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{
"name": "17_Heart_Disease_Prediction_XGBoost_UCI_ML",
"query": "Create a project to predict heart disease using an XGBoost model with the UCI Heart Disease dataset, which can be downloaded from [this link](https://archive.ics.uci.edu/dataset/45/heart+disease). Load the dataset in `src/data_loader.py`. Implement feature selection and data standardization in `src/data_loader.py`. Use SHAP values to explain the feature importance, and save the results as `results/figures/shap_importance.png`. Implement the XGBoost model in `src/model.py`. Then, use SHAP values to explain the feature importance, and save the results as `results/shap_importance.png`. Save the ROC curve to `results/figures/roc_curve.png`. Finally, generate an HTML report containing all the results and visualizations, and save it as `results/report.html`. Ensure the SHAP visualizations clearly highlight the most impactful features. Include a performance comparison with another model, such as Logistic Regression, to validate the robustness of the XGBoost model. Save the XGBoost model under `models/saved_models/`.",
"tags": [
"Classification",
"Medical Analysis",
"Supervised Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"UCI Heart Disease\" dataset is used, potentially being downloaded from [this link](https://archive.ics.uci.edu/dataset/45/heart+disease). Load the dataset in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Feature selection is implemented in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
0
],
"criteria": "Data standardization which ensures feature values are within the same range is implemented in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [],
"criteria": "The \"XGBoost\" model is implemented in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
0,
1,
2,
3
],
"criteria": "\"SHAP\" values are used for feature importance explanation, with results saved as `results/figures/shap_importance.png`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
0,
1,
2,
3
],
"criteria": "The ROC curve saved as `results/figures/roc_curve.png`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
0,
1,
2,
3,
4,
5
],
"criteria": "An HTML report containing results and visualizations is generated, saved as `results/report.html`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 7,
"prerequisites": [
1,
2,
3
],
"criteria": "A performance comparison with another model (e.g., Logistic Regression) is included to validate the robustness of the XGBoost model.",
"category": "Other",
"satisfied": null
},
{
"requirement_id": 8,
"prerequisites": [
1,
2,
3
],
"criteria": "A XGBoost model is saved under `models/saved_models/`.",
"category": "Save Trained Model",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The SHAP visualizations should be clear and highlight the most impactful features, making the results easy to interpret.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": true
} |