added some Information

notebooks/decision_tree.ipynb

@@ -7,6 +7,29 @@
     "# Decison Tree"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "import os\n",
+    "from datetime import datetime\n",
+    "from joblib import dump, load\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xgboost as xgb\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import confusion_matrix, f1_score\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "import seaborn as sns"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -16,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,9 +63,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train_x shape: (3502, 10)\n",
+      "test_x shape: (438, 10)\n",
+      "valid_x shape: (438, 10)\n",
+      "features: ['age', 'gender', 'artial_rate', 'ventricular_rate', 'qrs_duration', 'qt_length', 'qrs_count', 'q_peak', 'r_axis', 't_axis']\n",
+      "number of classes: 4\n"
+     ]
+    }
+   ],
    "source": [
     "# get the target and features\n",
     "train_y = train['y']\n",
@@ -93,11 +128,65 @@
     "num_classes= len(set(valid_y.to_list()))\n",
     "print('number of classes:', num_classes)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validierungsgenauigkeit: 0.7557077625570776\n",
+      "Testgenauigkeit: 0.7922374429223744\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Schritt 1: Importiere die notwendigen Bibliotheken\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "# Schritt 2: Erstelle das Decision Tree Modell\n",
+    "# Beispiel: Begrenzung der Tiefe des Baumes\n",
+    "dt_classifier = DecisionTreeClassifier(max_depth=5)\n",
+    "\n",
+    "# Schritt 3: Trainiere das Modell\n",
+    "dt_classifier.fit(train_x, train_y)\n",
+    "\n",
+    "# Schritt 4: Vorhersagen und Auswertung auf den Validierungsdaten\n",
+    "valid_pred = dt_classifier.predict(valid_x)\n",
+    "valid_accuracy = accuracy_score(valid_y, valid_pred)\n",
+    "print(f'Validierungsgenauigkeit: {valid_accuracy}')\n",
+    "\n",
+    "# Optional: Tuning des Modells basierend auf den Validierungsergebnissen\n",
+    "# Experimentiere mit verschiedenen Parametern und trainiere bei Bedarf neu\n",
+    "\n",
+    "# Schritt 6: Endgültige Bewertung mit Testdaten\n",
+    "test_pred = dt_classifier.predict(test_x)\n",
+    "test_accuracy = accuracy_score(test_y, test_pred)\n",
+    "print(f'Testgenauigkeit: {test_accuracy}')\n"
+   ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,