Market basket analysis with association rules

Abstract

This study was conducted in order to make a Market Basket Analysis by using Association Rules. The data used in the study are the sales data of any supermarket received from the Vancouver Island University website. Data were analyzed in the Weka program using a data set containing 225 different products. Apriori and FP Growth, which are Association Rules algorithms, were tried in order. Since the data set is categorical, the Apriori algorithm did not yield any results. Therefore, the FP Growth algorithm was used and the top 10 rules were given according to the conviction value. The best rule accordingly; a customer who buys Milk, Sweet Relish and Pepperoni Pizza (Frozen) also gets eggs. Best rule with 21.06 Conviction and 1 (100%) confidence values are this rule. 24 customers who received these 3 products in the dataset received eggs. Similarly, also other rules were interpreted in this study. As a result, product placement in the supermarket can be made according to these rules. Thus, sales of these products will increase and supermarket revenue will increase directly.