CUSTOMER CHURN PREDICTION

Abstract

Customer churn, the loss of customers to a business, can significantly impact a merchant's revenue and growth. Identifying customers who are likely to leave in advance enables merchants to take proactive measures to retain them and mitigate churn. This project proposes the development of a Customer Churn Detection System, leveraging machine learning techniques, to identify customers who express intentions to leave a merchant. We will utilize historical customer data, to build a predictive model capable of accurately detecting churn. By analyzing various customer attributes and behaviors, the system will learn to recognize patterns and indicators that are indicative of potential churn. The outcome of this project is a robust Customer Churn Detection System that can provide actionable insights to merchants regarding customers who are likely to churn.