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{
"name": "14_Customer_Churn_Prediction_LogisticRegression_Telco_ML",
"query": "Help me develop a system to predict customer churn using the Telco Customer Churn dataset, potentially being downloaded from [this link](https://huggingface.co/datasets/scikit-learn/churn-prediction). Load the dataset in `src/data_loader.py`. The project should include feature engineering, such as feature selection and scaling, and handle imbalanced data using oversampling or undersampling techniques implemented in `src/data_loader.py`. The exact details of this are left for you to decide. Implement a Logistic Regression model in `src/model.py` and perform cross-validation while training the model in `src/train.py`. Finally, print and save the classification report (including precision, recall, and F1-score) to `results/metrics/classification_report.txt`, and save a ROC curve to `results/figures/roc_curve.png`. Ensure the dataset loads smoothly with appropriate error handling. The feature engineering should thoroughly select the most relevant features.",
"tags": [
"Classification",
"Supervised Learning"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"Telco Customer Churn\" dataset is used, potentially being downloaded from [this link](https://huggingface.co/datasets/scikit-learn/churn-prediction). Load the dataset in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Feature engineering, including feature selection and scaling, is implemented in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
0
],
"criteria": "Imbalanced data is handled using oversampling or undersampling techniques in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [],
"criteria": "The \"Logistic Regression\" model is implemented in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
1,
2,
3
],
"criteria": "Cross-validation is used to evaluate the model in `src/train.py`.",
"category": "Performance Metrics",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
0,
1,
2,
3,
4
],
"criteria": "A classification report, including \"precision,\" \"recall,\" and \"F1-score,\" is saved as `results/metrics/classification_report.txt`.",
"category": "Performance Metrics",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
0,
1,
2,
3,
4
],
"criteria": "A \"ROC curve\" is saved as `results/figures/roc_curve.png`.",
"category": "Visualization",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The dataset should load smoothly, with proper error handling if issues arise during download.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The feature engineering process should be thorough, ensuring that the most relevant features are selected for the model.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": true
} |