Student major subject prediction model for real-application using neural network

Islam, Aminul and Mohd Zebaral Hoque, Jesmeen and Hossen, Md. Jakir and Basiron, Halizah (2025) Student major subject prediction model for real-application using neural network. International Journal of Advances in Intelligent Informatics, 11 (2). pp. 292-303. ISSN 2442-6571

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Abstract

The university admission test is an arena for students in Bangladesh. Millions of students have passed higher secondary school every year, and only limited government medical, engineering, and public universities are available to pursue further study. It is challenging for a student to prepare all three categories simultaneously within a short period in such a competitive environment. Selecting the correct category according to the student's capability became important than following the trend. This study developed a preliminary system to predict a suitable admission test category by evaluating students' early academic performance through data collecting, data preprocessing, data modelling, model selection, and finally, integrating the trained model into the real system. Eventually, the Neural Network was selected with a maximum 97.13% prediction accuracy through a systematic process of comparing it with three other machine learning models using the RapidMiner data modeling tool. Finally, the trained Neural Network model has been implemented by the Python programming language for evaluating the possible options to focus as a major for admission test candidates in Bangladesh.

Item Type:	Article
Uncontrolled Keywords:	Machine learning, Artificial neural network, Student’s major prediction, Model selection, Admission Test Bangladesh
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	23 Feb 2026 02:46
Last Modified:	23 Feb 2026 02:46
URI:	http://eprints.utem.edu.my/id/eprint/29496
Statistic Details:	View Download Statistic

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