CUSTOMER SEGMENTATION AND PRODUCT RECOMMENDATION SYSTEM

In today’s data-driven business environment, understanding customer behaviour is crucial for enhancing customer satisfaction and increasing profitability. This project presents a comprehensive system that combines customer segmentation and product recommendation techniques to analyse customer data effectively. The system utilizes the K-Means clustering algorithm to group customers based on attributes such as age, annual income, and spending score. These clusters represent distinct customer segments with similar characteristics.
Further, the project integrates association rule mining using the Apriori algorithm to identify frequently purchased product combinations within each customer segment. This enables businesses to understand purchasing patterns and generate targeted recommendations. The application is developed using Python and deployed through a Streamlit-based user interface, allowing users to upload datasets, perform clustering, visualize results, and view recommendations interactively.
The proposed system provides valuable insights into customer behaviour, enabling businesses to design personalized marketing strategies and improve decision-making processes.