Prediction of Student Depression State of Mind Using Machine Learning Technique

Abstract

one of the current major issues for people in the modern world is depressive disorders, the health issue is what could negatively influence people. Many students nowadays are suffering from depression. Struggling students are for one cannot see or comprehended their health problems. In this work, prediction of student depression is conducted using the method known as the Linear Regression (LR), under the domain of supervised Machine Learning (ML) techniques. Information includes social contacts, academic achievement and other types of data. It checks whether a student is depressed or not. This approach mainly applies accuracy of the predicted values using r-squared (r2) and root mean squared error (rmse).