625091832621\n"
]
}
],
"source": [
"# Einfachregressionsmodell mit scikit-learn: Feature = \"Marktzins\", Target = \"Status_gekuendigt\"\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"import numpy as np\n",
"X_train_simple = X_train[['Marktzins']]\n",
"X_test_simple = X_test[['Marktzins']]\n",
"y_train_simple = y_train\n",
"y_test_simple = y_test\n",
"model_simple = LinearRegression()\n",
"model_simple.fit(X_train_simple, y_train_simple)\n",
"y_pred_simple = model_simple.predict(X_test_simple)\n",
"\n",
"# Modellbewertung mit statistischem Guetemass R^2, p-Werte und MSE\n",
"from statsmodels.api import OLS, add_constant\n",
"X_train_simple_const = add_constant(X_train_simple)\n",
"X_test_simple_const = add_constant(X_test_simple)\n",
"model_simple_ols = OLS(y_train_simple, X_train_simple_const).fit()\n",
"print(model_simple_ols.summary())\n",
"mse_simple = mean_squared_error(y_test_simple, y_pred_simple)\n",
"r2_simple = r2_score(y_test_simple, y_pred_simple)\n",
"print(f\"Mean Squared Error (MSE): {mse_simple}\")\n",
"print(f\"R^2: {r2_simple}\")"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "8b2f87a3",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Durchschnittlicher Status_gekuendigt für verschiedene Marktzinswerte\n",
"status_gekuendigt_mean = X_train.groupby('Marktzins')['Status_gekuendigt'].mean().reset_index()\n",
"\n",
"\n",
"# Plotten der Regressionsgerade und der Durchschnittswerte\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(X_test_simple, y_pred_simple, color='red', label='Regressionsgerade')\n",
"plt.scatter(status_gekuendigt_mean['Marktzins'], status_gekuendigt_mean['Status_gekuendigt'], color='green', label='Durchschnittswerte')\n",
"plt.title('Einfachregression: Status_gekuendigt vs. Marktzins')\n",
"plt.xlabel('Marktzins')\n",
"plt.ylabel('Status_gekuendigt')\n",
"plt.legend()\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "caa4c1bb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Status_gekuendigt R-squared: 0.108\n",
"Model: OLS Adj. R-squared: 0.108\n",
"Method: Least Squares F-statistic: 3.383e+04\n",
"Date: Wed, 09 Apr 2025 Prob (F-statistic): 0.00\n",
"Time: 11:54:45 Log-Likelihood: -1.2355e+05\n",
"No. Observations: 279004 AIC: 2.471e+05\n",
"Df Residuals: 279002 BIC: 2.471e+05\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 0.4233 0.001 299.224 0.000 0.421 0.426\n",
"Tarifzins -0.0473 0.000 -183.922 0.000 -0.048 -0.047\n",
"==============================================================================\n",
"Omnibus: 43458.038 Durbin-Watson: 1.999\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 67917.781\n",
"Skew: 1.208 Prob(JB): 0.00\n",
"Kurtosis: 3.056 Cond. No. 11.2\n",
"==============================================================================\n",
"\n",
"Notes:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"Mean Squared Error (MSE): 0.14247416326793394\n",
"R^2: 0.10750956918796062\n"
]
}
],
"source": [
"# Einfachregressionsmodell mit scikit-learn: Feature = \"Tarifzins\", Target = \"Status_gekuendigt\"\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"import numpy as np\n",
"X_train_simple = X_train[['Tarifzins']]\n",
"X_test_simple = X_test[['Tarifzins']]\n",
"y_train_simple = y_train\n",
"y_test_simple = y_test\n",
"model_simple = LinearRegression()\n",
"model_simple.fit(X_train_simple, y_train_simple)\n",
"y_pred_simple = model_simple.predict(X_test_simple)\n",
"\n",
"# Modellbewertung mit statistischem Guetemass R^2, p-Werte und MSE\n",
"from statsmodels.api import OLS, add_constant\n",
"X_train_simple_const = add_constant(X_train_simple)\n",
"X_test_simple_const = add_constant(X_test_simple)\n",
"model_simple_ols = OLS(y_train_simple, X_train_simple_const).fit()\n",
"print(model_simple_ols.summary())\n",
"mse_simple = mean_squared_error(y_test_simple, y_pred_simple)\n",
"r2_simple = r2_score(y_test_simple, y_pred_simple)\n",
"print(f\"Mean Squared Error (MSE): {mse_simple}\")\n",
"print(f\"R^2: {r2_simple}\")"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "67964cdc",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Durchschnittlicher Status_gekuendigt für verschiedene Tarifzinswerte\n",
"status_gekuendigt_mean = X_train.groupby('Tarifzins')['Status_gekuendigt'].mean().reset_index()\n",
"\n",
"\n",
"# Plotten der Regressionsgerade und der Durchschnittswerte\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(X_test_simple, y_pred_simple, color='red', label='Regressionsgerade')\n",
"plt.scatter(status_gekuendigt_mean['Tarifzins'], status_gekuendigt_mean['Status_gekuendigt'], color='green', label='Durchschnittswerte')\n",
"plt.title('Einfachregression: Status_gekuendigt vs. Tarifzins')\n",
"plt.xlabel('Tarifzins')\n",
"plt.ylabel('Status_gekuendigt')\n",
"plt.legend()\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "11d7a9ab",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Status_gekuendigt R-squared: 0.046\n",
"Model: OLS Adj. R-squared: 0.046\n",
"Method: Least Squares F-statistic: 1.351e+04\n",
"Date: Wed, 09 Apr 2025 Prob (F-statistic): 0.00\n",
"Time: 11:54:45 Log-Likelihood: -1.3291e+05\n",
"No. Observations: 279004 AIC: 2.658e+05\n",
"Df Residuals: 279002 BIC: 2.659e+05\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -0.0223 0.002 -10.941 0.000 -0.026 -0.018\n",
"Jahr 0.0355 0.000 116.220 0.000 0.035 0.036\n",
"==============================================================================\n",
"Omnibus: 53045.477 Durbin-Watson: 1.999\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 90506.856\n",
"Skew: 1.391 Prob(JB): 0.00\n",
"Kurtosis: 3.214 Cond. No. 18.8\n",
"==============================================================================\n",
"\n",
"Notes:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"Mean Squared Error (MSE): 0.15246656201925388\n",
"R^2: 0.04491492001188524\n"
]
}
],
"source": [
"# Einfachregressionsmodell mit scikit-learn: Feature = \"Jahr\", Target = \"Status_gekuendigt\"\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"import numpy as np\n",
"X_train_simple = X_train[['Jahr']]\n",
"X_test_simple = X_test[['Jahr']]\n",
"y_train_simple = y_train\n",
"y_test_simple = y_test\n",
"model_simple = LinearRegression()\n",
"model_simple.fit(X_train_simple, y_train_simple)\n",
"y_pred_simple = model_simple.predict(X_test_simple)\n",
"\n",
"# Modellbewertung mit statistischem Guetemass R^2, p-Werte und MSE\n",
"from statsmodels.api import OLS, add_constant\n",
"X_train_simple_const = add_constant(X_train_simple)\n",
"X_test_simple_const = add_constant(X_test_simple)\n",
"model_simple_ols = OLS(y_train_simple, X_train_simple_const).fit()\n",
"print(model_simple_ols.summary())\n",
"mse_simple = mean_squared_error(y_test_simple, y_pred_simple)\n",
"r2_simple = r2_score(y_test_simple, y_pred_simple)\n",
"print(f\"Mean Squared Error (MSE): {mse_simple}\")\n",
"print(f\"R^2: {r2_simple}\")"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "ed8a93cf",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Durchschnittlicher Status_gekuendigt für verschiedene Jahre\n",
"status_gekuendigt_mean = X_train.groupby('Jahr')['Status_gekuendigt'].mean().reset_index()\n",
"\n",
"\n",
"# Plotten der Regressionsgerade und der Durchschnittswerte\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(X_test_simple, y_pred_simple, color='red', label='Regressionsgerade')\n",
"plt.scatter(status_gekuendigt_mean['Jahr'], status_gekuendigt_mean['Status_gekuendigt'], color='green', label='Durchschnittswerte')\n",
"plt.title('Einfachregression: Status_gekuendigt vs. Jahr')\n",
"plt.xlabel('Jahr')\n",
"plt.ylabel('Status_gekuendigt')\n",
"plt.legend()\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "3079b781",
"metadata": {},
"source": [
"## Übersicht der Modelle\n",
"\n",
"### Marktzins-Modell\n",
"\n",
"- **R²:** ca. 0,055 (5,5 % der Varianz erklärt) \n",
"- **Mean Squared Error (MSE):** ca. 0,151 \n",
"- **Koeffizient:** +0,0405 \n",
"- **p-Wert:** 0,000 \n",
"\n",
"*Kommentar:* \n",
"Das Modell zeigt einen signifikanten Zusammenhang zwischen dem Marktzins und dem Kündigungsstatus, der Erklärungsgrad ist jedoch moderat.\n",
"\n",
"---\n",
"\n",
"### Tarifzins-Modell\n",
"\n",
"- **R²:** ca. 0,108 (10,8 % der Varianz erklärt) \n",
"- **Mean Squared Error (MSE):** ca. 0,142 \n",
"- **Koeffizient:** –0,0473 \n",
"- **p-Wert:** 0,000 \n",
"\n",
"*Kommentar:* \n",
"Dieses Modell liefert den höchsten Erklärungsgrad und den niedrigsten MSE der drei Varianten. Der negative Koeffizient deutet darauf hin, dass ein höherer Tarifzins mit einem geringeren Kündigungsrisiko assoziiert ist. Insgesamt ist das Tarifzins-Modell am aussagekräftigsten.\n",
"\n",
"---\n",
"\n",
"### Jahr-Modell\n",
"\n",
"- **R²:** ca. 0,046 (4,6 % der Varianz erklärt) \n",
"- **Mean Squared Error (MSE):** ca. 0,152 \n",
"- **Koeffizient:** +0,0355 \n",
"- **p-Wert:** 0,000 \n",
"\n",
"*Kommentar:* \n",
"Das Jahr-Modell weist den geringsten Erklärungsgrad und den höchsten MSE auf, weshalb es im Vergleich zu den anderen beiden Modellen weniger informativ erscheint.\n",
"\n",
"---\n",
"\n",
"## Zusammenfassung\n",
"\n",
"- **Statistische Signifikanz:** Alle Modelle zeigen signifikante Zusammenhänge (p < 0,001).\n",
"- **Bester Erklärungsgrad:** Das Tarifzins-Modell (R² ca. 10,8 %).\n",
"- **Niedrigster MSE:** Ebenfalls das Tarifzins-Modell (MSE ca. 0,142).\n",
"\n",
"Das **Tarifzins-Modell** ist demnach am aussagekräftigsten, während das **Jahr-Modell** den geringsten Beitrag zur Erklärung der Varianz im Kündigungsstatus liefert."
]
},
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"id": "fc23f104",
"metadata": {},
"source": [
"# "
]
}
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"display_name": "base",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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"pygments_lexer": "ipython3",
"version": "3.10.9"
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