Implementing XGBoost Model for Predicting Customer Churn in E-Commerce Platforms
DOI:
https://doi.org/10.62951/repeater.v3i2.401Keywords:
Churn Prediction, E-commerce, Machine Learning, XGBoostAbstract
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.
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