Datasets:
ArXiv:
License:
| { | |
| "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 | |
| } |