USING MACHINE LEARNING IN THE PROCESS OF FORMING A CONSUMER BASKET
Artificial intelligence technologies are used most actively in retail. This is as retailers must deal with large amounts of data, and their instant processing allows companies in this industry to significantly increase efficiency, strengthening their position in the market. Currently, there is a vector towards retail personalization for a deeper understanding of customers. The paper examines the process of making purchases from buyers’ point of view, highlights such problems as the time spent on making purchases and spontaneous purchases. These problems can be solved by using machine learning in the process of forming a consumer basket. The purpose of the study is to prove the possibility and feasibility of using machine learning methods in the process of forming a consumer basket. The objective of the study is to build an optimal algorithm that predicts the composition of the consumer basket for a given data set. The paper uses a decision tree method to classify the most suitable products available in the store. As part of the study, a model was built and trained, and its accuracy was assessed. It was concluded that the classifier is configured to determine all positive classes correctly and can recommend products that are similar in terms of characteristics. The study conducted by the authors contributes to an increase in the level of efficiency of the process of forming a consumer basket, represented by a minimum set of food products necessary for life.