Analysis and Prediction of Student Academic Performance Using Machine Learning - Oyedeji, Ajibola Oluwafemi; Salami, Abdulrazaq M; Folorunsho, Olaolu; Abolade, Olatilewa R. (JITCE (Journal of Information Technology and Computer Engineering), 2020)
Analyzing the academic performance of students is of utmost importance for academic institutions and educationists, so as to know the ways of improving individual student’s performance. The project analyzed the past results of students including their individual attributes including age, demographic distribution, family background and attitude to study and tests this data using machine learning tools. Three models which are; Linear regression for supervised learning, linear regression with deep learning and neural network were tested using the test and train data with the Linear regression for supervised learning having the best mean average error (MAE).