{
    "name": "11_House_Price_Prediction_LinearRegression_BostonHousing_ML",
    "query": "Hi. Set up a house price prediction project using a Linear Regression model on the Boston Housing dataset. Load the dataset using `from datasets import load_dataset` and `ds = load_dataset(\"~/mrseba/boston_house_price\")` in `src/data_loader.py`. Ensure feature scaling and data standardization are performed in `src/data_loader.py`. Implement the Linear Regression model in `src/model.py`. Use cross-validation to evaluate the model in `src/train.py`. Print the Mean Squared Error (MSE), Mean Absolute Error (MAE), and $R^2$ score, and save them under `results/metrics/metrics.txt`. Visualize the comparison between predicted and actual values and save the result as `results/figures/`prediction_vs_actual.png`. The visualizations should clearly demonstrate the model's accuracy (which, if done right, should be good).",
    "tags": [
        "Financial Analysis",
        "Regression",
        "Supervised Learning"
    ],
    "requirements": [
        {
            "requirement_id": 0,
            "prerequisites": [],
            "criteria": "The \"Boston Housing\" dataset is utilized using `from datasets import load_dataset` and `ds = load_dataset(\"mrseba/boston_house_price\")` in `src/data_loader.py`.",
            "category": "Dataset or Environment",
            "satisfied": null
        },
        {
            "requirement_id": 1,
            "prerequisites": [
                0
            ],
            "criteria": "Feature scaling and data standardization are performed in `src/data_loader.py`.",
            "category": "Data preprocessing and postprocessing",
            "satisfied": null
        },
        {
            "requirement_id": 2,
            "prerequisites": [],
            "criteria": "The \"Linear Regression\" model is implemented 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": [
                0,
                1,
                2,
                3
            ],
            "criteria": "\"Mean Squared Error (MSE),\" \"Mean Absolute Error (MAE),\" and \"R^2 score\" are printed, and saved as `results/metrics/metrics.txt`.",
            "category": "Performance Metrics",
            "satisfied": null
        },
        {
            "requirement_id": 5,
            "prerequisites": [
                0,
                1,
                2,
                3
            ],
            "criteria": "The comparison of predicted vs. actual values is visualized and saved as `results/figures/prediction_vs_actual.png`.",
            "category": "Visualization",
            "satisfied": null
        }
    ],
    "preferences": [
        {
            "preference_id": 0,
            "criteria": "The dataset should load smoothly using the provided `load_dataset` code, and other methods should be tried if issues arise.",
            "satisfied": null
        },
        {
            "preference_id": 1,
            "criteria": "The visualizations should clearly demonstrate the model's accuracy by highlighting the differences between predicted and actual values.",
            "satisfied": null
        }
    ],
    "is_kaggle_api_needed": false,
    "is_training_needed": true,
    "is_web_navigation_needed": false,
    "hint": "`ds = load_dataset(\"~/mrseba/boston_house_price\")` in the query is wrong, and it should be `ds = load_dataset(\"mrseba/boston_house_price\")`. We leave it here to check the self-debugging skill of the agents."
}