Next-Generation Computing Systems and Technologies
ISSN: 3070-3328 (Online)
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TY - JOUR AU - Banik, Barnali Gupta AU - Syed, Aman Basha PY - 2026 DA - 2026/03/07 TI - Predicting University Admission Chances Using Machine Learning JO - Next-Generation Computing Systems and Technologies T2 - Next-Generation Computing Systems and Technologies JF - Next-Generation Computing Systems and Technologies VL - 2 IS - 1 SP - 1 EP - 9 DO - 10.62762/NGCST.2026.766610 UR - https://www.icck.org/article/abs/NGCST.2026.766610 KW - prediction KW - admission KW - catBoost KW - historical data analysis KW - score-based forecasting AB - In the current academic landscape, students often face challenges in identifying suitable institutions for higher studies based on their academic and profile attributes. Existing advisory services and online tools are either expensive or lack predictive accuracy. This research proposes a machine learning-based admission prediction system that estimates the probability of university admission using historical applicant data. Linear Regression serves as a baseline model to capture linear relationships, Random Forest models non-linear feature interactions, and CatBoost is selected for its robustness on structured tabular data and native handling of categorical features. Comparative evaluation using MAE, RMSE, and R² shows that CatBoost outperforms the other models, achieving the lowest MAE of 0.042 and the highest R² of 0.81. The model also provides score-versus-admission probability analysis, enabling students to evaluate how improvements in test scores or CGPA affect their admission chances. The proposed approach offers an accurate, interpretable, and cost-free decision-support tool for students, addressing the limitations of existing admission prediction systems. SN - 3070-3328 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Banik2026Predicting,
author = {Barnali Gupta Banik and Aman Basha Syed},
title = {Predicting University Admission Chances Using Machine Learning},
journal = {Next-Generation Computing Systems and Technologies},
year = {2026},
volume = {2},
number = {1},
pages = {1-9},
doi = {10.62762/NGCST.2026.766610},
url = {https://www.icck.org/article/abs/NGCST.2026.766610},
abstract = {In the current academic landscape, students often face challenges in identifying suitable institutions for higher studies based on their academic and profile attributes. Existing advisory services and online tools are either expensive or lack predictive accuracy. This research proposes a machine learning-based admission prediction system that estimates the probability of university admission using historical applicant data. Linear Regression serves as a baseline model to capture linear relationships, Random Forest models non-linear feature interactions, and CatBoost is selected for its robustness on structured tabular data and native handling of categorical features. Comparative evaluation using MAE, RMSE, and R² shows that CatBoost outperforms the other models, achieving the lowest MAE of 0.042 and the highest R² of 0.81. The model also provides score-versus-admission probability analysis, enabling students to evaluate how improvements in test scores or CGPA affect their admission chances. The proposed approach offers an accurate, interpretable, and cost-free decision-support tool for students, addressing the limitations of existing admission prediction systems.},
keywords = {prediction, admission, catBoost, historical data analysis, score-based forecasting},
issn = {3070-3328},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 
Next-Generation Computing Systems and Technologies
ISSN: 3070-3328 (Online)
Email: [email protected]
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