  With the recent implementation of the K to 12 Program, academic institutions,
specifically, Colleges and Universities in the Philippines have been faced with
difficulties in determining projected freshmen enrollees vis-a-vis
decision-making factors for efficient resource management. Enrollment targets
directly impacts success factors of Higher Education Institutions. This study
covered an analysis of various characteristics of freshmen applicants affecting
their admission status in a Philippine university. A predictive model was
developed using Logistic Regression to evaluate the probability that an
admitted student will pursue to enroll in the Institution or not. The dataset
used was acquired from the University Admissions Office. The office designed an
online application form to capture applicants' details. The online form was
distributed to all student applicants, and most often, students, tend to
provide incomplete information. Despite this fact, student characteristics, as
well as geographic and demographic data based on the students' location are
significant predictors of enrollment decision. The results of the study show
that given limited information about prospective students, Higher Education
Institutions can implement machine learning techniques to supplement management
decisions and provide estimates of class sizes, in this way, it will allow the
institution to optimize the allocation of resources and will have better
control over net tuition revenue.
