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{
"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
} |