Using a Binary Classification Model to Predict the Likelihood of Enrolment to the Undergraduate Program of a Philippine University
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© 2020 by IJCTT Journal
Volume-68 Issue-5
Year of Publication : 2020
Authors : Joseph A. Esquivel, James A. Esquivel
DOI :  10.14445/22312803/IJCTT-V68I5P103

How to Cite?

Joseph A. Esquivel, James A. Esquivel, "sing a Binary Classification Model to Predict the Likelihood of Enrolment to the Undergraduate Program of a Philippine University," International Journal of Computer Trends and Technology, vol. 68, no. 5, pp. 6-10, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I5P103

Abstract
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-à-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 student’s 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.

Keywords
Data Mining, Education Data Mining, Predictive Modeling, Binary Classification, Logistic Regression.

Reference
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