Open AccessDeep Neural Network for Multi-Class Prediction of Student Performance in Educational Data
Author(s) -
V. Vijayalakshmi,
K. Venkatachalapathy
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b2155.078219
Subject(s) - softmax function , artificial intelligence , computer science , deep learning , artificial neural network , machine learning , data set , activation function , process (computing) , set (abstract data type) , class (philosophy) , excellence , data mining , political science , law , programming language , operating system
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%.