GitHub / KasiMuthuveerappan / CAB-Ensemble-Learning-CHURN-Prediction

📘 This repository predicts OLA driver churn using ensemble methods—Bagging (Random Forest) and Boosting (XGBoost)—with KNN imputation and SMOTE. It reveals city-wise churn trends and key performance drivers, powering smarter, data-backed retention strategies for the ride-hailing industry.

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KasiMuthuveerappan%2FCAB-Ensemble-Learning-CHURN-Prediction
PURL: pkg:github/KasiMuthuveerappan/CAB-Ensemble-Learning-CHURN-Prediction

Topics: bagging-ensemble, boosting-ensemble, decision-tree-classifier, exploratory-data-analysis, knn-imputer, random-forest, smote-oversampler, xgbclassifier, xgboost

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