Students’ Class Performance Prediction Using Machine Learning Classifiers

  • Adeel Ahmed Department of Commerce and Data Science, University of Catania, Italy
  • Kamlesh Kumar Department of Software Engineering, Sindh Madressatul Islam University, Karachi
  • Mansoor Ahmed Khuhro Department of Computer Science, Sindh Madressatul Islam University, Karachi
  • Asif Ali Wagan Department of Computer Science, Sindh Madressatul Islam University, Karachi
  • Imtiaz Ali Halepoto Department of Software Engineering, QUEST Nawabshah, Pakistan
  • Rafaqat Hussain Arain Department of Computer Science, Shah Abdul Latif University, Khairpur Mirs
Keywords: Educational Data Mining, Preprocessing, Classifiers, Prediction


Nowadays, educational data mining is being employed as assessing tool for study and analysis of hidden patterns in academic databases which can be used to predict student’s academic performance. This paper implements various machine learning classification techniques on students’ academic records for results predication. For this purpose, data of MS(CS) students were collected from a public university of Pakistan through their assignments, quizzes, and sessional marks. The WEKA data mining tool has been used for performing all experiments namely, data pre-processing, classification, and visualization. For performance measure, classifier models were trained with 3- and 10-fold cross validation methods to evaluate classifiers' accuracy. The results show that bagging classifier combined with support vector machines outperform other classifiers in terms of accuracy, precision, recall, and F-measure score. The obtained outcomes confirm that our research provides significant contribution in prediction of students’ academic performance which can ultimately be used to assists faculty members to focus low grades students in improving their academic records.

How to Cite
Ahmed, A., Kumar, K., Khuhro, M. A., Wagan, A. A., Halepoto, I. A., & Arain, R. H. (2021). Students’ Class Performance Prediction Using Machine Learning Classifiers. Quaid-E-Awam University Research Journal of Engineering, Science & Technology, Nawabshah., 19(1), 112-121.

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