instances/23_Wine_Quality_Prediction_DecisionTree_WineQuality_ML.json

{
    "name": "23_Wine_Quality_Prediction_DecisionTree_WineQuality_ML",
    "query": "Build a wine quality prediction system using a Decision Tree model with the Wine Quality dataset from UCI. Preprocess the data in `src/data_loader.py`, including handling missing values and feature scaling. Use cross-validation to evaluate the model in `src/train.py`. Implement the Decision Tree regression model in `src/model.py`.Save the mean squared error in `results/metrics/mean_squared_error.txt`. Visualize and save feature importance as `results/figures/feature_importance.png`. Create a Jupyter Notebook with results and visualizations, and summarize your observations. The Notebook should thoroughly document the preprocessing steps to ensure reproducibility. Convert the Notebook to a PDF report and save it as `results/report.pdf`. The PDF report should also include a brief discussion on potential improvements of the model.",
    "tags": [
        "Classification",
        "Supervised Learning"
    ],
    "requirements": [
        {
            "requirement_id": 0,
            "prerequisites": [],
            "criteria": "The \"Wine Quality\" dataset from \"UCI\" is used.",
            "category": "Dataset or Environment",
            "satisfied": null
        },
        {
            "requirement_id": 1,
            "prerequisites": [
                0
            ],
            "criteria": "Data preprocessing is performed in `src/data_loader.py`, including handling missing values and feature scaling.",
            "category": "Data preprocessing and postprocessing",
            "satisfied": null
        },
        {
            "requirement_id": 2,
            "prerequisites": [],
            "criteria": "The \"Decision Tree\" 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": "The Mean Squared Error (MSE) is saved in `results/metrics/mean_squared_error.txt`.",
            "category": "Performance Metrics",
            "satisfied": null
        },
        {
            "requirement_id": 5,
            "prerequisites": [
                0,
                1,
                2,
                3
            ],
            "criteria": "The feature importance plot is generated and saved as `results/figures/feature_importance.png`.",
            "category": "Visualization",
            "satisfied": null
        },
        {
            "requirement_id": 6,
            "prerequisites": [
                0,
                1,
                2,
                3,
                4,
                5
            ],
            "criteria": "A Jupyter Notebook containing  preprocessing steps, results and visualizations is generated with observations summarized. The Notebook is converted to a PDF report and saved as `results/report.pdf`.",
            "category": "Visualization",
            "satisfied": null
        }
    ],
    "preferences": [
        {
            "preference_id": 0,
            "criteria": "The feature importance plot should clearly highlight the top influential features.",
            "satisfied": null
        },
        {
            "preference_id": 1,
            "criteria": "The final PDF report should include a brief discussion on potential improvements of the model.",
            "satisfied": null
        }
    ],
    "is_kaggle_api_needed": false,
    "is_training_needed": true,
    "is_web_navigation_needed": false
}