Feature statistical analysis and comparison of machine learning models for university admission prediction

Abstract

In recent years, the pursue for academic degrees has intensified, leading to a surge in the number of undergraduate students applying for graduate programs at renowned universities worldwide. Consequently, universities have adopted a multifaceted approach to evaluate applicants, moving beyond traditional metrics like GPA to assess their overall potential. This study aims to comprehend the criteria employed by universities to select graduate applicants and assist undergraduate students in planning their academic trajectory. To achieve this, a diverse set of machine learning models are compared, including multiple linear regression and K-nearest neighbors, decision trees, support vector machines, and Bayesian classifiers. These models were trained with online admission probability data to predict the likelihood of admission and uncover the primary factors guiding university selection processes. The findings reveal that while research experience can enhance competitiveness in graduate admissions, academic indicators such as GPA, GRE scores, and language proficiency remain critical determinants of acceptance. Moreover, higher-ranked institutions exhibit a higher proportion of applicants with research experience. For candidates with strong GPAs, it is essential to demonstrate competitive language proficiency, augment research experience through well-crafted recommendation letters and personal statements. Conversely, applicants with lower GPAs should strive for outstanding GRE scores to compensate for academic performance.