Citation

Islam, Aminul and Hoque, Jesmeen Mohd Zebaral and Hossen, Md. Jakir and Basiron, Halizah and Tawsif Khan, Chy. Mohammed (2025) Student Major Subject Prediction Model for Real-Application Using Neural Network. International Journal of Advances in Intelligent Informatics, 11 (2). p. 292. ISSN 2442-6571

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Abstract

The university admission test is an arena for students in Bangladesh. Millions of students have passed the higher secondary school every year, and only limited government medical, engineering, and public universities are available to pursue their further study. It is challenging for a student to prepare all these three categories simultaneously within a short period in such a competitive environment. Selecting the correct category according to the student's capability became important rather 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 the maximum 97.13% prediction accuracy through a systematic process of comparing 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 opinionating the possible option 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
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	29 Jul 2025 05:47
Last Modified:	29 Jul 2025 05:47
URII:	http://shdl.mmu.edu.my/id/eprint/14389

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