ABSTRACT
In the current educational system, predicting student performance will surely help the teacher to keep track of the progress of a student. These prediction systems support universities and students in improving a student’s performance. This paper represents machine learning techniques for predicting student’s performance. This paper aims to investigate those machine-learning methods fit to predict student performance from in-house E-Governance system data in the context of Educational Data Mining(EDM). We have used Polynomial Regression, Decision Tree and Random Forest Model for performance forecasting of graduates from various engineering streams. By predicting student’s academic result priory can help both teacher and student to improve their teaching techniques and effectiveness of learning process, respectively.
