.

Decision_tree.ipynb

@@ -500,7 +500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "a470fd20",
    "metadata": {},
    "outputs": [
@@ -522,7 +522,7 @@
     },
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -543,7 +543,23 @@
       "    accuracy                           0.86      6033\n",
       "   macro avg       0.83      0.79      0.80      6033\n",
       "weighted avg       0.86      0.86      0.86      6033\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Classification Report:\n",
       "              precision    recall  f1-score   support\n",
       "\n",
@@ -555,6 +571,16 @@
       "weighted avg       0.86      0.87      0.86      6033\n",
       "\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -574,7 +600,8 @@
     "CM = confusion_matrix(y_test, y_pred_dt)\n",
     "disp = ConfusionMatrixDisplay(confusion_matrix=CM, display_labels=[\"Poor\", \"Rich\"])\n",
     "disp.plot(cmap=\"Blues\")\n",
-    "plt.title(\"Test Confusion Matrix (dt)\")\n",
+    "plt.title(\"Confusion Matrix (dt)\")\n",
+    "plt.savefig('./Report/CM_dt.png', format='png', dpi=300)\n",
     "plt.show()  \n",
     "\n",
     "\n",
@@ -591,6 +618,13 @@
     "print(\"Classification Report:\")\n",
     "print(classification_report(y_test, y_pred_rf, target_names=[\"Poor\", \"Rich\"]))\n",
     "\n",
+    "CM = confusion_matrix(y_test, y_pred_rf)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=CM, display_labels=[\"Poor\", \"Rich\"])\n",
+    "disp.plot(cmap=\"Blues\")\n",
+    "plt.title(\"Confusion Matrix Random Forest\")\n",
+    "plt.savefig('./Report/CM_rf.png', format='png', dpi=300)\n",
+    "plt.show() \n",
+    "\n",
     "\n",
     "# ---------------------------- XGBoost ----------------------------\n",
     "xgb = Pipeline([\n",
@@ -603,7 +637,14 @@
     "\n",
     "# Classification Report\n",
     "print(\"Classification Report:\")\n",
-    "print(classification_report(y_test, y_pred_xgb, target_names=[\"Poor\", \"Rich\"]))"
+    "print(classification_report(y_test, y_pred_xgb, target_names=[\"Poor\", \"Rich\"]))\n",
+    "\n",
+    "CM = confusion_matrix(y_test, y_pred_xgb)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=CM, display_labels=[\"Poor\", \"Rich\"])\n",
+    "disp.plot(cmap=\"Blues\")\n",
+    "plt.title(\"Confusion Matrix XGBoost\")\n",
+    "plt.savefig('./Report/CM_xgb.png', format='png', dpi=300)\n",
+    "plt.show() "
    ]
   }
  ],