removed estimated feature

A3/A3_Test.ipynb

@@ -307,10 +307,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Features (X) shape: (2094, 42)\n",
+      "Features (X) shape: (2094, 41)\n",
       "Target (y) shape: (2094,)\n",
       "\n",
-      "Number of features: 42\n",
+      "Number of features: 41\n",
       "Feature names: ['AimoScore', 'No_1_Angle_Deviation', 'No_2_Angle_Deviation', 'No_3_Angle_Deviation', 'No_4_Angle_Deviation', 'No_5_Angle_Deviation', 'No_6_Angle_Deviation', 'No_7_Angle_Deviation', 'No_8_Angle_Deviation', 'No_9_Angle_Deviation']...\n",
       "\n",
       "Number of classes: 14\n",
@@ -320,7 +320,7 @@
    ],
    "source": [
     "# Drop non-feature columns \n",
-    "exclude_from_X = ['ID', 'WeakestLink']\n",
+    "exclude_from_X = ['ID', 'WeakestLink', 'EstimatedScore']\n",
     "feature_columns = [col for col in merged_df.columns if col not in exclude_from_X]\n",
     "\n",
     "X = merged_df[feature_columns].copy()\n",
@@ -513,56 +513,56 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>LDA</td>\n",
-       "      <td>0.579952</td>\n",
-       "      <td>0.420048</td>\n",
-       "      <td>0.594921</td>\n",
-       "      <td>0.579952</td>\n",
-       "      <td>0.580049</td>\n",
+       "      <td>0.565632</td>\n",
+       "      <td>0.434368</td>\n",
+       "      <td>0.578220</td>\n",
+       "      <td>0.565632</td>\n",
+       "      <td>0.566721</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Logistic Regression</td>\n",
-       "      <td>0.548926</td>\n",
-       "      <td>0.451074</td>\n",
-       "      <td>0.623611</td>\n",
-       "      <td>0.548926</td>\n",
-       "      <td>0.566248</td>\n",
+       "      <td>0.546539</td>\n",
+       "      <td>0.453461</td>\n",
+       "      <td>0.614849</td>\n",
+       "      <td>0.546539</td>\n",
+       "      <td>0.562310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>KNN (k=5)</td>\n",
+       "      <th>4</th>\n",
+       "      <td>KNN (k=7)</td>\n",
        "      <td>0.553699</td>\n",
        "      <td>0.446301</td>\n",
-       "      <td>0.556792</td>\n",
+       "      <td>0.548231</td>\n",
        "      <td>0.553699</td>\n",
-       "      <td>0.546164</td>\n",
+       "      <td>0.545808</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>KNN (k=10)</td>\n",
-       "      <td>0.551313</td>\n",
-       "      <td>0.448687</td>\n",
-       "      <td>0.573710</td>\n",
-       "      <td>0.551313</td>\n",
-       "      <td>0.542103</td>\n",
+       "      <td>0.556086</td>\n",
+       "      <td>0.443914</td>\n",
+       "      <td>0.551704</td>\n",
+       "      <td>0.556086</td>\n",
+       "      <td>0.540911</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>KNN (k=7)</td>\n",
-       "      <td>0.544153</td>\n",
-       "      <td>0.455847</td>\n",
-       "      <td>0.542687</td>\n",
-       "      <td>0.544153</td>\n",
-       "      <td>0.538519</td>\n",
+       "      <th>3</th>\n",
+       "      <td>KNN (k=5)</td>\n",
+       "      <td>0.539379</td>\n",
+       "      <td>0.460621</td>\n",
+       "      <td>0.532738</td>\n",
+       "      <td>0.539379</td>\n",
+       "      <td>0.530905</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Naive Bayes</td>\n",
-       "      <td>0.431981</td>\n",
-       "      <td>0.568019</td>\n",
-       "      <td>0.513408</td>\n",
-       "      <td>0.431981</td>\n",
-       "      <td>0.447074</td>\n",
+       "      <td>0.434368</td>\n",
+       "      <td>0.565632</td>\n",
+       "      <td>0.511623</td>\n",
+       "      <td>0.434368</td>\n",
+       "      <td>0.449053</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -570,12 +570,12 @@
       ],
       "text/plain": [
        "                 Model  Accuracy  Error Rate  Precision    Recall  F1-Score\n",
-       "2                  LDA  0.579952    0.420048   0.594921  0.579952  0.580049\n",
-       "0  Logistic Regression  0.548926    0.451074   0.623611  0.548926  0.566248\n",
-       "3            KNN (k=5)  0.553699    0.446301   0.556792  0.553699  0.546164\n",
-       "5           KNN (k=10)  0.551313    0.448687   0.573710  0.551313  0.542103\n",
-       "4            KNN (k=7)  0.544153    0.455847   0.542687  0.544153  0.538519\n",
-       "1          Naive Bayes  0.431981    0.568019   0.513408  0.431981  0.447074"
+       "2                  LDA  0.565632    0.434368   0.578220  0.565632  0.566721\n",
+       "0  Logistic Regression  0.546539    0.453461   0.614849  0.546539  0.562310\n",
+       "4            KNN (k=7)  0.553699    0.446301   0.548231  0.553699  0.545808\n",
+       "5           KNN (k=10)  0.556086    0.443914   0.551704  0.556086  0.540911\n",
+       "3            KNN (k=5)  0.539379    0.460621   0.532738  0.539379  0.530905\n",
+       "1          Naive Bayes  0.434368    0.565632   0.511623  0.434368  0.449053"
       ]
      },
      "metadata": {},
@@ -603,7 +603,7 @@
      "text": [
       "\n",
       "CHAMPION: LDA\n",
-      "F1-Score: 0.5800\n"
+      "F1-Score: 0.5667\n"
      ]
     }
    ],
@@ -626,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "6cecb844-b3b1-448f-b0fa-e917e4f979d7",
    "metadata": {},
    "outputs": [
@@ -636,112 +636,8 @@
      "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",
-      "Best params: {'C': 10, 'class_weight': None, 'solver': 'lbfgs'}\n",
-      "CV F1-Score: 0.5968\n",
-      "Test F1-Score: 0.6195\n",
-      "Improvement: +0.0532\n",
-      "\n",
-      "Tuning LDA...\n",
-      "  Parameters to test: {'solver': ['svd', 'lsqr', 'eigen']}\n",
-      "Best params: {'solver': 'svd'}\n",
-      "CV F1-Score: 0.5774\n",
-      "Test F1-Score: 0.5800\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: 0.5560\n",
-      "Test F1-Score: 0.5571\n",
-      "Improvement: +0.0110\n",
-      "\n",
-      "\n",
-      "Hyperparameter Tuning Results (14-class):\n",
-      "\n",
-      "\n"
+      "  Parameters to test: {'C': [0.001, 0.01, 0.1, 1, 10, 100], 'solver': ['lbfgs', 'saga'], 'class_weight': [None, 'balanced']}\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': 10, 'class_weight': None, 'solver': 'lbf...</td>\n",
-       "      <td>0.596789</td>\n",
-       "      <td>0.620525</td>\n",
-       "      <td>0.619470</td>\n",
-       "      <td>0.053222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>LDA (Tuned)</td>\n",
-       "      <td>{'solver': 'svd'}</td>\n",
-       "      <td>0.577370</td>\n",
-       "      <td>0.579952</td>\n",
-       "      <td>0.580049</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>0.555957</td>\n",
-       "      <td>0.568019</td>\n",
-       "      <td>0.557133</td>\n",
-       "      <td>0.010970</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': 10, 'class_weight': None, 'solver': 'lbf...  0.596789       0.620525   \n",
-       "1                                  {'solver': 'svd'}  0.577370       0.579952   \n",
-       "2  {'metric': 'manhattan', 'n_neighbors': 5, 'wei...  0.555957       0.568019   \n",
-       "\n",
-       "   Test F1-Score  Improvement  \n",
-       "0       0.619470     0.053222  \n",
-       "1       0.580049     0.000000  \n",
-       "2       0.557133     0.010970  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -833,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "9a43d212-4268-4921-8926-0ee1a017d771",
    "metadata": {},
    "outputs": [],
@@ -844,193 +740,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "2256f87c-6d23-4a03-a614-53cfe31916bc",
    "metadata": {},
-   "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"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Define the groups as mentioned in Slide 6 into 3 distinct regions\n",
     "def get_region(label):\n",
@@ -1057,24 +770,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "c556f1ac-552b-4ccb-82a1-6c3b782c3c75",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Training set: 1675 samples\n",
-      "Test set: 419 samples\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Prepare data for region wise classification\n",
     "\n",
-    "exclude_region = ['ID', 'WeakestLink', 'WeakestRegion']\n",
+    "exclude_region = ['ID', 'WeakestLink', 'WeakestRegion', 'EstimatedScore']\n",
     "features_region = [c for c in merged_df.columns if c not in exclude_region]\n",
     "\n",
     "X_region = merged_df[features_region].copy()\n",
@@ -1095,130 +798,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "cc39415b-8dab-4af3-bd1c-830c01735921",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\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",
-       "    }\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>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>2</th>\n",
-       "      <td>LDA</td>\n",
-       "      <td>0.842482</td>\n",
-       "      <td>0.157518</td>\n",
-       "      <td>0.837328</td>\n",
-       "      <td>0.842482</td>\n",
-       "      <td>0.837989</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</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>5</th>\n",
-       "      <td>KNN (k=7)</td>\n",
-       "      <td>0.832936</td>\n",
-       "      <td>0.167064</td>\n",
-       "      <td>0.827125</td>\n",
-       "      <td>0.832936</td>\n",
-       "      <td>0.822706</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>KNN (k=5)</td>\n",
-       "      <td>0.825776</td>\n",
-       "      <td>0.174224</td>\n",
-       "      <td>0.818545</td>\n",
-       "      <td>0.825776</td>\n",
-       "      <td>0.818477</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Logistic Regression</td>\n",
-       "      <td>0.809069</td>\n",
-       "      <td>0.190931</td>\n",
-       "      <td>0.833434</td>\n",
-       "      <td>0.809069</td>\n",
-       "      <td>0.815823</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",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>QDA</td>\n",
-       "      <td>0.548926</td>\n",
-       "      <td>0.451074</td>\n",
-       "      <td>0.688926</td>\n",
-       "      <td>0.548926</td>\n",
-       "      <td>0.570997</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 Model  Accuracy  Error Rate  Precision    Recall  F1-Score\n",
-       "2                  LDA  0.842482    0.157518   0.837328  0.842482  0.837989\n",
-       "6           KNN (k=10)  0.832936    0.167064   0.826575  0.832936  0.824195\n",
-       "5            KNN (k=7)  0.832936    0.167064   0.827125  0.832936  0.822706\n",
-       "4            KNN (k=5)  0.825776    0.174224   0.818545  0.825776  0.818477\n",
-       "0  Logistic Regression  0.809069    0.190931   0.833434  0.809069  0.815823\n",
-       "1          Naive Bayes  0.770883    0.229117   0.763969  0.770883  0.766729\n",
-       "3                  QDA  0.548926    0.451074   0.688926  0.548926  0.570997"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "models_region = {\n",
     "    'Logistic Regression': LogisticRegression(max_iter=1000, random_state=42, class_weight='balanced'),\n",
@@ -1244,20 +827,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "c291d288-2668-4c28-a731-0ccc845dc4d1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Baseline Champion (Body Regions): LDA\n",
-      "F1-Score: 0.8380\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "champion_region_baseline = results_region_baseline.iloc[0]['Model']\n",
     "champion_region_f1_baseline = results_region_baseline.iloc[0]['F1-Score']\n",
@@ -1275,122 +848,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "aafe02c6-101b-4494-8679-70c6f8c67563",
    "metadata": {},
-   "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': 0.1, 'solver': 'lbfgs'}\n",
-      "CV F1-Score: 0.8290\n",
-      "Test F1-Score: 0.8137\n",
-      "Improvement: -0.0022\n",
-      "\n",
-      "Tuning LDA...\n",
-      "  Parameters to test: {'solver': ['svd', 'lsqr', 'eigen']}\n",
-      "Best params: {'solver': 'svd'}\n",
-      "CV F1-Score: 0.8434\n",
-      "Test F1-Score: 0.8380\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': 'euclidean', 'n_neighbors': 10, 'weights': 'distance'}\n",
-      "CV F1-Score: 0.8643\n",
-      "Test F1-Score: 0.8335\n",
-      "Improvement: +0.0093\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>1</th>\n",
-       "      <td>LDA (Tuned)</td>\n",
-       "      <td>{'solver': 'svd'}</td>\n",
-       "      <td>0.843434</td>\n",
-       "      <td>0.842482</td>\n",
-       "      <td>0.837989</td>\n",
-       "      <td>0.00000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>KNN (k=10) (Tuned)</td>\n",
-       "      <td>{'metric': 'euclidean', 'n_neighbors': 10, 'we...</td>\n",
-       "      <td>0.864273</td>\n",
-       "      <td>0.844869</td>\n",
-       "      <td>0.833524</td>\n",
-       "      <td>0.00933</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Logistic Regression (Tuned)</td>\n",
-       "      <td>{'C': 0.1, 'solver': 'lbfgs'}</td>\n",
-       "      <td>0.829037</td>\n",
-       "      <td>0.806683</td>\n",
-       "      <td>0.813662</td>\n",
-       "      <td>-0.00216</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         Model  \\\n",
-       "1                  LDA (Tuned)   \n",
-       "2           KNN (k=10) (Tuned)   \n",
-       "0  Logistic Regression (Tuned)   \n",
-       "\n",
-       "                                         Best Params  CV Score  Test Accuracy  \\\n",
-       "1                                  {'solver': 'svd'}  0.843434       0.842482   \n",
-       "2  {'metric': 'euclidean', 'n_neighbors': 10, 'we...  0.864273       0.844869   \n",
-       "0                      {'C': 0.1, 'solver': 'lbfgs'}  0.829037       0.806683   \n",
-       "\n",
-       "   Test F1-Score  Improvement  \n",
-       "1       0.837989      0.00000  \n",
-       "2       0.833524      0.00933  \n",
-       "0       0.813662     -0.00216  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tuning_configs_region = {\n",
     "    'Logistic Regression': {\n",
@@ -1473,28 +934,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "4080074b-fd20-4ed2-8130-84ee3c45c888",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original features: 42\n",
-      "After polynomial transformation: 903\n",
-      "New features created: 861\n",
-      "\n",
-      "Training Logistic Regression with polynomial features...\n",
-      "\n",
-      "Polynomial Features Results:\n",
-      "Accuracy: 0.8496\n",
-      "F1-Score: 0.8498\n",
-      "Baseline F1: 0.8380\n",
-      "Improvement: +0.0119\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create polynomial features\n",
     "poly = PolynomialFeatures(degree=2, include_bias=False, interaction_only=True)\n",
@@ -1528,110 +971,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "6aaac401-c2d4-43b9-aba0-45a3315fd932",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "ALL ITERATIONS RANKED BY 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",
-       "    }\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>Approach</th>\n",
-       "      <th>Iteration</th>\n",
-       "      <th>Model</th>\n",
-       "      <th>F1-Score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Body Regions</td>\n",
-       "      <td>Polynomial</td>\n",
-       "      <td>LR + Poly Features</td>\n",
-       "      <td>0.849847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Body Regions</td>\n",
-       "      <td>Tuned</td>\n",
-       "      <td>LDA (Tuned)</td>\n",
-       "      <td>0.837989</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Body Regions</td>\n",
-       "      <td>Baseline</td>\n",
-       "      <td>LDA</td>\n",
-       "      <td>0.837989</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>14-Class</td>\n",
-       "      <td>Tuned</td>\n",
-       "      <td>Logistic Regression (Tuned)</td>\n",
-       "      <td>0.619470</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>14-Class</td>\n",
-       "      <td>Baseline</td>\n",
-       "      <td>LDA</td>\n",
-       "      <td>0.580049</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       Approach   Iteration                        Model  F1-Score\n",
-       "4  Body Regions  Polynomial           LR + Poly Features  0.849847\n",
-       "3  Body Regions       Tuned                  LDA (Tuned)  0.837989\n",
-       "2  Body Regions    Baseline                          LDA  0.837989\n",
-       "1      14-Class       Tuned  Logistic Regression (Tuned)  0.619470\n",
-       "0      14-Class    Baseline                          LDA  0.580049"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "FINAL CHAMPION MODEL\n",
-      "Approach: Body Regions\n",
-      "Iteration: Polynomial\n",
-      "Model: LR + Poly Features\n",
-      "F1-Score: 0.8498\n",
-      "Improvement over baseline: +0.0119\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "all_iterations = pd.DataFrame([\n",
     "    # 14-class approaches\n",
@@ -1671,39 +1014,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "bb170b58-c408-4db5-a262-0fc00b93ba07",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Classification Report:\n",
-      "----------------------------------------------------------------------------------------------------\n",
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "  Lower Body       0.72      0.73      0.72       114\n",
-      "  Upper Body       0.90      0.90      0.90       305\n",
-      "\n",
-      "    accuracy                           0.85       419\n",
-      "   macro avg       0.81      0.81      0.81       419\n",
-      "weighted avg       0.85      0.85      0.85       419\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Determine which champion to use\n",
     "if final_champion_row['Approach'] == 'Body Regions':\n",
@@ -1751,18 +1065,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "b894966a-0c41-4d24-83a9-3d9eef777493",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model saved: final_champion_model_A3.pkl\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Save the champion model\n",
     "model_filename = 'final_champion_model_A3.pkl'\n",