Datasets:
ArXiv:
License:
File size: 4,122 Bytes
6822471 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
{
"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
} |