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Educational data mining, Decision Tree, K-Nearest Neighbor, Neural Network, Random Forest, Support Vector Machine
Predicting the student performance is playing vital role in educational sector so that the analysis of student’s status helps to improve for better performance. Applying data mining concepts and algorithms in the field of education is Educational Data Mining. In recent days, Machine learning algorithms are very much useful in almost all the fields. Many researchers used machine learning algorithms only. In this paper we proposed the student performance prediction system using Deep Neural Network. We trained the model and tested with Kaggle dataset using different algorithms such as Decision Tree (C5.0), Naïve Bayes, Random Forest, Support Vector Machine, K-Nearest Neighbor and Deep neural network in R Programming and compared the accuracy of all other algorithms. Among six algorithms Deep Neural Network outperformed with 84% as accuracy.
V. Vijayalakshmi, K. Venkatachalapathy, "Comparison of Predicting Student’s Performance using Machine Learning Algorithms", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.12, pp.34-45, 2019. DOI:10.5815/ijisa.2019.12.04
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