CUSTOMER BEHAVIOUR ANALYSIS USING DATA MINING TECHNIQUES WITH AI-DRIVEN RECOMMENDATION

Abstract

Customer behaviour analysis has become an important component for modern businesses to understand customer preferences, improve customer engagement, and support decision-making processes. With the rapid growth of digital platforms and online transactions, large volumes of customer data are generated daily, creating opportunities for effective data analysis.This project focuses on analysing customer behaviour using data mining techniques integrated with Artificial Intelligence (AI)-driven recommendation systems. Data mining techniques such as data preprocessing and pattern analysis are used to identify customer trends, purchasing behaviour, and preferences from the dataset.The proposed system uses a content-based recommendation approach to generate product suggestions. It applies text mining techniques such as Bag of Words (Count Vectorizer) and cosine similarity to identify similar product categories and provide relevant recommendations to users. The system also includes data visualization features to present customer behaviour insights in a clear and understandable manner.By combining data mining with similarity-based recommendation techniques, the system helps improve user experience and supports better decision-making. Overall, the proposed system provides a simple, efficient, and scalable solution for customer behaviour analysis and recommendation in modern digital environments.