Registration Status Prediction of Students Using Machine Learning in the Context of Private University of Bangladesh
Md. Jueal Mia1, Al Amin Biswas2, Abdus Sattar2, Md. Tarek Habib4

1Md. Jueal Mia, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
2Al Amin Biswas, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
3Abdus Sattar, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
4Md. Tarek Habib, Department of CSE, Daffodil International University, Dhaka, Bangladesh. 

Manuscript received on October 11, 2019. | Revised Manuscript received on 24 October, 2019. | Manuscript published on November 10, 2019. | PP: 2594-2600 | Volume-9 Issue-1, November 2019. | Retrieval Number: A3912119119/2019©BEIESP | DOI: 10.35940/ijitee.A5292.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Bangladesh is a densely populated country where a large portion of citizens 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 predicted the number of registered students in a semester in the context of a private university by following several machine learning approaches. We have applied seven 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 five attributes. First, all data are preprocessed. Then preprocessed data are 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 computed six 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 achieving 85.76% accuracy, whereas Random Forest achieved the lowest accuracy which is 79.65%.
Keywords: Private University, Machine Learning, Registration Status, Prominent Classifier, Performance Evaluation Metrics.
Scope of the Article: Machine Learning