Students Performance Prediction in Online Courses Using Machine Learning Algorithms

Online learning has attracted a large number of participants because it has no limit to enrollment and regardless of personal background and location. Predicting academic performance is an important task for the students in university, college, and school, etc. Machine Learning is a field of computer science that makes the computer to learn itself without any help of external programs. The dataset used in this project is stored in a SQL database and accessed using queries as and when required. There are two approaches for machine learning techniques one is supervised learning and the other one is unsupervised learning. In unsupervised learning, K-means clustering are being used and in supervised, ensemble techniques like Random Forest and XgBoost algorithm are implemented. Nowadays evaluating the student performance of any organization is going to play a vital role to train the students. All of the above algorithms were combined and used for student evaluation and a possible suggestion to the student is provided to improve their career.

Keywords: Predicting Academic Performance of Students, Machine Learning, K-Means, XG Boost, Random Forest, Ensemble method.