Predictive Modeling for Customer Churn: Data-Driven Approach

True customer churn prediction is crucial for business companies to achieve better retention and reduction of financial losses. This paper proposes the development of a machine learning model on five algorithms: K-Nearest Neighbors, Support Vector Machine, Random Forest, AdaBoost, and Gradient Boosting. After careful data preprocessing, the best-performing model achieved an accuracy of 83. Such a model provides actionable insights in determining high-risk customers and the implementation of targeted retention strategies. Future work will focus on fine-tuning the hyperparameters and exploring other ensemble techniques in order to gain greater improvements in predictive performance.