DEVAI / instances /31_Cancer_Prediction_SVM_BreastCancer_ML.json
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{
"name": "31_Cancer_Prediction_SVM_BreastCancer_ML",
"query": "Could you help me create a project for breast cancer prediction using an SVM model with the Breast Cancer Wisconsin dataset? Load the dataset and perform feature selection to identify important features in `src/data_loader.py`. Implement the SVM classifier for cancer prediction in `src/model.py`. Use cross-validation to evaluate the model in `src/train.py`. Save the confusion matrix as `results/figures/confusion_matrix.png`. Put together a detailed report that documents the entire process-from data preprocessing to model training and evaluation. The report should cover the feature selection process and include a clear heatmap of the performance metrics. Save the report as `results/metrics/breast_cancer_prediction_report.pdf`.",
"tags": [
"Classification",
"Medical Analysis",
"Supervised Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"Breast Cancer Wisconsin\" dataset is used.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Feature selection is performed to identify important features in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [],
"criteria": "The \"SVM classifier\" is used for cancer prediction and should be implemented in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [
1,
2
],
"criteria": "Cross-validation is used to evaluate the model in `src/train.py`.",
"category": "Performance Metrics",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
1,
2,
3
],
"criteria": "The confusion matrix is printed and saved as `results/figures/confusion_matrix.png`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
1,
2,
3,
4
],
"criteria": "A detailed report containing the data preprocessing, model training, and evaluation process is created and saved as `results/metrics/breast_cancer_prediction_report.pdf`.",
"category": "Other",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The feature selection process should be well-documented in the report, explaining why certain features were chosen.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The heatmap should clearly distinguish between different performance metrics, such as precision, recall, and F1-score.",
"satisfied": null
},
{
"preference_id": 2,
"criteria": "The report should include a discussion on the model's performance and potential areas for improvement.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false
}