Analysis of the Apriori Algorithm for Enhancing Retail Product Staple Sales Recommendations

Abstract

Products are fundamental commodities in the market that cater to various consumer needs and desires. This research employs the Apriori algorithm to generate product recommendations based on the analysis of high-demand patterns arising from product sales and association patterns. Specifically, we focus on identifying elevated sales in categories such as Bulk Products, Biscuits/Snacks, Drinks, Milk/Coffee/Tea, and Sauces & Spices during specific time intervals. The model's evaluation and validation entail measuring the Lift Ratio value, a key metric. In our assessment using the RapidMiner Studio application, we find that the Lift Ratio value equals 1. Consequently, our model asserts that combinations with a Lift Ratio value greater than or equal to 1 are deemed valid and beneficial.