{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c2fce892-a05e-493e-8ba7-659bd9495e00",
   "metadata": {},
   "source": [
    "# Fallstudie: Vorhersage von Immobilienpreisen\n",
    "\n",
    "## Ziel dieser Fallstudie\n",
    "\n",
    "* Anwendung der erlernten Methoden zur Vorhersage von Immobilienpreisen.\n",
    "* Verwendung eines realen Datensatzes zur Modellierung.\n",
    "* Umsetzung in Python mit `scikit-learn`.\n",
    "\n",
    "## Schritte zur Umsetzung\n",
    "\n",
    "1. Daten laden und verstehen\n",
    "\n",
    "   * Nutzung eines offenen Datensatzes (z.B. California Housing Dataset oder Kaggle Immobilienpreise).\n",
    "   * Untersuchung der Datenverteilung, Korrelationen und möglicher Ausreißer.\n",
    "\n",
    "2. Datenvorbereitung\n",
    "\n",
    "   * Umwandlung kategorischer Merkmale (One-Hot-Encoding).\n",
    "   * Normalisierung und Skalierung numerischer Merkmale.\n",
    "   * Aufteilung in Trainings- und Testdaten.\n",
    "\n",
    "3. Modelltraining mit Linearer Regression\n",
    "\n",
    "   * Trainieren eines linearen Regressionsmodells mit scikit-learn.\n",
    "   * Verwendung von Metriken zur Bewertung der Modellgüte (z.B. MSE, R²).\n",
    "\n",
    "4. Modellbewertung und Interpretation\n",
    "\n",
    "   * Bewertung der Modellperformance auf dem Testdatensatz.\n",
    "   * Interpretation der wichtigsten Einflussgrößen.\n",
    "\n",
    "## Code-Beispiel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d7f69c6d-4db0-484d-9c68-c3ec4fce16b9",
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    "execution": {
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   "source": [
    "import sys\n",
    "# !{sys.executable} -m pip install scikit-learn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d22c329-4a99-4a23-af24-6e4b2eafec89",
   "metadata": {},
   "source": [
    "### Importe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9dc4ae8-b7b7-48c2-b4bb-8d8e28cd3f82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:32:57.743235Z",
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     "shell.execute_reply": "2026-03-24T15:32:58.836398Z",
     "shell.execute_reply.started": "2026-03-24T15:32:57.743226Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7346190a-b639-455f-95ef-420470149e57",
   "metadata": {},
   "source": [
    "### Beispieldatensatz laden (California Housing Dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "950468c1-4b1f-4f1d-987f-8c09ed9f18c9",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-03-24T15:32:58.884204Z",
     "shell.execute_reply": "2026-03-24T15:32:58.883553Z",
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   "outputs": [
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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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>1.5603</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.045455</td>\n",
       "      <td>1.133333</td>\n",
       "      <td>845.0</td>\n",
       "      <td>2.560606</td>\n",
       "      <td>39.48</td>\n",
       "      <td>-121.09</td>\n",
       "      <td>0.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>2.5568</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.114035</td>\n",
       "      <td>1.315789</td>\n",
       "      <td>356.0</td>\n",
       "      <td>3.122807</td>\n",
       "      <td>39.49</td>\n",
       "      <td>-121.21</td>\n",
       "      <td>0.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>1.7000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.205543</td>\n",
       "      <td>1.120092</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>2.325635</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.22</td>\n",
       "      <td>0.923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>1.8672</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.329513</td>\n",
       "      <td>1.171920</td>\n",
       "      <td>741.0</td>\n",
       "      <td>2.123209</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.32</td>\n",
       "      <td>0.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>2.3886</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.254717</td>\n",
       "      <td>1.162264</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>2.616981</td>\n",
       "      <td>39.37</td>\n",
       "      <td>-121.24</td>\n",
       "      <td>0.894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0      8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1      8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2      7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3      5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4      3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "...       ...       ...       ...        ...         ...       ...       ...   \n",
       "20635  1.5603      25.0  5.045455   1.133333       845.0  2.560606     39.48   \n",
       "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49   \n",
       "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43   \n",
       "20638  1.8672      18.0  5.329513   1.171920       741.0  2.123209     39.43   \n",
       "20639  2.3886      16.0  5.254717   1.162264      1387.0  2.616981     39.37   \n",
       "\n",
       "       Longitude  PRICE  \n",
       "0        -122.23  4.526  \n",
       "1        -122.22  3.585  \n",
       "2        -122.24  3.521  \n",
       "3        -122.25  3.413  \n",
       "4        -122.25  3.422  \n",
       "...          ...    ...  \n",
       "20635    -121.09  0.781  \n",
       "20636    -121.21  0.771  \n",
       "20637    -121.22  0.923  \n",
       "20638    -121.32  0.847  \n",
       "20639    -121.24  0.894  \n",
       "\n",
       "[20640 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "df = pd.DataFrame(housing.data, columns=housing.feature_names)\n",
    "df[\"PRICE\"] = housing.target\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1cd4396-c456-40cf-94d4-2b77343816f9",
   "metadata": {},
   "source": [
    "## Aufteilung in Merkmale (`x`) und Zielvariable (`y`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3678106-3be7-4f56-82c6-69bcaab16098",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:32:58.884565Z",
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     "iopub.status.idle": "2026-03-24T15:32:58.887140Z",
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     "shell.execute_reply.started": "2026-03-24T15:32:58.884544Z"
    }
   },
   "outputs": [],
   "source": [
    "x = df.drop(\"PRICE\", axis=1)\n",
    "y = df[\"PRICE\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fda3eb3-4cbd-4a8f-892a-184f67805fbd",
   "metadata": {},
   "source": [
    "## Aufteilung in Trainings- und Testsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a49903ee-a733-4811-86d4-1461484b23da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:32:58.887461Z",
     "iopub.status.busy": "2026-03-24T15:32:58.887382Z",
     "iopub.status.idle": "2026-03-24T15:32:58.890931Z",
     "shell.execute_reply": "2026-03-24T15:32:58.890581Z",
     "shell.execute_reply.started": "2026-03-24T15:32:58.887454Z"
    }
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    x, y, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12d3948-f89a-485e-a436-d50535628b56",
   "metadata": {},
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9233e7e2-a869-4518-a1a8-2f56933e7b4c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:32:58.891267Z",
     "iopub.status.busy": "2026-03-24T15:32:58.891201Z",
     "iopub.status.idle": "2026-03-24T15:32:58.895490Z",
     "shell.execute_reply": "2026-03-24T15:32:58.894871Z",
     "shell.execute_reply.started": "2026-03-24T15:32:58.891260Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_test_scaled = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4488417-0ffd-44ec-aedc-aa958f50e619",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4764cb7b-6fef-4449-b692-4ad8630d3b09",
   "metadata": {},
   "source": [
    "## Lineare Regression trainieren"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8755422-164b-488e-936b-253c2c3d67de",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-03-24T15:32:58.905015Z",
     "shell.execute_reply": "2026-03-24T15:32:58.904552Z",
     "shell.execute_reply.started": "2026-03-24T15:32:58.895922Z"
    }
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   "outputs": [
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       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
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       "\n",
       "#sk-container-id-1.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
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       "\n",
       "#sk-container-id-1.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
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       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
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       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
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       "\n",
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       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
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       "  background-position: center center;\n",
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       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Whether to calculate the intercept for this model. If set<br>to False, no intercept will be used in calculations<br>(i.e. data is expected to be centered).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy_X',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=copy_X,-bool%2C%20default%3DTrue\">\n",
       "            copy_X\n",
       "            <span class=\"param-doc-description\">copy_X: bool, default=True<br><br>If True, X will be copied; else, it may be overwritten.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
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       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=tol,-float%2C%20default%3D1e-6\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-6<br><br>The precision of the solution (`coef_`) is determined by `tol` which<br>specifies a different convergence criterion for the `lsqr` solver.<br>`tol` is set as `atol` and `btol` of :func:`scipy.sparse.linalg.lsqr` when<br>fitting on sparse training data. This parameter has no effect when fitting<br>on dense data.<br><br>.. versionadded:: 1.7</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1e-06</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to use for the computation. This will only provide<br>speedup in case of sufficiently large problems, that is if firstly<br>`n_targets > 1` and secondly `X` is sparse or if `positive` is set<br>to `True`. ``None`` means 1 unless in a<br>:obj:`joblib.parallel_backend` context. ``-1`` means using all<br>processors. See :term:`Glossary <n_jobs>` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('positive',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LinearRegression.html#:~:text=positive,-bool%2C%20default%3DFalse\">\n",
       "            positive\n",
       "            <span class=\"param-doc-description\">positive: bool, default=False<br><br>When set to ``True``, forces the coefficients to be positive. This<br>option is only supported for dense arrays.<br><br>For a comparison between a linear regression model with positive constraints<br>on the regression coefficients and a linear regression without such constraints,<br>see :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(x_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "288f2010-48e6-4ec0-9123-82cfa2709522",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59a43b36-584b-44a1-8886-794e6dae7387",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:32:58.905543Z",
     "iopub.status.busy": "2026-03-24T15:32:58.905457Z",
     "iopub.status.idle": "2026-03-24T15:32:58.908309Z",
     "shell.execute_reply": "2026-03-24T15:32:58.907681Z",
     "shell.execute_reply.started": "2026-03-24T15:32:58.905536Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.85438303,  0.12254624, -0.29441013,  0.33925949, -0.00230772,\n",
       "       -0.0408291 , -0.89692888, -0.86984178])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f1d07fc0-8600-44be-b5b4-50a6352b7846",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2026-03-24T15:32:58.908684Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(2.071946937378881)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a770e9-051c-4842-b54d-75c67a5df7a2",
   "metadata": {},
   "source": [
    "## Vorhersagen treffen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "84deab4f-653c-496e-b72e-76a3cedb0575",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:32:58.912273Z",
     "iopub.status.busy": "2026-03-24T15:32:58.912187Z",
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     "shell.execute_reply": "2026-03-24T15:32:58.913921Z",
     "shell.execute_reply.started": "2026-03-24T15:32:58.912265Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c9e0185e-dbd3-4c8c-8930-eb6567c65da0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:32:58.914818Z",
     "iopub.status.busy": "2026-03-24T15:32:58.914736Z",
     "iopub.status.idle": "2026-03-24T15:32:58.917125Z",
     "shell.execute_reply": "2026-03-24T15:32:58.916698Z",
     "shell.execute_reply.started": "2026-03-24T15:32:58.914811Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.71912284, 1.76401657, 2.70965883, ..., 4.46877017, 1.18751119,\n",
       "       2.00940251], shape=(4128,))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad49305-42aa-43b6-94ea-6e8ee0640802",
   "metadata": {},
   "source": [
    "## Modellbewertung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a1e7040-cc53-4f9f-afa1-0c8c9ff1e6dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-24T15:32:58.917505Z",
     "iopub.status.busy": "2026-03-24T15:32:58.917422Z",
     "iopub.status.idle": "2026-03-24T15:32:58.920246Z",
     "shell.execute_reply": "2026-03-24T15:32:58.919710Z",
     "shell.execute_reply.started": "2026-03-24T15:32:58.917498Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mittlerer quadratischer Fehler (MSE): 0.5558915986952444\n",
      "Bestimmtheitsmaß (R²): 0.5757877060324508\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"Mittlerer quadratischer Fehler (MSE): {mse}\")\n",
    "print(f\"Bestimmtheitsmaß (R²): {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e69757f7-1f5b-426a-b721-23bcabb048bf",
   "metadata": {},
   "source": [
    "## Visualisierung der Vorhersagen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16270434-db30-472f-ab59-033142994c49",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-03-24T15:32:58.920586Z",
     "iopub.status.idle": "2026-03-24T15:32:58.983170Z",
     "shell.execute_reply": "2026-03-24T15:32:58.982519Z",
     "shell.execute_reply.started": "2026-03-24T15:32:58.920648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred, alpha=0.4)\n",
    "plt.xlabel(\"Tatsächliche Preise\")\n",
    "plt.ylabel(\"Vorhergesagte Preise\")\n",
    "plt.title(\"Tatsächliche vs. Vorhergesagte Immobilienpreise\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}