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Ensemble Method (using random forest and AdaBoost) with Validation code #102

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89 changes: 83 additions & 6 deletions Stock_Price_Prediction.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -276,15 +276,13 @@
"</table>\n",
"</div>"
],

"text/plain": [
" 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"

]
},
"execution_count": 30,
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},
"outputs": [
{

"data": {
"text/plain": [
"(1415,)"

]
},
"execution_count": 46,
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},
"outputs": [
{

"name": "stdout",
"output_type": "stream",
"text": [
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"MAPE: 0.0126215060590655\n",
"\n"
]

}
],
"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": {
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