Market basket analysis with association rules in the retail sector using Orange. Case Study: Appliances Sales Company

Abstract

This research is conducted to analyze the shopping basket by using association rules in the retail area, more speci cally in a home goods sales company such as appliances, computer items, furniture, and sporting goods, among others. With the rise of globalization and the advancement of technology, retail companies are constantly struggling to maintain and raise their pro ts, as well ordering the products and services that the customer wants to obtain. In this sense, they need a new approach to identify different objectives in order to be more competitive and successful, looking for new decision-making strategies. To achieve this goal, and to obtain clear and efficient strategies, by providing large amounts of data collected in business transactions, the need arises to intelligently analyze such data in order to extract useful knowledge that will support decision-making and, an understanding of the association patterns that occur in sales-customer behavior. Predicting which product will make the most pro t, products that are sold together, this type of information is of great value for storing products in inventory. Knowing when a product is out of fashion can support inventory management effectively. In this sense, this work presents the rules of association of products obtained by analyzing the data with the FPGrowth algorithm using the Orange tool.

Keywords

• Knowledge Discovery in Databases
• Data Mining
• Market Basket Analysis
• Association Rules
• Orange Canvas
• FP- Growth