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

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. [ABSTRACT FROM AUTHOR]

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