https://github.com/deaneeth/telco-churn-prediction-mlops
Production-ready ML pipeline for telco customer churn prediction using advanced ensemble methods (XGBoost, CatBoost, Random Forest). Handles class imbalance, provides business insights, and includes modular MLOps architecture. Built with scikit-learn, featuring comprehensive EDA, feature engineering, and business impact analysis.
https://github.com/deaneeth/telco-churn-prediction-mlops
catboost data-preprocessing ensemble-methods feature-engineering machine-learning mlops pipeline-development python random-forest scikit-learn telco-analytics xgboost
Last synced: 2 months ago
JSON representation
Production-ready ML pipeline for telco customer churn prediction using advanced ensemble methods (XGBoost, CatBoost, Random Forest). Handles class imbalance, provides business insights, and includes modular MLOps architecture. Built with scikit-learn, featuring comprehensive EDA, feature engineering, and business impact analysis.
- Host: GitHub
- URL: https://github.com/deaneeth/telco-churn-prediction-mlops
- Owner: deaneeth
- Created: 2025-08-20T15:18:41.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-08-23T16:09:57.000Z (10 months ago)
- Last Synced: 2025-08-31T11:18:54.904Z (10 months ago)
- Topics: catboost, data-preprocessing, ensemble-methods, feature-engineering, machine-learning, mlops, pipeline-development, python, random-forest, scikit-learn, telco-analytics, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 16.3 MB
- Stars: 1
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files: