Datasets:
ArXiv:
License:
| { | |
| "name": "24_Diabetes_Prediction_LogisticRegression_PimaIndians_ML", | |
| "query": "Set up a diabetes prediction project using a Logistic Regression model and the Pima Indians Diabetes dataset. Perform feature scaling and data standardization in `src/data_loader.py`. Use cross-validation to evaluate the model in `src/train.py`, and save the accuracy score to `results/metrics/accuracy_score.txt`. Generate and save the ROC curve to `results/figures/roc_curve.png`. Create an interactive dashboard using Tableau or Power BI to showcase the model's performance and highlight important features. Ensure the dashboard is user-friendly and document the dataset processing and visualization creation steps. During development, the system should automatically manage the opening and closing of Tableau or Power BI to prevent unnecessary blocking.", | |
| "tags": [ | |
| "Classification", | |
| "Medical Analysis", | |
| "Supervised Learning" | |
| ], | |
| "requirements": [ | |
| { | |
| "requirement_id": 0, | |
| "prerequisites": [], | |
| "criteria": "The \"Pima Indians Diabetes\" dataset is used.", | |
| "category": "Dataset or Environment", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 1, | |
| "prerequisites": [ | |
| 0 | |
| ], | |
| "criteria": "Feature scaling and data standardization are implemented in `src/data_loader.py`.", | |
| "category": "Data preprocessing and postprocessing", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 2, | |
| "prerequisites": [ | |
| 1 | |
| ], | |
| "criteria": "A \"Logistic 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": [ | |
| 1, | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "The accuracy score is saved in `results/metrics/accuracy_score.txt`.", | |
| "category": "Performance Metrics", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 5, | |
| "prerequisites": [ | |
| 1, | |
| 2, | |
| 3 | |
| ], | |
| "criteria": "The ROC curve is generated and saved as `results/figures/roc_curve.png`.", | |
| "category": "Visualization", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 6, | |
| "prerequisites": [ | |
| 1, | |
| 2, | |
| 3, | |
| 4, | |
| 5 | |
| ], | |
| "criteria": "An interactive visualization dashboard using \"Tableau\" or \"Power BI\" is created to showcase model performance and important features. ", | |
| "category": "Visualization", | |
| "satisfied": null | |
| } | |
| ], | |
| "preferences": [ | |
| { | |
| "preference_id": 0, | |
| "criteria": "The dashboard should allow users to explore different aspects of the model's performance and understand which features contribute most to predictions.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 1, | |
| "criteria": "The dashboard should clearly show how the dataset was processed and how the visualizations were created.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 2, | |
| "criteria": "During development, the system should automatically open and close \"Tableau\" or \"Power BI\" as needed to avoid long periods of blocking or inactivity.", | |
| "satisfied": null | |
| } | |
| ], | |
| "is_kaggle_api_needed": false, | |
| "is_training_needed": true, | |
| "is_web_navigation_needed": false | |
| } |