diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..21d0b898
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.venv/
diff --git a/Decision_tree.ipynb b/Decision_tree.ipynb
index d7243552..501ce55d 100644
--- a/Decision_tree.ipynb
+++ b/Decision_tree.ipynb
@@ -66,7 +66,7 @@
     "from sklearn.metrics import mean_squared_error, mean_absolute_error , r2_score\n",
     "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
     "from scipy.stats import randint\n",
-    "from sklearn.naive_bayes import NB\n",
+    "\n",
     "\n",
     "\n",
     "\n",
@@ -121,12 +121,9 @@
     "    precision=2        # Limit decimals\n",
     ")\n",
     "\n",
-    "plt.savefig('decision_tree.pdf', format='pdf', dpi=300)\n",
+    "#plt.savefig('decision_tree.pdf', format='pdf', dpi=300)\n",
     "plt.show()\n",
     "\n",
-    "#mse = mean_squared_error(y_val, y_pred)\n",
-    "#print(f'Mean Squared Error: {mse}')\n",
-    "\n",
     "CM = confusion_matrix(y_val, y_pred)\n",
     "disp = ConfusionMatrixDisplay(confusion_matrix=CM, display_labels=[\"Poor\", \"Rich\"])\n",
     "disp.plot(cmap=\"Blues\")\n",
@@ -150,27 +147,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "id": "e567e4e9",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Classification Report:\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "        Poor       0.89      0.93      0.91      4524\n",
-      "        Rich       0.74      0.64      0.69      1509\n",
-      "\n",
-      "    accuracy                           0.86      6033\n",
-      "   macro avg       0.81      0.78      0.80      6033\n",
-      "weighted avg       0.85      0.86      0.85      6033\n",
-      "\n",
-      "Best max_depth: 11\n",
-      "Best min_samples_split: 3\n",
-      "Best min_samples_leaf: 4\n"
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m      6\u001b[39m skf = StratifiedKFold(n_splits=\u001b[32m10\u001b[39m, shuffle=\u001b[38;5;28;01mTrue\u001b[39;00m, random_state=\u001b[32m42\u001b[39m)\n\u001b[32m      7\u001b[39m grid_search = GridSearchCV(model, param_grid, scoring=\u001b[33m'\u001b[39m\u001b[33maccuracy\u001b[39m\u001b[33m'\u001b[39m, cv=skf, n_jobs=-\u001b[32m1\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m \u001b[43mgrid_search\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     11\u001b[39m \u001b[38;5;66;03m# Best model training\u001b[39;00m\n\u001b[32m     12\u001b[39m best_model = grid_search.best_estimator_\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/base.py:1365\u001b[39m, in \u001b[36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[39m\u001b[34m(estimator, *args, **kwargs)\u001b[39m\n\u001b[32m   1358\u001b[39m     estimator._validate_params()\n\u001b[32m   1360\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[32m   1361\u001b[39m     skip_parameter_validation=(\n\u001b[32m   1362\u001b[39m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[32m   1363\u001b[39m     )\n\u001b[32m   1364\u001b[39m ):\n\u001b[32m-> \u001b[39m\u001b[32m1365\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1051\u001b[39m, in \u001b[36mBaseSearchCV.fit\u001b[39m\u001b[34m(self, X, y, **params)\u001b[39m\n\u001b[32m   1045\u001b[39m     results = \u001b[38;5;28mself\u001b[39m._format_results(\n\u001b[32m   1046\u001b[39m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[32m   1047\u001b[39m     )\n\u001b[32m   1049\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[32m-> \u001b[39m\u001b[32m1051\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1053\u001b[39m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[32m   1054\u001b[39m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[32m   1055\u001b[39m first_test_score = all_out[\u001b[32m0\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mtest_scores\u001b[39m\u001b[33m\"\u001b[39m]\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1605\u001b[39m, in \u001b[36mGridSearchCV._run_search\u001b[39m\u001b[34m(self, evaluate_candidates)\u001b[39m\n\u001b[32m   1603\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_run_search\u001b[39m(\u001b[38;5;28mself\u001b[39m, evaluate_candidates):\n\u001b[32m   1604\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Search all candidates in param_grid\"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1605\u001b[39m     \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\u001b[43mParameterGrid\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mparam_grid\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/model_selection/_search.py:997\u001b[39m, in \u001b[36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[39m\u001b[34m(candidate_params, cv, more_results)\u001b[39m\n\u001b[32m    989\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.verbose > \u001b[32m0\u001b[39m:\n\u001b[32m    990\u001b[39m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[32m    991\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mFitting \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[33m folds for each of \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[33m candidates,\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    992\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33m totalling \u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[33m fits\u001b[39m\u001b[33m\"\u001b[39m.format(\n\u001b[32m    993\u001b[39m             n_splits, n_candidates, n_candidates * n_splits\n\u001b[32m    994\u001b[39m         )\n\u001b[32m    995\u001b[39m     )\n\u001b[32m--> \u001b[39m\u001b[32m997\u001b[39m out = \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    998\u001b[39m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    999\u001b[39m \u001b[43m        \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_estimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1000\u001b[39m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1001\u001b[39m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1002\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1003\u001b[39m \u001b[43m        \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1004\u001b[39m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m=\u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1005\u001b[39m \u001b[43m        \u001b[49m\u001b[43msplit_progress\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_splits\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1006\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcandidate_progress\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_candidates\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1007\u001b[39m \u001b[43m        \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mfit_and_score_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1008\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1009\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mproduct\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1010\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcandidate_params\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1011\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcv\u001b[49m\u001b[43m.\u001b[49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mrouted_params\u001b[49m\u001b[43m.\u001b[49m\u001b[43msplitter\u001b[49m\u001b[43m.\u001b[49m\u001b[43msplit\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1012\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1013\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1015\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) < \u001b[32m1\u001b[39m:\n\u001b[32m   1016\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m   1017\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mNo fits were performed. \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1018\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mWas the CV iterator empty? \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1019\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mWere there no candidates?\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   1020\u001b[39m     )\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/sklearn/utils/parallel.py:82\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m     73\u001b[39m warning_filters = warnings.filters\n\u001b[32m     74\u001b[39m iterable_with_config_and_warning_filters = (\n\u001b[32m     75\u001b[39m     (\n\u001b[32m     76\u001b[39m         _with_config_and_warning_filters(delayed_func, config, warning_filters),\n\u001b[32m   (...)\u001b[39m\u001b[32m     80\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[32m     81\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m82\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config_and_warning_filters\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/joblib/parallel.py:2072\u001b[39m, in \u001b[36mParallel.__call__\u001b[39m\u001b[34m(self, iterable)\u001b[39m\n\u001b[32m   2066\u001b[39m \u001b[38;5;66;03m# The first item from the output is blank, but it makes the interpreter\u001b[39;00m\n\u001b[32m   2067\u001b[39m \u001b[38;5;66;03m# progress until it enters the Try/Except block of the generator and\u001b[39;00m\n\u001b[32m   2068\u001b[39m \u001b[38;5;66;03m# reaches the first `yield` statement. This starts the asynchronous\u001b[39;00m\n\u001b[32m   2069\u001b[39m \u001b[38;5;66;03m# dispatch of the tasks to the workers.\u001b[39;00m\n\u001b[32m   2070\u001b[39m \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[32m-> \u001b[39m\u001b[32m2072\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/joblib/parallel.py:1682\u001b[39m, in \u001b[36mParallel._get_outputs\u001b[39m\u001b[34m(self, iterator, pre_dispatch)\u001b[39m\n\u001b[32m   1679\u001b[39m     \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[32m   1681\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m._backend.retrieval_context():\n\u001b[32m-> \u001b[39m\u001b[32m1682\u001b[39m         \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m._retrieve()\n\u001b[32m   1684\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mGeneratorExit\u001b[39;00m:\n\u001b[32m   1685\u001b[39m     \u001b[38;5;66;03m# The generator has been garbage collected before being fully\u001b[39;00m\n\u001b[32m   1686\u001b[39m     \u001b[38;5;66;03m# consumed. This aborts the remaining tasks if possible and warn\u001b[39;00m\n\u001b[32m   1687\u001b[39m     \u001b[38;5;66;03m# the user if necessary.\u001b[39;00m\n\u001b[32m   1688\u001b[39m     \u001b[38;5;28mself\u001b[39m._exception = \u001b[38;5;28;01mTrue\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/MLP/Projects/MLPproject/.venv/lib/python3.12/site-packages/joblib/parallel.py:1800\u001b[39m, in \u001b[36mParallel._retrieve\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m   1789\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.return_ordered:\n\u001b[32m   1790\u001b[39m     \u001b[38;5;66;03m# Case ordered: wait for completion (or error) of the next job\u001b[39;00m\n\u001b[32m   1791\u001b[39m     \u001b[38;5;66;03m# that have been dispatched and not retrieved yet. If no job\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1795\u001b[39m     \u001b[38;5;66;03m# control only have to be done on the amount of time the next\u001b[39;00m\n\u001b[32m   1796\u001b[39m     \u001b[38;5;66;03m# dispatched job is pending.\u001b[39;00m\n\u001b[32m   1797\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m (nb_jobs == \u001b[32m0\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[32m   1798\u001b[39m         \u001b[38;5;28mself\u001b[39m._jobs[\u001b[32m0\u001b[39m].get_status(timeout=\u001b[38;5;28mself\u001b[39m.timeout) == TASK_PENDING\n\u001b[32m   1799\u001b[39m     ):\n\u001b[32m-> \u001b[39m\u001b[32m1800\u001b[39m         \u001b[43mtime\u001b[49m\u001b[43m.\u001b[49m\u001b[43msleep\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m0.01\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m   1801\u001b[39m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m   1803\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m nb_jobs == \u001b[32m0\u001b[39m:\n\u001b[32m   1804\u001b[39m     \u001b[38;5;66;03m# Case unordered: jobs are added to the list of jobs to\u001b[39;00m\n\u001b[32m   1805\u001b[39m     \u001b[38;5;66;03m# retrieve `self._jobs` only once completed or in error, which\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1811\u001b[39m     \u001b[38;5;66;03m# timeouts before any other dispatched job has completed and\u001b[39;00m\n\u001b[32m   1812\u001b[39m     \u001b[38;5;66;03m# been added to `self._jobs` to be retrieved.\u001b[39;00m\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: "
      ]
     }
    ],
@@ -212,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "e68b1ea9",
    "metadata": {},
    "outputs": [],
@@ -236,13 +233,13 @@
     "            X, y, test_size=test_size, stratify=y, random_state=random_state)\n",
     "\n",
     "        # Scale features\n",
-    "        scaler = StandardScaler()\n",
-    "        self.X_train = scaler.fit_transform(X_train)\n",
-    "        self.X_test  = scaler.transform(X_test)\n",
+    "        #scaler = StandardScaler()\n",
+    "        #self.X_train = scaler.fit_transform(X_train)\n",
+    "        #self.X_test  = scaler.transform(X_test)\n",
     "\n",
     "        # Scaling not necessary for decision trees\n",
-    "        #self.X_train = X_train\n",
-    "        #self.X_test = X_test\n",
+    "        self.X_train = X_train\n",
+    "        self.X_test = X_test\n",
     "\n",
     "        self.y_train       = y_train\n",
     "        self.y_test        = y_test\n",
@@ -275,7 +272,6 @@
     "\n",
     "        results_list = []\n",
     "        cv = StratifiedKFold(n_splits=self.cv_folds, shuffle=True, random_state=42)\n",
-    "        #models_with_params = self.get_models_with_params()\n",
     "\n",
     "        for name, (model, param_grid) in models_with_params.items():\n",
     "            print(f\"Tuning {name} ...\")\n",
@@ -359,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 6,
    "id": "d6fd1fee",
    "metadata": {},
    "outputs": [
@@ -372,13 +368,13 @@
       "\n",
       "------ Results Sorted by Accuracy ------\n",
       "        Model  Accuracy  Precision   Recall  F1 Score                                                     Best Params  Training Time (s)  Prediction Time (s)  Total Time (s)\n",
-      "Random Forest  0.858777   0.853301 0.858777  0.853292 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         186.568059             0.070525      186.638585\n",
-      "Decision Tree  0.848168   0.844754 0.848168  0.846079                       {'max_depth': 10, 'min_samples_split': 5}          11.661536             0.001839       11.663375\n",
+      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         160.295069             0.188931      160.484000\n",
+      "Decision Tree  0.848334   0.844900 0.848334  0.846228                       {'max_depth': 10, 'min_samples_split': 5}          11.927628             0.006326       11.933954\n",
       "\n",
       "------ Results Sorted by Total Time ------\n",
       "        Model  Accuracy  Precision   Recall  F1 Score                                                     Best Params  Training Time (s)  Prediction Time (s)  Total Time (s)\n",
-      "Decision Tree  0.848168   0.844754 0.848168  0.846079                       {'max_depth': 10, 'min_samples_split': 5}          11.661536             0.001839       11.663375\n",
-      "Random Forest  0.858777   0.853301 0.858777  0.853292 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         186.568059             0.070525      186.638585\n"
+      "Decision Tree  0.848334   0.844900 0.848334  0.846228                       {'max_depth': 10, 'min_samples_split': 5}          11.927628             0.006326       11.933954\n",
+      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         160.295069             0.188931      160.484000\n"
      ]
     }
    ],
@@ -386,7 +382,7 @@
     "# optimized classifiers and find their hyperparameters\n",
     "clf_opt = ClassifierComparisonOpt(X_train_val, y_train_val)\n",
     "clf_opt.fit_models()\n",
-    "clf_opt.print_summary()\n"
+    "clf_opt.print_summary()"
    ]
   },
   {
@@ -400,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 7,
    "id": "4185a428",
    "metadata": {},
    "outputs": [
@@ -413,13 +409,13 @@
       "\n",
       "------ Results Sorted by Accuracy ------\n",
       "        Model  Accuracy  Precision   Recall  F1 Score                                                     Best Params  Training Time (s)  Prediction Time (s)  Total Time (s)\n",
-      "Random Forest  0.860766   0.855462 0.860766  0.854443 {'max_depth': 15, 'min_samples_split': 10, 'n_estimators': 100}         145.080616             0.069650      145.150266\n",
-      "Decision Tree  0.849163   0.843062 0.849163  0.839388                        {'max_depth': 8, 'min_samples_split': 6}           1.626699             0.001421        1.628121\n",
+      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         167.364476             0.126707      167.491183\n",
+      "Decision Tree  0.849163   0.843062 0.849163  0.839388                        {'max_depth': 8, 'min_samples_split': 6}           2.160097             0.003498        2.163594\n",
       "\n",
       "------ Results Sorted by Total Time ------\n",
       "        Model  Accuracy  Precision   Recall  F1 Score                                                     Best Params  Training Time (s)  Prediction Time (s)  Total Time (s)\n",
-      "Decision Tree  0.849163   0.843062 0.849163  0.839388                        {'max_depth': 8, 'min_samples_split': 6}           1.626699             0.001421        1.628121\n",
-      "Random Forest  0.860766   0.855462 0.860766  0.854443 {'max_depth': 15, 'min_samples_split': 10, 'n_estimators': 100}         145.080616             0.069650      145.150266\n"
+      "Decision Tree  0.849163   0.843062 0.849163  0.839388                        {'max_depth': 8, 'min_samples_split': 6}           2.160097             0.003498        2.163594\n",
+      "Random Forest  0.858942   0.853479 0.858942  0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100}         167.364476             0.126707      167.491183\n"
      ]
     },
     {
@@ -434,7 +430,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -479,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "id": "776cdb17",
    "metadata": {},
    "outputs": [
@@ -611,7 +607,6 @@
    "source": [
     "# Remove redundant features\n",
     "X_filtered = X_train_val.drop(columns=['sex', 'native.country', 'marital.status', 'race', 'occupation', 'workclass'])\n",
-    "# ===== MAIN =====\n",
     "\n",
     "# optimized classifiers and find their hyperparameters\n",
     "clf_opt = ClassifierComparisonOpt(X_filtered, y_train_val)\n",
diff --git a/decision_tree.pdf b/decision_tree.pdf
index 9510187d..13f459fe 100644
Binary files a/decision_tree.pdf and b/decision_tree.pdf differ