A Hybrid Machine Learning Approach for Graduate Admission Prediction and Combined University-Program Recommendation

TL;DR

This paper presents a hybrid machine learning framework for predicting graduate admissions and recommending suitable university-program options, achieving high accuracy and actionable guidance.

Contribution

It introduces a novel hybrid model combining XGBoost and k-nearest neighbors, along with a recommendation system based on enriched applicant and university data.

Findings

Achieved 87% accuracy in admission prediction.

Improved acceptance probability by 70% with recommendations.

University quality metrics significantly influence admissions decisions.

Abstract

Graduate admissions have become increasingly competitive. This study highlights the need for a hybrid machine learning framework for graduate admission prediction, focusing on high-quality similar applicants and a recommendation system. The dataset, collected and enriched by the authors, includes 13,000 self-reported GradCafe application records from 2021 to 2025, enriched with features from the OpenAlex API, QS World University Rankings by Subject, and Wikidata SPARQL queries. A hybrid model was developed by combining XGBoost with a residual refinement k-nearest neighbors module, achieving 87\% accuracy on the test set. A recommendation module, then built on the model for rejected applicants, provided targeted university and program alternatives, resulting in actionable guidance and improving expected acceptance probability by 70\%. The results indicate that university quality metrics strongly influence admission decisions in competitive applicant pools. The features used in the study include applicant quality metrics, university quality metrics, program-level metrics, and interaction features.