oosei25/bank-customer-churn
Churn risk modeling demo: clean messy bank data, train ML models (Logistic Regression, Random Forest, XGBoost), and explain predictions with SHAP at both global and local levels.
Stars
3
Forks
—
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 15, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/oosei25/bank-customer-churn"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
retentioneering/retentioneering-tools
Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web...
iterative/demo-bank-customer-churn
Demo DVC project training a classification model on tabular data
junfengn-ctrl/uplift-modeling-customer-retention
End-to-end uplift modeling pipeline for customer retention using T-Learner and X-Learner with...
Pradnya1208/Telecom-Customer-Churn-prediction
Customers in the telecom industry can choose from a variety of service providers and actively...
gattsu001/Telecom-Churn-Predictor
Predicts which telecom customers are likely to churn with 95% accuracy using engineered features...