CUSTOMER BEHAVIOR ANALYSIS USING DATA MINING TECHNIQUES WITH AI DRIVEN RECOMMENDATIONS

Abstract

This project focuses on analyzing customer behavior using data mining techniques and providing intelligent product recommendations through machine learning. The system utilizes a dataset to identify patterns in customer purchasing habits and applies a content-based filtering algorithm to recommend products based on user preferences and past behavior. Various visualization techniques are employed to represent customer insights such as age-wise purchasing trends, gender-based product preferences, regional distribution, and spending behavior. The system includes modules for admin management, user registration, dataset loading, visualization, and recommendation generation. A web-based interface is developed to enable users to interact with the system efficiently. The recommendation engine suggests relevant products when a user inputs a current purchase item, improving decisionmaking and enhancing user experience. Experimental results show that the system effectively identifies customer trends and provides meaningful recommendations. This project demonstrates the power of integrating data mining and AI techniques in understanding customer behavior and supporting business strategies.