Predicting Customer Churn Using Advanced Machine Learning Ensemble Methods with Sentiment Analysis Integration

Abstract: Customer churn represents a critical business challenge in telecommunications and subscription-based services, with annual revenue losses exceeding billions of dollars globally. This research presents a comprehensive machine learning framework for predicting customer churn using an ensemble of five advanced algorithms: Logistic Regression, Ran-dom Forest, XGBoost, LightGBM, and Gradient Boosting. We integrate structured behavioral data with sentiment analysis derived from customer feedback using TextBlob, incorporating dynamic sentiment weighting that adjusts churn probabilities by up to 45%. The framework employs SMOTE (Synthetic Minority Over-sampling Technique) for handling class imbalance and SHAP (SHapley Additive Explanations) for model interpretability. Experimental results on a telecommunications dataset of 7,043 customers demonstrate that the XGBoost classifier achieves supe-rior performance with 89.4% accuracy, 0.86 F1-score, and 0.95 AUC-ROC, outperforming baseline models by 7.8%. The sentiment weighting mechanism reduces false negatives by 23%, significantly improving identification of at-risk customers. The complete system is deployed as an interactive Gradio web application with real-time sentiment analysis, enabling businesses to make data-driven retention decisions. This research contributes a production-ready, interpretable churn prediction system that integrates multiple ensemble methods with sentiment-based probability adjustment for enhanced predictive accuracy.

Keywords: Customer Churn Prediction, Ensemble Learning, XGBoost, LightGBM, Random Forest, SMOTE, Senti- ment Analysis, SHAP, Gradio Deployment