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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Autors principals:	Reyuli Andespa, Kusman Sadik, Cici Suhaeni, Agus M Soleh
Format:	Artigo
Idioma:	Inglês
Publicat:	Universitas Diponegoro 2025-10-01
Col·lecció:	Media Statistika
Matèries:	customer churn xgboost random forest smote imbalanced data.
Accés en línia:	https://ejournal.undip.ac.id/index.php/media_statistika/article/view/77839
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