Machine Learning Based Customer Churn Prediction Using Improved Feature Selection Techniques

Abstract

In the contemporary, fiercely competitive business environment, the retention of customers holds utmost significance for the continued growth and profitability of companies. Customer churn, which entails the defection of customers to rival services, stands out as a critical and prevalent challenge in a variety of sectors, spanning from telecommunications to e-commerce. This study delves into the realm of customer churn prediction, presenting an innovative approach that leverages machine learning and advanced feature selection techniques. The research utilizes a comprehensive dataset encompassing customer attributes, historical behavior, and churn labels. Employing state-of-the-art machine learning algorithms, including decision trees, random forests, and support vector machines, a predictive model is developed to identify potential churners accurately. The distinctive aspect of this study lies in its feature selection methodology, which integrates recursive feature elimination (RFE) with feature importance ranking. This hybrid approach refines the feature set, eliminating irrelevant or redundant variables, thereby enhancing model efficiency and interpretability. Empirical results showcase the effectiveness of the enhanced feature selection approach in improving predictive accuracy and model performance.