Advanced Prediction of Performance of a Student in an University using ML Techniques

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 cloud server 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: Performance, Machine Learning, Supervised and Unsupervised learning, K Means, Random Forest, XgBoost.