Analysing the Impact of Irregular Sleep Patterns on Academic Performance Using Machine Learning

Abstract

Student academic Grades depend on the sleep quality of the student. The research focused on how the duration of sleep affects students' academic grades by using two machine learning models, namely decision trees and random forest classifiers. The data was collected from students who are currently studying in school and colleges. The attributes like sleep duration and academic scores were considered as a parameter for the study. The data pre-processing and correct split into the train and test set was done under the study. The decision tree and random forest implemented on the records set. The comparative study indicated that random forest returned more accurate results than the decision tree.