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{
"name": "20_Car_Price_Prediction_RandomForest_CarPrices_ML",
"query": "Can you help me create a car price prediction project using a Random Forest model with the Kaggle Car Prices dataset? Load the dataset and perform feature selection to identify important features in `src/data_loader.py`. Use cross-validation to evaluate the model in `src/train.py`. Save the R-squared score, Mean Squared Error (MSE), and Mean Absolute Error (MAE) to `results/metrics/results/metrics.txt`. Visualize the feature importance and save it to `results/figures/feature_importance.png`. Generate a Markdown report with insights into how the selected features contribute to the car price predictions. Saving the report as `results/report.md`.",
"tags": [
"Financial Analysis",
"Regression",
"Supervised Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"Kaggle Car Prices\" dataset is loaded in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Feature selection is implemented to identify important features in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [],
"criteria": "The \"Random Forest\" regression model is used in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [
0,
1,
2
],
"criteria": "Cross-validation is used to evaluate the model in `src/train.py`.",
"category": "Performance Metrics",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
1,
2,
3
],
"criteria": "The \"R-squared\" score, \"Mean Squared Error (MSE),\" and \"Mean Absolute Error (MAE)\" are saved in `results/metrics/results/metrics.txt`.",
"category": "Performance Metrics",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
1,
2,
3
],
"criteria": "Feature importances are visualized and saved as `results/figures/feature_importance.png`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
1,
2,
3,
4,
5
],
"criteria": "A Markdown file containing results and visualizations is generated and saved as `results/report.md`.",
"category": "Visualization",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The feature selection process should be thorough, ensuring that only the most relevant features are used in the model.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The Markdown report should provide clear insights into how the selected features contribute to the car price predictions.",
"satisfied": null
}
],
"is_kaggle_api_needed": true,
"is_training_needed": true,
"is_web_navigation_needed": false
} |