Investigation and Prediction of Itemsets Frequency Using Machine Learning Techniques

Abstract

Frequency of Itemsets plays a crucial role in analytics of retail industry, which delves into latent patterns in customer purchasing behavior. This paper presents an Apriori algorithm to extract associations among products in a given dataset, shedding light on frequently co-occurring items. By discerning these relationships, the business gains profound insights into customer preferences and tendencies, aiming not only to understand current purchasing behavior but also to identify potential cross-selling opportunities. As businesses rely on transactional data for insights, analysis reliability hinges on data quality. This study explores missing values, outliers, and data inconsistency, impacting market basket analysis accuracy. Leveraging the Apriori algorithm facilitates the revelation of robust product associations, enabling strategic optimization and heightened customer satisfaction. The gleaned insights inform targeted marketing, product placements, and inventory management, catalyzing more effective business optimization in the retail sector.