Apriori Algorithm based Association Rule Mining to Enhance Small-Scale Retailer Sales

Abstract

This research aims to assist small online gift store retailers who have not yet adopted a recommender system by leveraging association rules to offer personalized product recommendations to their customers. Using the Apriori algorithm and association rule mining techniques in Python, a dataset of UK online gift store retailer sales transactions was analyzed to identify frequent itemsets and association rules. The main objective of this research is to enable retailers to better manage their inventory and boost sales by offering products that customers are likely to purchase based on their previous buying behaviour. The result presents the top 10 frequent itemsets and their support values, along with a list of association rules. The result indicates that candlelight holders, bags, and tableware items are frequently purchased together. This research provides valuable insights for small online gift store retailers to enhance their sales performance and customer satisfaction by leveraging association rules to offer personalized product recommendations.