Consumer Purchase Pattern Extraction Using Apriori Algorithm in Retail Market Basket Optimization
Аннотация
Improving sales and guaranteeing customer satisfaction in the competitive retail sector depends on a precise knowledge of consumer buying behavior. This capacity guarantees consumer happiness. However, retailers frequently find it harder to discover concealed trends in large transaction data sets. This paper provides a solution to the issue of utilizing the Apriori approach to extract frequent item sets and association rules from retail market basket data. The approach consists of preparing transaction data, executing the Apriori algorithm with best support and confidence criteria, and evaluating the correlations generated by the algorithm's execution, revealing consumer buying patterns. Transactions frequently show product combos, such as bread and butter or shampoo and conditioner. The study exposes the most critical finding. Stores can improve their inventory control, promotional bundling, and product placements the findings illustrating the approach's efficacy show that Apriori can identify actionable patterns across vast data sets. The research results indicate that association rule mining is a quick way to conclude in the retail sector. It led to more focused advertising and higher revenue.