In the conditions of creating a national system of education that will promote the development of personal competences on the basis of culture and art of the Ukrainian people, folk traditions, attracting the younger generation to the achievements of spiritual culture, the issue of formation of future artistic and aesthetic competence by arts and craftsmen becomes a matter of urgency. The subject of research is the process of formation of students of educational institutions of higher education of artistic and aesthetic competence by means of decorative and applied arts. The purpose of the study is to determine the theoretical foundations of the formation of a future teacher of artistic and aesthetic competence by means of decorative and applied arts. To achieve the research goal and for solution of the tasks was to use a set of theoretical research methods: the analysis of philosophical, psychological, pedagogical, methodological and special literature, as well as analysis, synthesis, abstraction, systematization of theoretical data, comparative analysis of the dissertation papers in order to clarify the essence the concept of «professional competence of the teacher»; substantiation of the content and definition of the structure of the artistic and aesthetic competence of the teacher. The article analyzes the notion of competence as an integrative

This article presents data mining, which is based on the methods of mathematical statistics and machine learning, describes the features of applying regression analysis methods in the machine learning systems. The developed machine learning model includes the regression analysis modules based on the Bayesian linear, artificial neural network, decision tree, decision forest, and linear regressions. In the process of applying this machine learning model, using the mentioned algorithms, the corresponding regression models were constructed and their comparative analysis was performed, the results were analyzed. The results obtained indicate the feasibility of using data mining in the medical research using machine learning systems. The presented methods can serve as a basis for strategic development of a new directions of the medical data processing and decision-making in this field. We have identified the prospects for further research aimed at applying data mining methods to the healthcare system, namely, clustering, classification, anomaly detection.