Customer Behaviour Analysis Using Data Mining Techniques with AI-Driven Recommendation System

E-commerce platforms require businesses to understand customer behavior because it enables them to provide better personalized experiences and achieve higher user engagement. The paper describes a customer behavior analysis system which combines data mining methods with an AI-powered hybrid recommendation system to investigate customer behavior and purchasing habits. The system uses customer browsing history and transaction records together with demographic information to create personalized product recommendations based on identified behavioral patterns. The system uses Python together with the Django framework and MySQL database system to handle data efficiently. The data mining process uses association rule mining and content-based filtering techniques to discover important patterns which help increase the accuracy of recommendations. The researchers tested their system using an openaccess e-commerce dataset which includes customer demographic information and their purchasing history. The results show that the hybrid model developed by the researchers achieves 91 percent recommendation prediction accuracy while successfully detecting valuable customer segments. The system enables users to analyze visual data which shows how different demographic groups make purchasing decisions. The proposed approach helps businesses achieve better marketing outcomes through enhanced customer interaction and data-based decision support.