Student Performance Prediction Using Machine Learning

Abstract

Student academic performance is an important factor in evaluating the effectiveness of the learning process and identifying students who may require additional academic support. Traditional methods of evaluating student performance mainly depend on examination marks and teacher observations, which may not provide sufficient information for early identification of students at academic risk. This paper presents a machine learning-based approach for predicting student performance using relevant academic and personal attributes. The proposed system involves data preprocessing, feature selection, model training, and performance evaluation. Machine learning algorithms such as Linear Regression, Decision Tree, Random Forest, and Support Vector Machine can be applied to identify patterns in student data and predict their expected academic performance. The performance of the models can be compared using suitable evaluation metrics such as accuracy, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), depending on the prediction task. The proposed approach can assist educational institutions in identifying students who may need additional guidance and support. The system demonstrates the potential of machine learning in supporting data-driven academic decision-making.