Enhancing Retail Performance Through Market Basket Analysis

Retail optimization remains a crucial pursuit for supermarkets and grocery stores aiming to bolster customer satisfaction and sales revenue. In this context, leveraging data-driven methodologies has emerged as a promising strategy. The system features a user-friendly interface comprising login and registration functionalities, facilitating seamless access for retailers. Upon login, retailers can upload their dataset in CSV format and specify the minimum support and confidence percentages tailored to their analysis needs. Subsequently, the system employs association rule mining algorithms, including Eclat, FP-growth, and Apriori, to unearth hidden patterns and correlations within the dataset. The runtime performance of each algorithm is visualized through intuitive bar charts, providing insights into computational efficiency. Additionally, the system presents the frequent itemsets and corresponding support and confidence values, aiding retailers in understanding purchasing trends. Based on the identified associations, the system dynamically optimizes the store layout to enhance customer convenience and encourage additional purchases. By seamlessly integrating data analysis, visualization, and layout optimization functionalities, our system offers a comprehensive solution to enhance retail performance. Through empirical validation and case studies, we demonstrate the efficacy and practical utility of our approach in driving sales growth and improving the overall shopping experience for customers.