Deep Neural Network for Multi-Class Prediction of Student Performance in Educational Data
V. Vijayalakshmi1, K. Venkatachalapathy2
1V. Vijayalakshmi, Department of Computer Science and Engineering, Annamalai University, Chidambaram, India.
2K. Venkatachalapathy, Division of Computer and Information Science, Annamalai University, Chidambaram, India.
Manuscript received on 13 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 5073-5081 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2155078219/19©BEIESP | DOI: 10.35940/ijrte.B2155.078219
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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: Prediction of student performance is the significant part in processing the educational data. Machine learning algorithms are leading the role in this process. Deep learning is one of the important concepts of machine learning algorithm. In this paper, we applied the deep learning technique for prediction of the academic excellence of the students using R Programming. Keras and Tensorflow libraries utilized for making the model using neural network on the Kaggle dataset. The data is separated into testing data training data set. Plot the neural network model using neuralnet method and created the Deep Learning model using two hidden layers using ReLu activation function and one output layer using softmax activation function. After fine tuning process until the stable changes; this model produced accuracy as 85%.
Keywords: Deep Neural Network, Educational data, Machine Learning, Performance Prediction, R Programming.
Scope of the Article: Machine Learning