Customer Behaviour Analysis and Predictive Modelling in Supermarket: A Comprehensive Data Mining Approach

Abstract

Customer behavior analysis plays a crucial role in the retail industry by enabling businesses to understand customer preferences, purchasing patterns, and spending habits. With the rapid growth of customer transaction data, supermarkets can utilize data mining and machine learning techniques to extract meaningful insights and enhance business performance. This project, “Customer Behavior Analysis and Predictive Modeling in Supermarket: A Comprehensive Data Mining Approach,” focuses on analyzing customer purchasing behavior and providing personalized product recommendations through intelligent predictive models. The proposed web-based system includes modules such as user registration, login authentication, dataset management, customer purchase analysis, visualization, and product recommendation. Customer transaction data is processed to identify shopping trends, demographic influences, spending patterns, and product preferences. Interactive visualizations help users understand customer behavior based on factors such as age, gender, and location. A content-based filtering recommendation algorithm is employed to suggest products that customers are likely to purchase based on their previous buying history and interests. The recommendation engine improves customer experience by generating relevant and personalized product suggestions. Experimental results demonstrate that the system effectively transforms raw supermarket data into actionable business intelligence, supporting datadriven decision-making, improved marketing strategies, enhanced customer satisfaction, and increased retail profitability.