A Statistical Approach to Graduate Admissions’ Chance Prediction

Abstract

In the current scenario, grad students often experience difficulty in choosing a proper institution for pursuing masters based on their academic performances. Although there are many consultancy services and Web applications suggesting students, institutions in which they are most likely to get admitted. But, not always the decisions are staunch since there are different kinds of students with different portfolios and performances in their academic careers and institution selection is done on the basis of historical admissions’ data. This study aims to analyze a student’s academic achievements as well as university rating and give the probability of getting admission in that university, as output. The gradient boosting regressor model is deployed, which accomplished a $${R^2}$$-score of 0.84 eventually surpassing the performance of the state-of-the-art model. In addition to $${R^2}$$-score, other performance error metrics like mean absolute error, mean square error, and root mean square error are computed and showcased.