Implementing XGBoost Model for Predicting Customer Churn in E-Commerce Platforms

Andy Hermawan
Aji Saputra
Muhammad Dhika Rafi
Syafiq Basmallah
Yilmaz Trigumari Syah Putra
Wafa Nabila

Repeater: Publikasi Teknik Informatika dan Jaringan
Vol. 3 No. 2 (2025): April: Repeater : Publikasi Teknik Informatika dan Jaringan

Customer churn is a major challenge in e-commerce, directly affecting revenue and profit. This study aims to develop a machine learning model using XGBoost to predict churn probability. To handle class imbalance, SMOTE was applied as a resampling method, and hyperparameter tuning was performed to enhance performance. The model was evaluated using the F2-score, prioritizing recall while maintaining precision. The results show that the XGBoost model with SMOTE achieves strong performance, with an F2-score of 0.849 on the tuned test data. This model can help businesses identify at-risk customers early, enabling proactive retention strategies.