Ensemble Method with Validation code

Stock_Price_Prediction.ipynb

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        "</table>\n",
        "</div>"
       ],
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        "        Open       High        Low      Close      Volume\n",
        "0  18.691147  18.978922  18.540184  18.823240  43733533.0\n",
        "1  18.894005  18.964767  17.738192  18.224106  56167280.0\n",
        "2  18.327892  18.568489  17.643839  17.738192  68296318.0\n",
        "3  17.502312  17.832542  17.223972  17.676863  86073880.0\n",
        "4  17.738192  17.785366  17.459852  17.577793  76613039.0"
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       ]
      },
      "execution_count": 30,
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    },
    "outputs": [
     {
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        "(1415,)"
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      "execution_count": 46,
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    "outputs": [
     {
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      "output_type": "stream",
      "text": [
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       "MAPE: 0.0126215060590655\n",
       "\n"
      ]
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     }
    ],
    "source": [
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     "metrics_catboost = train_and_evaluate_model(model_catboost, X_train, X_test, y_train, y_test)\n",
     "print(\"CatBoost Metrics:\", metrics_catboost)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ensemble method using random forest and AdaBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "# Assuming you have your features (X) and target variable (y)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Create individual models\n",
+    "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
+    "adaboost_model = AdaBoostRegressor(n_estimators=100, random_state=42)\n",
+    "\n",
+    "# Train the models\n",
+    "rf_model.fit(X_train, y_train)\n",
+    "adaboost_model.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions\n",
+    "rf_predictions = rf_model.predict(X_test)\n",
+    "adaboost_predictions = adaboost_model.predict(X_test)\n",
+    "\n",
+    "# Combine predictions (simple averaging)\n",
+    "ensemble_predictions = (rf_predictions + adaboost_predictions) / 2\n",
+    "\n",
+    "# Evaluate the ensemble model\n",
+    "ensemble_mse = mean_squared_error(y_test, ensemble_predictions)\n",
+    "print(\"Ensemble MSE:\", ensemble_mse)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, mean_squared_error, mean_absolute_error\n",
+    "\n",
+    "# Assuming we have the true labels (y_test) and the ensemble predictions (ensemble_predictions)\n",
+    "\n",
+    "# Calculate accuracy\n",
+    "accuracy = accuracy_score(y_test, ensemble_predictions.round())\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "\n",
+    "# Calculate RMSE\n",
+    "rmse = mean_squared_error(y_test, ensemble_predictions, squared=False)\n",
+    "print(\"RMSE:\", rmse)\n",
+    "\n",
+    "# Calculate MAE\n",
+    "mae = mean_absolute_error(y_test, ensemble_predictions)\n",
+    "print(\"MAE:\", mae)\n",
+    "\n",
+    "# Other relevant metrics\n",
+    "# For example, if your target variable is categorical:\n",
+    "# precision = precision_score(y_test, ensemble_predictions.round())\n",
+    "# recall = recall_score(y_test, ensemble_predictions.round())\n",
+    "# f1_score = f1_score(y_test, ensemble_predictions.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {