A Comparison of Regression Models for Prediction of Graduate Admissions

Abstract

Prospective graduate students always face a dilemma deciding universities of their choice while applying to master’s programs. While there are a good number of predictors and consultancies that guide a student, they aren’t always reliable since decision is made on the basis of select past admissions. In this paper, we present a Machine Learning based method where we compare different regression algorithms, such as Linear Regression, Support Vector Regression, Decision Trees and Random Forest, given the profile of the student. We then compute error functions for the different models and compare their performance to select the best performing model. Results then indicate if the university of choice is an ambitious or a safe one.