Student Result Prediction System using Linear Regression

Abstract

The success of an academic institution depends heavily on the performance of its students. A student result prediction system can be beneficial in improving their performance. This study proposes a machine learning algorithm-based Linear regression model to predict the board exam CGPA. The research used a data set of 253 entries, each encoded using one-hot encoding. The Linear regression model was created using 80% of the data set, while the remaining 20% was used to test the model. The results show that the model can accurately predict the final exam's CGPA. This can be useful in identifying students who require additional support and enhancing teaching techniques.