Leveraging Ensemble Learning and Feature Engineering for Enhanced Predictive Accuracy in Customer Churn Prediction

Abstract

Customer churn prediction is a critical challenge for businesses seeking to maintain and grow their customer base. This research investigates the application of ensemble learning techniques combined with advanced feature engineering to enhance the accuracy of churn prediction models. We explore several ensemble methods, including Random Forest, Gradient Boosting Machines (GBM), and XGBoost, and evaluate their performance against traditional machine learning algorithms. Furthermore, we implement a comprehensive feature engineering strategy, incorporating techniques such as interaction feature generation, polynomial features, and domain-specific feature extraction. Our results demonstrate that the proposed approach significantly improves churn prediction accuracy compared to baseline models, offering valuable insights for customer retention strategies. The study highlights the importance of both model selection and feature engineering in building robust and effective churn prediction systems.