Abstract
Students' enrollments become an essential structure for academic management. Colleges and universities are interested in prospective students, who can further acquire scholarships and other financial support from institutions and the government. Many factors allow admission committees to identify the applicants with better parameters, including student's GPA, diplomas and exam results. The main and the most important exam for applicants in Kazakhstan the Unified National Testing (UNT), which is considered as the primary selection criteria for all national universities. Therefore, it is necessary for a student who takes a mock UNT exam to know in advance his chance to get admitted to universities. Prediction of the student's enrollment is a critical topic for many applicants because it is not clear whether it is possible to be admitted to the university with certain results. The following work aims to utilize machine learning tools to statistically analyze the predictability of Kazakhstan student's enrollment according to the different factors, including UNT results, GPA, research activity, and some other criteria. The main aim of the predictability of the student's enrollment is to help students from Kazakhstan's high schools to track the chance of being enrolled by national universities. Besides, the following prediction will help students to know their chances of admission in advance and adjust their preparation strategy accordingly.