Developing an efficient behaviour segmentation model for online shoppers using ML

Abstract

Online shopping platforms generate a massive amount of user data, which can be effectively utilized to understand customer behavior and improve business performance. This project focuses on segmenting online shoppers based on their browsing and interaction patterns and predicting their likelihood of making a purchase using machine learning techniques. By analyzing features such as time spent on pages, number of visits, and product interactions, the system classifies users into meaningful groups and forecasts their purchase intentions. This approach helps businesses design personalized marketing strategies, enhance recommendation systems, and ultimately increase conversion rates. The proposed system provides accurate and real-time insights, making it highly beneficial for modern e-commerce applications.