Ensemble method with validation on Updated ipynb file

Stock_Price_Prediction(Updated).ipynb

@@ -5215,6 +5215,82 @@
         "plt.tight_layout()\n",
         "plt.show()\n"
       ]
+    },
+    {
+      "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())"
+      ]
     }
   ],
   "metadata": {