{
    "name": "41_Stock_Classification_KNN_YahooFinance_ML",
    "query": "Develop a stock classification system using a KNN model on the Yahoo Finance dataset. Your implementation should decide if a given stock will increase or decrease in price. Start by loading the dataset and performing feature engineering, including generating technical indicators and selecting the most relevant features in `src/data_loader.py`. Standardize the data to ensure feature values are within the same range in `src/data_loader.py`. Apply the KNN classifier to classify stocks based on the engineered features, and save the implementation in `src/model.py`. Next, save the classification results to `results/classification_results.txt`, and visualize the correlation between the technical indicators and the classification result as a heatmap using seaborn. Save the headmap as `results/figures/feature_correlation_heatmap.png`. Finally, create an interactive Jupyter Notebook under `results/` that explains the process, showcases the classification results, and will help ease future updates that introduce new data.",
    "tags": [
        "Classification",
        "Financial Analysis",
        "Supervised Learning"
    ],
    "requirements": [
        {
            "requirement_id": 0,
            "prerequisites": [],
            "criteria": "The \"Yahoo Finance\" dataset is used, including data loading and preparation in `src/data_loader.py`.",
            "category": "Dataset or Environment",
            "satisfied": null
        },
        {
            "requirement_id": 1,
            "prerequisites": [
                0
            ],
            "criteria": "Feature engineering is performed, including generating technical indicators and conducting feature selection in `src/data_loader.py`.",
            "category": "Data preprocessing and postprocessing",
            "satisfied": null
        },
        {
            "requirement_id": 2,
            "prerequisites": [
                0
            ],
            "criteria": "Data is standardized to ensure feature values are within the same range in `src/data_loader.py`.",
            "category": "Data preprocessing and postprocessing",
            "satisfied": null
        },
        {
            "requirement_id": 3,
            "prerequisites": [
                2
            ],
            "criteria": "The \"KNN classifier\" is applied to classify stocks based on the engineered features. Please save the implementation in `src/model.py`.",
            "category": "Machine Learning Method",
            "satisfied": null
        },
        {
            "requirement_id": 4,
            "prerequisites": [
                3
            ],
            "criteria": "The classification results are saved in `results/classification_results.txt`.",
            "category": "Other",
            "satisfied": null
        },
        {
            "requirement_id": 5,
            "prerequisites": [
                4
            ],
            "criteria": "A heatmap representing the correlations between the technical indicators and the classification results is saved as `results/figures/feature_correlation_heatmap.png`.",
            "category": "Visualization",
            "satisfied": null
        },
        {
            "requirement_id": 6,
            "prerequisites": [
                4
            ],
            "criteria": "An interactive \"Jupyter Notebook\" is created under `results/` to explain the process and showcase the classification results.",
            "category": "Human Computer Interaction",
            "satisfied": null
        }
    ],
    "preferences": [
        {
            "preference_id": 0,
            "criteria": "The Jupyter Notebook should include clear explanations of each step, including feature engineering and model evaluation.",
            "satisfied": null
        },
        {
            "preference_id": 1,
            "criteria": "The correlation heatmap should highlight the most significant technical indicators and provide insights into their relationships.",
            "satisfied": null
        },
        {
            "preference_id": 2,
            "criteria": "The system should allow easy updates with new data, making the notebook flexible for future analysis.",
            "satisfied": null
        }
    ],
    "is_kaggle_api_needed": false,
    "is_training_needed": true,
    "is_web_navigation_needed": false
}