Machine learning model for detecting high school students as candidates for drop-out from a study program

Transition from high school to university is not successful for all students. Before enrolling in a program, the Admission Office tries to help those students to decide whether that program is best suited for them. They do so by using collected empirical data. The challenge is to classify which students would successfully finish the program and which would not. The students who are most likely not to finish the program successfully should be warned that their decision is not necessarily the best decision for them, and they should consider some other possibilities. This paper proposes one possible criterion for the given challenge: we will develop a machine learning model with the collected data from high schools which the students have attended prior to Algebra and calculate the probability of not finishing the program successfully. Students who would be classified by the model as unsuccessful will receive recommendation for another study program.