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

• URL: http://arxiv.org/abs/2603.29881v1
• Date: Mon, 09 Feb 2026 17:25:42 GMT
• Title: A Hybrid Machine Learning Approach for Graduate Admission Prediction and Combined University-Program Recommendation
• Abstract summary: 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.<n>The dataset includes 13,000 self-reported GradCafe application records from 2021 to 2025.<n>A hybrid model was developed by combining XGBoost with a residual refinement k-nearest neighbors module, achieving 87% accuracy on the test set.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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