Investigation of Shopping Habits Using Data Mining Classification Algorithms

Abstract

Data mining is the discovery of interesting and valuable information hidden in large data sets. Data mining, whose usage area is expanding day by day, is also widely used in the shopping sector. In this paper, a data collection form related to shopping habits was prepared and applied to individuals and a data set was obtained. The data obtained from this form were analyzed using data mining techniques. Thus, it was tried to determine what kinds of products people spend their money, tendency to save money according to gender and what they attach importance to shopping. In this study, many classification algorithms were used and as a result, J48, Naive Bayes, SMO and Random Forest classification algorithms were found to be the highest performing algorithms. The results revealed that gender and occupational knowledge affect the shopping rate and that the budget allocated to shopping varies according to gender. In addition, it was observed that the educational status and place of residence did not affect shopping tendency. © 2019 IEEE.