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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# KNN Model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Datensatz laden\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Vertragsnummer | \n",
+ " Jahr | \n",
+ " Vertragsalter | \n",
+ " Status | \n",
+ " Marktzins | \n",
+ " Tarifzins | \n",
+ " Guthaben | \n",
+ " Sparbeitrag | \n",
+ " Beruf | \n",
+ " Alter | \n",
+ " p | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 10000 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " gekuendigt | \n",
+ " 8.7 | \n",
+ " 8.7 | \n",
+ " 3933.85 | \n",
+ " 3619.0 | \n",
+ " angestellt | \n",
+ " 48 | \n",
+ " 0.154465 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 9999 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " aktiv | \n",
+ " 8.7 | \n",
+ " 8.7 | \n",
+ " 523.93 | \n",
+ " 482.0 | \n",
+ " angestellt | \n",
+ " 28 | \n",
+ " 0.154465 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 9998 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " aktiv | \n",
+ " 8.7 | \n",
+ " 8.7 | \n",
+ " 7281.81 | \n",
+ " 6699.0 | \n",
+ " selbstaendig | \n",
+ " 22 | \n",
+ " 0.231475 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 9997 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " aktiv | \n",
+ " 8.7 | \n",
+ " 8.7 | \n",
+ " 8905.79 | \n",
+ " 8193.0 | \n",
+ " angestellt | \n",
+ " 26 | \n",
+ " 0.154465 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 9996 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " aktiv | \n",
+ " 8.7 | \n",
+ " 8.7 | \n",
+ " 8566.65 | \n",
+ " 7881.0 | \n",
+ " angestellt | \n",
+ " 30 | \n",
+ " 0.154465 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "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": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "data = pd.read_csv('../3_lernfall/VertragsdatenABCAG.csv', sep=',', decimal='.')\n",
+ "data.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 2. Datenvorverarbeitung"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "# One hot encoding für die Spalten \"Beruf\" und \"Status\"\n",
+ "data = pd.get_dummies(data, columns=['Beruf', 'Status'], drop_first=True)\n",
+ "\n",
+ "# Zielvariable und Merkmale definieren\n",
+ "y = data['Status_gekuendigt']\n",
+ "X = data.drop(['Status_gekuendigt', 'Status_aktiv', 'Status_verstorben'], axis=1)\n",
+ "\n",
+ "# Daten standardisieren\n",
+ "scaler = StandardScaler()\n",
+ "X_scaled = scaler.fit_transform(X)\n",
+ "\n",
+ "# Train-Test-Split\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=11)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3. KNN Modell implementieren und MSE berechnen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Werte für k definieren\n",
+ "k_values = [1, 10, 100]\n",
+ "train_mse = []\n",
+ "test_mse = []\n",
+ "\n",
+ "for k in k_values:\n",
+ " model = KNeighborsClassifier(n_neighbors=k)\n",
+ " model.fit(X_train, y_train)\n",
+ " y_train_pred = model.predict(X_train)\n",
+ " y_test_pred = model.predict(X_test)\n",
+ " train_mse.append(mean_squared_error(y_train, y_train_pred))\n",
+ " test_mse.append(mean_squared_error(y_test, y_test_pred))\n",
+ "\n",
+ "# Ergebnisse visualisieren\n",
+ "plt.figure(figsize=(10, 6))\n",
+ "plt.plot(k_values, train_mse, label='Train MSE', marker='o')\n",
+ "plt.plot(k_values, test_mse, label='Test MSE', marker='o')\n",
+ "plt.title('MSE für verschiedene Werte von k')\n",
+ "plt.xlabel('Wert von k')\n",
+ "plt.ylabel('Mean Squared Error')\n",
+ "plt.xscale('log')\n",
+ "plt.legend()\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "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": 4
+}