Design and Comparative Analysis of Machine Learning Algorithms for Student Performance Prediction

Abstract

The increasing availability of educational data has created new opportunities for understanding and improving student learning outcomes. Predicting student performance at an early stage is crucial for identifying at-risk learners and enabling timely academic interventions. This study presents a comparative analysis of multiple machine learning algorithms for student performance prediction using structured academic and behavioral data. The dataset includes attributes such as attendance records, internal assessment scores, assignment performance, demographic information, and historical academic results. After data cleaning, normalization, and feature encoding, several supervised machine learning models are implemented and evaluated, including Linear Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting techniques. The comparative evaluation is conducted using standard performance metrics such as accuracy, precision, recall, F1-score, and root mean squared error to assess both classification and regression effectiveness. Experimental results demonstrate that ensemble-based algorithms outperform traditional linear and single-tree models by effectively capturing non-linear relationships and complex feature interactions present in student data. Random Forest and Gradient Boosting models achieve superior prediction accuracy and stability, while Support Vector Machine shows competitive performance for medium-sized datasets. The analysis also highlights the impact of feature importance and data preprocessing on model performance. The findings of this study confirm that machine learning-based predictive models can serve as effective tools for academic performance monitoring and early warning systems in educational institutions. The comparative insights provided in this work can assist educators and administrators in selecting suitable machine learning techniques for data-driven student performance evaluation and academic decision-making.