EVALUATING RANDOM FOREST AND XGBOOST FOR BANK CUSTOMER CHURN PREDICTION ON IMBALANCED DATA USING SMOTE AND SMOTE-ENN

The banking industry faces significant challenges in retaining customers, as churn can critically affect both revenue and reputation. This study introduces a robust churn prediction framework by comparing the performance of XGBoost and Random Forest algorithms under imbalanced data conditions. The n...

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出版年:	Media Statistika
主要な著者:	Reyuli Andespa, Kusman Sadik, Cici Suhaeni, Agus M Soleh
フォーマット:	論文
言語:	英語
出版事項:	Universitas Diponegoro 2025-10-01
主題:	customer churn xgboost random forest smote imbalanced data.
オンライン･アクセス:	https://ejournal.undip.ac.id/index.php/media_statistika/article/view/77839