Prediction of students’ academic performance using Machine Learning Techniques
Utkarsh Verma, Chetna Garg, Megha Bhushan, Piyush Samant, Ashok Kumar, Arun Negi
Abstract
The prediction of students’ academic performance is a subset of Educational Data Mining (EDM) which deals with the large-scale data gathered from an education system. EDM aims at deriving meaningful information from this data to facilitate the stakeholders of the education system. The proposed work uses various machine learning (ML) techniques to predict the student’s academic performance using the real data collected (comprising the academic history and personal habits of the students). Furthermore, a comparison of ML techniques on different evaluation metrics has been presented. It will assist the students to keep a track of their academic performance and accordingly, manage their study pattern to help them perform well in future.