Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 8 Issue 5
Student Performance Prediction
Isha D Shetty, Dipshi Shetty, Sneha Roundhal
10.7753/IJCATR0805.1003
keywords : Artificial Neural Network, Performance Prediction, Convolutional Neural Network, Multilayer perceptron, SMOTE, SGPI,CGP.I
Predicting Students performance beforehand can be very beneficial for educational institutions to improve their teaching quality. This paper proposes to predict students performance by considering their academic details. For classification, decision tree, Random forest, Support vector machine, Linear regression, Naive Bayes are used. This paper also focuses on the use of Artificial Neural network algorithms such as MLP and Convolutional Neural network for predicting the performance.
@artical{i852019ijcatr08051003,
Title = "Student Performance Prediction ",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "8",
Issue ="5",
Pages ="157 - 160",
Year = "2019",
Authors ="Isha D Shetty, Dipshi Shetty, Sneha Roundhal"}
This paper proposes to predict students performance by considering their academic details
Student’s future semester SGPI is calculated based on different categories of previous semester SGPI
Different machine learning algorithms such as decision trees,random forest,bagging,boosting and artificial neural networks were implemented
The accuracy was calculated using confusion matrix.