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Customer Churn Prediction using XGBoost

Sayali Tanpure..( Department of Data Science Dr. D. Y. Patil Arts, Commerce and Science College Pimpri)

Pooja Patil..(Department of Data Science Dr. D. Y. Patil Arts, Commerce and Science College Pimpri)

Abstract - Customer churn prediction is a critical task for businesses aiming to improve customer retention and maximize long-term profitability. This study proposes an advanced churn prediction model using XGBoost, an optimized gradient boosting technique designed for high performance on structured data. The model is evaluated using multiple performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results demonstrate that XGBoost significantly improves recall and AUC-ROC compared to traditional Gradient Boosted Decision Trees (GBDT), making it more effective in identifying potential churn customers. The findings highlight the importance of using advanced ensemble methods for predictive analytics and provide actionable insights for developing targeted customer retention strategies.Key Words: Predictive Analystics, Customer Retention, Ensemble Learning