Predicting the Enrollment and Dropout of Students in the Post-Graduation Degree using Machine Learning Classifier
Al Amin Biswas1, Anup Majumder2, Md. Jueal Mia3, Itisha Nowrin4, Nadia Afrin Ritu5
1Al Amin Biswas, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
2Anup Majumder, Department of CSE, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh.
3Md. Jueal Mia, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
4Itisha Nowrin, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
5Nadia Afrin Ritu, Department of CSE, Bangladesh University of Business and Technology, Dhaka, Bangladesh.
Manuscript received on 20 August 2019. | Revised Manuscript received on 07 September 2019. | Manuscript published on 30 September 2019. | PP: 3083-3088 | Volume-8 Issue-11, September 2019. | Retrieval Number: K24350981119/2019©BEIESP | DOI: 10.35940/ijitee.K2435.0981119
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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: Nowadays, In Bangladesh, the dropout rate at post-graduation level or incompletion of the post-graduation degree is considered as a serious problem in the education sector. This work can be used to support for identifying the specific individuals as well as the institutional factors which may next lead to the enrollment or drop out at the post-graduation degree. The real dataset is used to accomplish this work. Here, seven classification algorithms namely Naïve Bayes, Multilayer Perceptron, Logistic, Locally Weighted Learning (LWL), Random Forest, Random Tree, and Part are applied in this context. A confusion matrix is calculated for each classification model. Then, we computed all the seven performance evaluation metrics (accuracy, sensitivity, precision, specificity, F1 score, FPR, and FNR). Each classifier’s performances are analyzed and measured from the computed performance evaluation metrics. Naïve Bayes, LWL, and Part classifier perform better than all other working classifiers attaining 86.36% accuracy and on the contrary, Random Tree classifier performs worst achieving 74.24% accuracy. After further analyzing of the result based on performance evaluation metrics, it is observed that LWL classifier performed best in this context among all the classifiers.
Keywords: Machine Learning, Data Mining, Classification, Post-Graduation, Enrollment, Dropout.
Scope of the Article: Machine Learning