A Hybrid Machine Learning Approach for Graduate Admission Prediction and Combined University-Program Recommendation
This addresses the problem of competitive graduate admissions for applicants by providing predictive and recommendation tools, though it is incremental as it builds on existing methods like XGBoost.
The study tackled graduate admission prediction by developing a hybrid machine learning model that achieved 87% accuracy on a test set and a recommendation system that improved expected acceptance probability by 70% for rejected applicants.
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.