The e-commerce industry is rapidly growing in competition. The primary challenge lies in retaining customers through quality service and reasonable pricing. Predictive customer churn techniques can identify potential losses, allowing for improved marketing strategies. Meeting high demand and enhancing loyalty necessitate tailored services and strategies. Nevertheless, e-commerce customer churn is intricate, characterized by nonlinear fluctuations and asymmetrical customer types. Imbalanced data further complicates the scenario. The study offers proactive methods and models for online market places to reduce customer churn effectively. Classification is done using techniques machine learning models. This research innovates the churn prediction by employing a unique ensemble of machine learning models. Its distinctive features include in-depth exploratory data analysis (EDA), systematic model comparison, and the novel application of XGBoost as a unifying force. This approach sets the study apart, offering a comprehensive and advanced methodology for predicting churn rate of customers.