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Registration status prediction of students using machine learning in the context of private university of Bangladesh
Bangladesh is a densely populated country where a large portion ofcitizens is living under poverty. In Bangladesh,a significant portion of higher education is accomplished at private universities. In this twenty-first century, these students of higher education are highly mobile and different from earlier generations. Thus, retaining existing students has become a great challenge for many private universities in Bangladesh. Early prediction of the total number of registered students in a semester can help in this regard. This can have a direct impact on a private university in terms of budget, marketing strategy, and sustainability. In this paper, we have predictedthe number of registered students in a semester in the context of a private university by following several machine learning approaches. We have appliedseven prominent classifiers, namely SVM, Naive Bayes, Logistic, JRip, J48, Multilayer Perceptron, and Random Forest on a data set of more than a thousand students of a private university in Bangladesh,where each record contains fiveattributes. First, all data are preprocessed. Thenpreprocessed dataare separated into the training and testing set. Then, all these classifiers are trained and tested. Since a suitable classifier is required to solve the problem, the performances of all seven classifiers need to be thoroughly assessed. So, we have computedsix performance metrics, i.e. accuracy, sensitivity, specificity, precision, false positive rate(FPR)and false negative rate(FNR)for each of the seven classifiers and compare them. We have found that SVM outperforms all other classifiers achieving85.76%accuracy, whereas Random Forest achievedthe lowest accuracy which is 79.65%.
Private University, Machine Learning, Registration Status, Prominent Classifier, Performance Evaluation Metrics.
Md. Jueal Mia, Al Amin Biswas, Abdus Sattar, Md. Tarek Habib
Journal or Conference Name
International Journal of Innovative Technology and Exploring Engineering
Publish Year