Academic Performance Prediction Using MachineLearning Approaches

Academic performance prediction plays a crucial role in educational analytics, helping to assess student outcomes and provide timely interventions. This project presents a machine learning-based system for predicting academic performance using four machine learning algorithms: Random Forest, Support Vector Machine (SVM), and Gradient Boosting. The system classifies student performance into five categories: A, B, C, D, and F, based on their academic data. The models are trained on various student-related features, such as attendance, assignment scores, and exam results, to accurately predict their final grades. Random Forest provides robust classification by aggregating decision trees, while SVM ensures high accuracy with its ability to separate data with a margin. Gradient Boosting enhances performance by focusing on misclassified instances through iterative learning. These models are trained and evaluated using Python and libraries such as scikit-learn for Random Forest, SVM, and Gradient Boosting. The system aims to provide educators with insightful predictions, helping them identify at-risk students and improve overall academic performance management. The project highlights the potential of machine learning techniques to enhance decision-making in educational institutions.

Keywords: Academic Performance Prediction, Random Forest, Support Vector Machine, Gradient Boosting, Machine Learning, Educational Analytics, Classification, Student Performance, Python, Scikit-learn.