added kmeans and downproj

05_ML/code/supervised_3_svm.ipynb

@@ -1311,14 +1311,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy: 0.9473684210526315\n",
+      "Accuracy: 0.9824561403508771\n",
       "Accuracy: 0.956140350877193\n"
      ]
     }
@@ -1340,7 +1340,7 @@
     "# Aufteilen der Daten in Trainings- und Testset (80% Training, 20% Test)\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
     "# SVM-Modell mit RBF-Kernel\n",
-    "svm_model = SVC(kernel='rbf', gamma='scale', C=1.0)\n",
+    "svm_model = SVC(kernel='rbf', gamma='scale', C=100)\n",
     "svm_model = svm_model.fit(X_train, y_train)\n",
     "# Vorhersagen auf dem Testset\n",
     "y_pred = svm_model.predict(X_test)\n",
@@ -1361,7 +1361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1467,20 +1467,6 @@
     "    plt.title('SVM Decision Boundary')\n",
     "    plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {