A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study

Pratik Chougule; Vinayak Rhatankar; Saiprasad Lendale; Sachin Takmare

A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study

Abstract

In higher education admissions, particularly in resource-constrained institutions, researchers face challenges in predicting student enrollment likelihood from post-entrance exam visitor data due to imbalanced or unlabeled datasets. This paper proposes a hybrid unsupervised machine learning framework that integrates One-Class Support Vector Machine (OCSVM) for anomaly detection, Gaussian Mixture Model (GMM) for probabilistic clustering, and Nearest Neighbors (NN) for similarity-based scoring. We apply the framework to a real-world dataset of 721 admitted students from D. Y. Patil College of Engineering and Technology (DYPCET), Kolhapur, India. The framework engineers domain-specific features (academic performance, geographic proximity, engagement indicators) to compute a composite likelihood score (0-1 scale). Evaluation yields an 86.1% inlier rate for OCSVM, a silhouette score of up to 0.342 (mean 0.242 ± 0.061 across random initializations) for GMM, and an average NN distance of 0.468. The system enables targeted faculty follow-ups, supporting admission cells in prioritizing high-likelihood candidates for counselor outreach. This deployable pipeline addresses gaps in unsupervised admission prediction for small colleges with limited labeled data.

Keywords

Supporting Institution

This research received no external funding. The study was conducted as part of academic research within the Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), D. Y. Patil College of Engineering and Technology, Kolhapur, India.

Ethical Statement

This study was conducted using anonymized institutional admission feedback data. No personally identifiable information of students was collected or used in this research. The dataset was obtained with institutional permission and was processed solely for academic research purposes. As the study involved secondary data analysis without direct interaction with human participants, formal ethical committee approval was not required. All procedures were carried out in accordance with applicable ethical guidelines for data privacy, confidentiality, and responsible research conduct.

Thanks

The authors thank the DYPCET Admission Cell, D. Y. Patil College of Engineering and Technology, Kolhapur, for their support and for providing access to the institutional campus visit data that made this study possible. The authors also wish to acknowledge Anushka Mohite for her valuable contributions to the broader admission analytics project of which this paper forms a part.

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Details

Primary Language

English

Subjects

Machine Learning Algorithms, Data Engineering and Data Science

Journal Section

Research Article

Authors

Pratik Chougule ^*
0009-0008-7733-4136
India

Vinayak Rhatankar
0009-0008-2132-342X
India

Saiprasad Lendale This is me
0009-0007-4194-4568
India

Sachin Takmare This is me
0009-0008-4868-0670
India

Publication Date

May 19, 2026

Submission Date

January 28, 2026

Acceptance Date

May 17, 2026

Published in Issue

Year 2026 Number: 10

IZ

https://izlik.org/JA29KN73LM

Cite

RIS / Bibtex

APA

Chougule, P., Rhatankar, V., Lendale, S., & Takmare, S. (2026). A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study. Journal of AI, 10, 85-104. https://izlik.org/JA29KN73LM

AMA

1.Chougule P, Rhatankar V, Lendale S, Takmare S. A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study. Journal of AI. 2026;(10):85-104. https://izlik.org/JA29KN73LM

Chicago

Chougule, Pratik, Vinayak Rhatankar, Saiprasad Lendale, and Sachin Takmare. 2026. “A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study”. Journal of AI, nos. 10: 85-104. https://izlik.org/JA29KN73LM.

EndNote

Chougule P, Rhatankar V, Lendale S, Takmare S (May 1, 2026) A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study. Journal of AI 10 85–104.

IEEE

[1]P. Chougule, V. Rhatankar, S. Lendale, and S. Takmare, “A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study”, Journal of AI, no. 10, pp. 85–104, May 2026, [Online]. Available: https://izlik.org/JA29KN73LM

ISNAD

Chougule, Pratik - Rhatankar, Vinayak - Lendale, Saiprasad - Takmare, Sachin. “A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study”. Journal of AI. 10 (May 1, 2026): 85-104. https://izlik.org/JA29KN73LM.

JAMA

1.Chougule P, Rhatankar V, Lendale S, Takmare S. A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study. Journal of AI. 2026;:85–104.

MLA

Chougule, Pratik, et al. “A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study”. Journal of AI, no. 10, May 2026, pp. 85-104, https://izlik.org/JA29KN73LM.

Vancouver

1.Pratik Chougule, Vinayak Rhatankar, Saiprasad Lendale, Sachin Takmare. A Hybrid Unsupervised ML Framework for Predicting Student Admissions in Higher Education: A Case Study. Journal of AI [Internet]. 2026 May 1;(10):85-104. Available from: https://izlik.org/JA29KN73LM