Macroeconomics

Product (mathematics)

Association rule learning

Apriori algorithm

Yield (engineering)

Metallurgy

Materials science

Association (psychology)

Economics

Set (abstract data type)

Data mining

Computer science

Business

Operations research

Psychology

Mathematics

Affinity analysis

Political science

Market basket

Data set

Psychotherapist

Artificial intelligence

Geometry

Revenue

Programming language

Finance

Categorical variable

Conviction

Order (exchange)

Machine learning

Communication in Statistics- Theory and Methods

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.

Market basket analysis with association rules