This work solves the problem of increasing the effectiveness of educational activities by predicting student performance based on external and internal factors. To solve this problem, a model for predicting student performance was built using the Python programming language. The initial data for building the decision tree model was taken from the UCI Machine Learning Repository platform and pre-processed using the Deductor Studio Academic analytical platform. The results of the model are presented and a study was conducted to evaluate the effectiveness of predicting student performance.

Keywords: forecasting, decision tree, student performance, influence of factors, effectiveness assessment