Exploring the Depths of Market Basket Analysis: A Comprehensive Guide to Transaction Analysis with FP-Growth and Apriori Algorithms

Abstract

This research investigates the role of data science in understanding customer behavior and enhancing sales, focusing specifically on the application of Apriori and FP-Growth Algorithms at a retail store, Deli Point, in Labuan Bajo. It illuminates the impact of 'rubbish data' on transactional data analysis, emphasizing the need for robust data cleaning procedures to ensure accurate results. Utilizing the faster FP-Growth Algorithm, the study effectively analyzed customer purchasing patterns to identify optimal product combinations for sales improvement. It discovered that 'parsley local' and 'mint flores' items had the highest support with a value of 0.036, indicating that strategic placement of these items together could enhance sales. The rule between chicken leg bone, orange sunkist, and chicken breast boneless was found to have a high confidence value and a lift value higher than 1, implying a higher potential for these items to be sold when positioned near each other. This study contributes to understanding consumer behavior and provides insights for enhancing sales and competitiveness in the retail industry. An association rule involving 'chicken leg bone’, 'orange sunkist', and 'chicken breast boneless' demonstrated high confidence and a lift value above one, suggesting significant sales potential when these items are grouped together. This study not only contributes valuable insights into retail consumer behavior and effective product placement strategies but also underscores the transformative role of data science in optimizing sales and boosting competitiveness in the retail sector.