Added all the remaining lectures

13-Gradient Boosting/Projects/Regression/Gradientboost Regression Implementation.ipynb

@@ -652,18 +652,19 @@
     {
      "data": {
       "text/plain": [
+       "model\n",
        "i20            906\n",
        "Swift Dzire    890\n",
        "Swift          781\n",
        "Alto           778\n",
        "City           757\n",
        "              ... \n",
-       "Ghost            1\n",
+       "Ghibli           1\n",
+       "Altroz           1\n",
        "GTC4Lusso        1\n",
-       "Gurkha           1\n",
        "Aura             1\n",
-       "Ghibli           1\n",
-       "Name: model, Length: 120, dtype: int64"
+       "Gurkha           1\n",
+       "Name: count, Length: 120, dtype: int64"
       ]
      },
      "execution_count": 12,
@@ -1296,14 +1297,14 @@
       "\n",
       "K-Neighbors Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 325886.8736\n",
-      "- Mean Absolute Error: 91467.6671\n",
+      "- Root Mean Squared Error: 325873.0088\n",
+      "- Mean Absolute Error: 91425.4705\n",
       "- R2 Score: 0.8691\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 253118.4156\n",
-      "- Mean Absolute Error: 112704.3545\n",
-      "- R2 Score: 0.9149\n",
+      "- Root Mean Squared Error: 253024.3951\n",
+      "- Mean Absolute Error: 112526.3461\n",
+      "- R2 Score: 0.9150\n",
       "===================================\n",
       "\n",
       "\n",
@@ -1314,35 +1315,35 @@
       "- R2 Score: 0.9995\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 305299.7207\n",
-      "- Mean Absolute Error: 125466.7991\n",
-      "- R2 Score: 0.8762\n",
+      "- Root Mean Squared Error: 305590.0520\n",
+      "- Mean Absolute Error: 124898.2512\n",
+      "- R2 Score: 0.8759\n",
       "===================================\n",
       "\n",
       "\n",
       "Random Forest Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 155076.8819\n",
-      "- Mean Absolute Error: 40176.2373\n",
-      "- R2 Score: 0.9703\n",
+      "- Root Mean Squared Error: 126269.9485\n",
+      "- Mean Absolute Error: 39841.5849\n",
+      "- R2 Score: 0.9803\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 229169.8191\n",
-      "- Mean Absolute Error: 102220.2001\n",
-      "- R2 Score: 0.9302\n",
+      "- Root Mean Squared Error: 228022.4703\n",
+      "- Mean Absolute Error: 101693.9021\n",
+      "- R2 Score: 0.9309\n",
       "===================================\n",
       "\n",
       "\n",
       "Adaboost Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 418464.4830\n",
-      "- Mean Absolute Error: 306779.6276\n",
-      "- R2 Score: 0.7841\n",
+      "- Root Mean Squared Error: 448206.7247\n",
+      "- Mean Absolute Error: 344322.2238\n",
+      "- R2 Score: 0.7523\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 454609.4495\n",
-      "- Mean Absolute Error: 327688.7964\n",
-      "- R2 Score: 0.7255\n",
+      "- Root Mean Squared Error: 477538.1159\n",
+      "- Mean Absolute Error: 359776.8696\n",
+      "- R2 Score: 0.6971\n",
       "===================================\n",
       "\n",
       "\n",
@@ -1353,9 +1354,9 @@
       "- R2 Score: 0.9482\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 256880.4088\n",
-      "- Mean Absolute Error: 126637.5099\n",
-      "- R2 Score: 0.9123\n",
+      "- Root Mean Squared Error: 254160.0289\n",
+      "- Mean Absolute Error: 125953.4555\n",
+      "- R2 Score: 0.9142\n",
       "===================================\n",
       "\n",
       "\n"
@@ -1452,43 +1453,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done  98 tasks      | elapsed:   20.2s\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:   54.0s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 32 concurrent workers.\n",
-      "[Parallel(n_jobs=-1)]: Done 144 tasks      | elapsed:    0.9s\n",
-      "[Parallel(n_jobs=-1)]: Done 237 out of 300 | elapsed:   16.6s remaining:    4.3s\n",
-      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  9.9min finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
+      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
       "---------------- Best Params for RF -------------------\n",
-      "{'n_estimators': 100, 'min_samples_split': 2, 'max_features': 8, 'max_depth': 15}\n",
+      "{'n_estimators': 100, 'min_samples_split': 2, 'max_features': 5, 'max_depth': 15}\n",
       "---------------- Best Params for GradientBoost -------------------\n",
-      "{'n_estimators': 200, 'min_samples_split': 8, 'max_depth': 10, 'loss': 'huber', 'criterion': 'mse'}\n"
+      "{'n_estimators': 200, 'min_samples_split': 2, 'max_depth': 15, 'loss': 'huber', 'criterion': 'squared_error'}\n"
      ]
     }
    ],
@@ -1514,7 +1484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1523,27 +1493,27 @@
      "text": [
       "Random Forest Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 133489.8497\n",
-      "- Mean Absolute Error: 39724.3165\n",
-      "- R2 Score: 0.9780\n",
+      "- Root Mean Squared Error: 128227.7974\n",
+      "- Mean Absolute Error: 56311.0901\n",
+      "- R2 Score: 0.9797\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 222711.7974\n",
-      "- Mean Absolute Error: 101412.0000\n",
-      "- R2 Score: 0.9341\n",
+      "- Root Mean Squared Error: 205038.0659\n",
+      "- Mean Absolute Error: 97150.2658\n",
+      "- R2 Score: 0.9442\n",
       "===================================\n",
       "\n",
       "\n",
       "GradientBoost Regressor\n",
       "Model performance for Training set\n",
-      "- Root Mean Squared Error: 68673.6455\n",
-      "- Mean Absolute Error: 37702.1429\n",
-      "- R2 Score: 0.9942\n",
+      "- Root Mean Squared Error: 25277.8555\n",
+      "- Mean Absolute Error: 5915.7977\n",
+      "- R2 Score: 0.9992\n",
       "----------------------------------\n",
       "Model performance for Test set\n",
-      "- Root Mean Squared Error: 227779.4363\n",
-      "- Mean Absolute Error: 97038.0649\n",
-      "- R2 Score: 0.9311\n",
+      "- Root Mean Squared Error: 255151.1310\n",
+      "- Mean Absolute Error: 108318.0849\n",
+      "- R2 Score: 0.9135\n",
       "===================================\n",
       "\n",
       "\n"
@@ -1553,10 +1523,10 @@
    "source": [
     "## Retraining the models with best parameters\n",
     "models = {\n",
-    "    \"Random Forest Regressor\": RandomForestRegressor(n_estimators=100, min_samples_split=2, max_features='auto', max_depth=None, \n",
+    "    \"Random Forest Regressor\": RandomForestRegressor(n_estimators=100, min_samples_split=2, max_features=5, max_depth=15, \n",
     "                                                     n_jobs=-1),\n",
     "     \"GradientBoost Regressor\":GradientBoostingRegressor(n_estimators= 200,\n",
-    "                                                         min_samples_split=8, max_depth=10, loss= 'huber', criterion='mse')\n",
+    "                                                         min_samples_split=2, max_depth=15, loss= 'huber', criterion='squared_error')\n",
     "    \n",
     "}\n",
     "for i in range(len(list(models))):\n",
@@ -1599,7 +1569,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -1613,7 +1583,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,