instances/20_Car_Price_Prediction_RandomForest_CarPrices_ML.json

{
    "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
}