Enhancing Customer Retention In Telecom Industry With Machine Learning Driven Churn Prediction

This article discusses the importance of predicting customer churn for businesses and the use of machine learning algorithms to address this issue. It introduces a novel approach, the Ratio-based data balancing technique, which improves accuracy in predictive modeling by addressing data skewness. The study also highlights the effectiveness of ensemble algorithms and the critical role of data balancing techniques in optimizing churn prediction models.