Resumen de Comparison of Methods for classifying data for division into groups when learning English

Alfiya Rafailovna Baranova, Karina Irikovna Kalimullina

• The article deals with the possibility of applying methods of data analysis, in particular data classification, with the aim of dividing the students into groups when learning English. Purpose of the research is to compare methods for classifying data such as a method of k-nearest neighbors, and decision tree. It is necessary to define what the optimality criterion is and to compare models based on these methods. The result of the research detects two models, allowing the input data to divide students into groups to study the English language, and an analysis of the feasibility of their application.