diff --git "a/A3/A3_Test.ipynb" "b/A3/A3_Test.ipynb"
--- "a/A3/A3_Test.ipynb"
+++ "b/A3/A3_Test.ipynb"
@@ -5,7 +5,16 @@
    "execution_count": 1,
    "id": "ebf9ad89-fceb-4097-8ca7-652640702196",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/cyclonaut/.local/lib/python3.12/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n",
+      "  warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n"
+     ]
+    }
+   ],
    "source": [
     "# Import all necessary libraries \n",
     "import numpy as np\n",
@@ -165,7 +174,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x600 with 1 Axes>"
       ]
@@ -475,7 +484,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Failed to train QDA: y has only 1 sample in class RightHeelRises, covariance is ill defined.\n",
+      "Failed to train QDA: The covariance matrix of class ExcessiveForwardLean is not full rank. Increase the value of `reg_param` to reduce the collinearity.\n",
       "\n",
       " Classification Performance (Baseline - 14 classes): \n"
      ]
@@ -626,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "6cecb844-b3b1-448f-b0fa-e917e4f979d7",
    "metadata": {},
    "outputs": [
@@ -636,8 +645,112 @@
      "text": [
       "\n",
       "Tuning Logistic Regression...\n",
-      "  Parameters to test: {'C': [0.001, 0.01, 0.1, 1, 10, 100], 'solver': ['lbfgs', 'saga'], 'class_weight': [None, 'balanced']}\n"
+      "  Parameters to test: {'C': [0.001, 0.01, 0.1, 1, 10, 100], 'solver': ['lbfgs', 'saga'], 'class_weight': [None, 'balanced']}\n",
+      "Best params: {'C': 100, 'class_weight': None, 'solver': 'saga'}\n",
+      "CV F1-Score: 0.5976\n",
+      "Test F1-Score: 0.6101\n",
+      "Improvement: +0.0478\n",
+      "\n",
+      "Tuning LDA...\n",
+      "  Parameters to test: {'solver': ['svd', 'lsqr', 'eigen']}\n",
+      "Best params: {'solver': 'svd'}\n",
+      "CV F1-Score: 0.5785\n",
+      "Test F1-Score: 0.5667\n",
+      "Improvement: +0.0000\n",
+      "\n",
+      "Tuning KNN (k=5)...\n",
+      "  Parameters to test: {'n_neighbors': [3, 5, 7, 9, 11, 13], 'weights': ['uniform', 'distance'], 'metric': ['euclidean', 'manhattan']}\n",
+      "Best params: {'metric': 'manhattan', 'n_neighbors': 5, 'weights': 'distance'}\n",
+      "CV F1-Score: 0.5529\n",
+      "Test F1-Score: 0.5494\n",
+      "Improvement: +0.0185\n",
+      "\n",
+      "\n",
+      "Hyperparameter Tuning Results (14-class):\n",
+      "\n",
+      "\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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>Model</th>\n",
+       "      <th>Best Params</th>\n",
+       "      <th>CV Score</th>\n",
+       "      <th>Test Accuracy</th>\n",
+       "      <th>Test F1-Score</th>\n",
+       "      <th>Improvement</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression (Tuned)</td>\n",
+       "      <td>{'C': 100, 'class_weight': None, 'solver': 'sa...</td>\n",
+       "      <td>0.597575</td>\n",
+       "      <td>0.613365</td>\n",
+       "      <td>0.610090</td>\n",
+       "      <td>0.047780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LDA (Tuned)</td>\n",
+       "      <td>{'solver': 'svd'}</td>\n",
+       "      <td>0.578549</td>\n",
+       "      <td>0.565632</td>\n",
+       "      <td>0.566721</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KNN (k=5) (Tuned)</td>\n",
+       "      <td>{'metric': 'manhattan', 'n_neighbors': 5, 'wei...</td>\n",
+       "      <td>0.552886</td>\n",
+       "      <td>0.560859</td>\n",
+       "      <td>0.549444</td>\n",
+       "      <td>0.018539</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Model  \\\n",
+       "0  Logistic Regression (Tuned)   \n",
+       "1                  LDA (Tuned)   \n",
+       "2            KNN (k=5) (Tuned)   \n",
+       "\n",
+       "                                         Best Params  CV Score  Test Accuracy  \\\n",
+       "0  {'C': 100, 'class_weight': None, 'solver': 'sa...  0.597575       0.613365   \n",
+       "1                                  {'solver': 'svd'}  0.578549       0.565632   \n",
+       "2  {'metric': 'manhattan', 'n_neighbors': 5, 'wei...  0.552886       0.560859   \n",
+       "\n",
+       "   Test F1-Score  Improvement  \n",
+       "0       0.610090     0.047780  \n",
+       "1       0.566721     0.000000  \n",
+       "2       0.549444     0.018539  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -729,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "9a43d212-4268-4921-8926-0ee1a017d771",
    "metadata": {},
    "outputs": [],
@@ -740,10 +853,193 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "2256f87c-6d23-4a03-a614-53cfe31916bc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New Class Distribution based on two Body Regions\n",
+      "WeakestRegion\n",
+      "Upper Body    1525\n",
+      "Lower Body     569\n",
+      "Name: count, dtype: int64\n",
+      "(2094, 45)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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>AimoScore</th>\n",
+       "      <th>No_1_Angle_Deviation</th>\n",
+       "      <th>No_2_Angle_Deviation</th>\n",
+       "      <th>No_3_Angle_Deviation</th>\n",
+       "      <th>No_4_Angle_Deviation</th>\n",
+       "      <th>No_5_Angle_Deviation</th>\n",
+       "      <th>No_6_Angle_Deviation</th>\n",
+       "      <th>No_7_Angle_Deviation</th>\n",
+       "      <th>No_8_Angle_Deviation</th>\n",
+       "      <th>No_9_Angle_Deviation</th>\n",
+       "      <th>...</th>\n",
+       "      <th>No_22_NASM_Deviation</th>\n",
+       "      <th>No_23_NASM_Deviation</th>\n",
+       "      <th>No_24_NASM_Deviation</th>\n",
+       "      <th>No_25_NASM_Deviation</th>\n",
+       "      <th>No_1_Time_Deviation</th>\n",
+       "      <th>No_2_Time_Deviation</th>\n",
+       "      <th>EstimatedScore</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>WeakestLink</th>\n",
+       "      <th>WeakestRegion</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.323667</td>\n",
+       "      <td>0.538020</td>\n",
+       "      <td>0.815878</td>\n",
+       "      <td>0.346724</td>\n",
+       "      <td>0.382114</td>\n",
+       "      <td>0.302248</td>\n",
+       "      <td>0.947872</td>\n",
+       "      <td>0.275945</td>\n",
+       "      <td>0.521760</td>\n",
+       "      <td>0.457198</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.552846</td>\n",
+       "      <td>0.648972</td>\n",
+       "      <td>0.578192</td>\n",
+       "      <td>0.560019</td>\n",
+       "      <td>0.821616</td>\n",
+       "      <td>0.818747</td>\n",
+       "      <td>0.209947</td>\n",
+       "      <td>0003cdcc-86ed-494a-a3b5-90d09e96e06b.Kinect</td>\n",
+       "      <td>RightShoulderElevation</td>\n",
+       "      <td>Upper Body</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.323699</td>\n",
+       "      <td>0.443807</td>\n",
+       "      <td>0.306552</td>\n",
+       "      <td>0.823529</td>\n",
+       "      <td>0.188905</td>\n",
+       "      <td>0.497370</td>\n",
+       "      <td>0.140124</td>\n",
+       "      <td>0.664275</td>\n",
+       "      <td>0.521760</td>\n",
+       "      <td>0.729316</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.805356</td>\n",
+       "      <td>0.848876</td>\n",
+       "      <td>0.889048</td>\n",
+       "      <td>0.816834</td>\n",
+       "      <td>0.307987</td>\n",
+       "      <td>0.248207</td>\n",
+       "      <td>0.457198</td>\n",
+       "      <td>003115c4-bdb8-491c-b571-8fcebdecf8ed.Kinect</td>\n",
+       "      <td>RightArmFallForward</td>\n",
+       "      <td>Upper Body</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.848327</td>\n",
+       "      <td>0.603539</td>\n",
+       "      <td>0.373984</td>\n",
+       "      <td>0.346724</td>\n",
+       "      <td>0.590626</td>\n",
+       "      <td>0.341942</td>\n",
+       "      <td>0.298900</td>\n",
+       "      <td>0.276901</td>\n",
+       "      <td>0.623625</td>\n",
+       "      <td>0.658058</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.690579</td>\n",
+       "      <td>0.648972</td>\n",
+       "      <td>0.578192</td>\n",
+       "      <td>0.555715</td>\n",
+       "      <td>0.218556</td>\n",
+       "      <td>0.235294</td>\n",
+       "      <td>0.107126</td>\n",
+       "      <td>00316bfb-ed43-489f-a55b-11c7f01c852d.Kinect</td>\n",
+       "      <td>LeftArmFallForward</td>\n",
+       "      <td>Upper Body</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   AimoScore  No_1_Angle_Deviation  No_2_Angle_Deviation  \\\n",
+       "0   0.323667              0.538020              0.815878   \n",
+       "1   0.323699              0.443807              0.306552   \n",
+       "2   0.848327              0.603539              0.373984   \n",
+       "\n",
+       "   No_3_Angle_Deviation  No_4_Angle_Deviation  No_5_Angle_Deviation  \\\n",
+       "0              0.346724              0.382114              0.302248   \n",
+       "1              0.823529              0.188905              0.497370   \n",
+       "2              0.346724              0.590626              0.341942   \n",
+       "\n",
+       "   No_6_Angle_Deviation  No_7_Angle_Deviation  No_8_Angle_Deviation  \\\n",
+       "0              0.947872              0.275945              0.521760   \n",
+       "1              0.140124              0.664275              0.521760   \n",
+       "2              0.298900              0.276901              0.623625   \n",
+       "\n",
+       "   No_9_Angle_Deviation  ...  No_22_NASM_Deviation  No_23_NASM_Deviation  \\\n",
+       "0              0.457198  ...              0.552846              0.648972   \n",
+       "1              0.729316  ...              0.805356              0.848876   \n",
+       "2              0.658058  ...              0.690579              0.648972   \n",
+       "\n",
+       "   No_24_NASM_Deviation  No_25_NASM_Deviation  No_1_Time_Deviation  \\\n",
+       "0              0.578192              0.560019             0.821616   \n",
+       "1              0.889048              0.816834             0.307987   \n",
+       "2              0.578192              0.555715             0.218556   \n",
+       "\n",
+       "   No_2_Time_Deviation  EstimatedScore  \\\n",
+       "0             0.818747        0.209947   \n",
+       "1             0.248207        0.457198   \n",
+       "2             0.235294        0.107126   \n",
+       "\n",
+       "                                            ID             WeakestLink  \\\n",
+       "0  0003cdcc-86ed-494a-a3b5-90d09e96e06b.Kinect  RightShoulderElevation   \n",
+       "1  003115c4-bdb8-491c-b571-8fcebdecf8ed.Kinect     RightArmFallForward   \n",
+       "2  00316bfb-ed43-489f-a55b-11c7f01c852d.Kinect      LeftArmFallForward   \n",
+       "\n",
+       "   WeakestRegion  \n",
+       "0     Upper Body  \n",
+       "1     Upper Body  \n",
+       "2     Upper Body  \n",
+       "\n",
+       "[3 rows x 45 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Define the groups as mentioned in Slide 6 into 3 distinct regions\n",
     "def get_region(label):\n",
@@ -770,10 +1066,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "c556f1ac-552b-4ccb-82a1-6c3b782c3c75",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Training set: 1675 samples\n",
+      "Test set: 419 samples\n"
+     ]
+    }
+   ],
    "source": [
     "# Prepare data for region wise classification\n",
     "\n",
@@ -798,10 +1104,121 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "cc39415b-8dab-4af3-bd1c-830c01735921",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failed to train QDA: The covariance matrix of class Lower Body is not full rank. Increase the value of `reg_param` to reduce the collinearity.\n",
+      "\n",
+      "Final Classification Performance\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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",
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+       "\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>Model</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>Error Rate</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "      <th>F1-Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>KNN (k=7)</td>\n",
+       "      <td>0.837709</td>\n",
+       "      <td>0.162291</td>\n",
+       "      <td>0.832673</td>\n",
+       "      <td>0.837709</td>\n",
+       "      <td>0.827772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LDA</td>\n",
+       "      <td>0.830549</td>\n",
+       "      <td>0.169451</td>\n",
+       "      <td>0.824148</td>\n",
+       "      <td>0.830549</td>\n",
+       "      <td>0.824773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>KNN (k=10)</td>\n",
+       "      <td>0.832936</td>\n",
+       "      <td>0.167064</td>\n",
+       "      <td>0.826575</td>\n",
+       "      <td>0.832936</td>\n",
+       "      <td>0.824195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>KNN (k=5)</td>\n",
+       "      <td>0.830549</td>\n",
+       "      <td>0.169451</td>\n",
+       "      <td>0.823808</td>\n",
+       "      <td>0.830549</td>\n",
+       "      <td>0.823450</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression</td>\n",
+       "      <td>0.811456</td>\n",
+       "      <td>0.188544</td>\n",
+       "      <td>0.834799</td>\n",
+       "      <td>0.811456</td>\n",
+       "      <td>0.817984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Naive Bayes</td>\n",
+       "      <td>0.770883</td>\n",
+       "      <td>0.229117</td>\n",
+       "      <td>0.763969</td>\n",
+       "      <td>0.770883</td>\n",
+       "      <td>0.766729</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Model  Accuracy  Error Rate  Precision    Recall  F1-Score\n",
+       "4            KNN (k=7)  0.837709    0.162291   0.832673  0.837709  0.827772\n",
+       "2                  LDA  0.830549    0.169451   0.824148  0.830549  0.824773\n",
+       "5           KNN (k=10)  0.832936    0.167064   0.826575  0.832936  0.824195\n",
+       "3            KNN (k=5)  0.830549    0.169451   0.823808  0.830549  0.823450\n",
+       "0  Logistic Regression  0.811456    0.188544   0.834799  0.811456  0.817984\n",
+       "1          Naive Bayes  0.770883    0.229117   0.763969  0.770883  0.766729"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "models_region = {\n",
     "    'Logistic Regression': LogisticRegression(max_iter=1000, random_state=42, class_weight='balanced'),\n",
@@ -827,10 +1244,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "c291d288-2668-4c28-a731-0ccc845dc4d1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Baseline Champion (Body Regions): KNN (k=7)\n",
+      "F1-Score: 0.8278\n"
+     ]
+    }
+   ],
    "source": [
     "champion_region_baseline = results_region_baseline.iloc[0]['Model']\n",
     "champion_region_f1_baseline = results_region_baseline.iloc[0]['F1-Score']\n",
@@ -848,10 +1275,122 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "aafe02c6-101b-4494-8679-70c6f8c67563",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Tuning Logistic Regression...\n",
+      "  Parameters to test: {'C': [0.01, 0.1, 1, 10, 100], 'solver': ['lbfgs', 'saga', 'newton-cg']}\n",
+      "Best params: {'C': 1, 'solver': 'lbfgs'}\n",
+      "CV F1-Score: 0.8266\n",
+      "Test F1-Score: 0.8180\n",
+      "Improvement: +0.0000\n",
+      "\n",
+      "Tuning LDA...\n",
+      "  Parameters to test: {'solver': ['svd', 'lsqr', 'eigen']}\n",
+      "Best params: {'solver': 'svd'}\n",
+      "CV F1-Score: 0.8395\n",
+      "Test F1-Score: 0.8248\n",
+      "Improvement: +0.0000\n",
+      "\n",
+      "Tuning KNN (k=10)...\n",
+      "  Parameters to test: {'n_neighbors': [7, 9, 10, 11, 13, 15], 'weights': ['uniform', 'distance'], 'metric': ['euclidean', 'manhattan', 'minkowski']}\n",
+      "Best params: {'metric': 'manhattan', 'n_neighbors': 7, 'weights': 'distance'}\n",
+      "CV F1-Score: 0.8672\n",
+      "Test F1-Score: 0.8263\n",
+      "Improvement: +0.0022\n",
+      "Hyperparameter Tuning Results (Body Regions):\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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>Model</th>\n",
+       "      <th>Best Params</th>\n",
+       "      <th>CV Score</th>\n",
+       "      <th>Test Accuracy</th>\n",
+       "      <th>Test F1-Score</th>\n",
+       "      <th>Improvement</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>KNN (k=10) (Tuned)</td>\n",
+       "      <td>{'metric': 'manhattan', 'n_neighbors': 7, 'wei...</td>\n",
+       "      <td>0.867194</td>\n",
+       "      <td>0.835322</td>\n",
+       "      <td>0.826347</td>\n",
+       "      <td>0.002153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>LDA (Tuned)</td>\n",
+       "      <td>{'solver': 'svd'}</td>\n",
+       "      <td>0.839462</td>\n",
+       "      <td>0.830549</td>\n",
+       "      <td>0.824773</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression (Tuned)</td>\n",
+       "      <td>{'C': 1, 'solver': 'lbfgs'}</td>\n",
+       "      <td>0.826577</td>\n",
+       "      <td>0.811456</td>\n",
+       "      <td>0.817984</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Model  \\\n",
+       "2           KNN (k=10) (Tuned)   \n",
+       "1                  LDA (Tuned)   \n",
+       "0  Logistic Regression (Tuned)   \n",
+       "\n",
+       "                                         Best Params  CV Score  Test Accuracy  \\\n",
+       "2  {'metric': 'manhattan', 'n_neighbors': 7, 'wei...  0.867194       0.835322   \n",
+       "1                                  {'solver': 'svd'}  0.839462       0.830549   \n",
+       "0                        {'C': 1, 'solver': 'lbfgs'}  0.826577       0.811456   \n",
+       "\n",
+       "   Test F1-Score  Improvement  \n",
+       "2       0.826347     0.002153  \n",
+       "1       0.824773     0.000000  \n",
+       "0       0.817984     0.000000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "tuning_configs_region = {\n",
     "    'Logistic Regression': {\n",
@@ -934,10 +1473,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "4080074b-fd20-4ed2-8130-84ee3c45c888",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original features: 41\n",
+      "After polynomial transformation: 861\n",
+      "New features created: 820\n",
+      "\n",
+      "Training Logistic Regression with polynomial features...\n",
+      "\n",
+      "Polynomial Features Results:\n",
+      "Accuracy: 0.8473\n",
+      "F1-Score: 0.8481\n",
+      "Baseline F1: 0.8278\n",
+      "Improvement: +0.0203\n"
+     ]
+    }
+   ],
    "source": [
     "# Create polynomial features\n",
     "poly = PolynomialFeatures(degree=2, include_bias=False, interaction_only=True)\n",
@@ -1110,7 +1667,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,