ML-Based Prediction of Ideal Discipline for UG Students: A Sustainable Educational Perspective

The main idea of this chapter is to investigate a machine learning (ML) approach to help students select the ideal college for them after the preparatory year, based on the inputs provided from the preparatory year only, such as courses and grade. The output of the system is a six-class classification of recommended colleges. We divided the 2264 records of students between 2018 and 2021 with 20 feature data using a tenfold cross-validation and then passed it to a voting classifier with ExtraTrees classifier achieving the highest accuracy with 95%. Moreover, we built a Web app with two functionalities: a single prediction (for the students) and a multi-prediction (for the administrator). The admin can upload the data as comma-separated value (CSV) or as a Microsoft Excel spreadsheet (XLSX) file, and then the file is passed to a function that appends a prediction to each record; then pass it again to the admin as a downloadable file. As for the student, it is a simple form having the required inputs to enter and predict their ideal college, in addition to a backend admin control Web app for authentication and authorization.