Authors: Dinesh G, Kabilan KM, Sowndar K, Vikram S, Indhuja N
Abstract: Student performance prediction is an important application of machine learning in educational data mining. Academic institutions collect different types of information, including attendance, internal assessment marks, previous academic performance, study habits, and assignment completion. Analyzing these factors manually can be time-consuming, particularly when the number of students increases. This paper presents a machine-learning-based framework for predicting student academic performance using relevant educational attributes. The proposed system consists of data collection, data preprocessing, exploratory data analysis, feature selection, model training, evaluation, and prediction. Classification algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine can be evaluated to identify a suitable model for categorizing students into performance levels. Regression methods can alternatively be used when the target variable is a numerical examination score. The system is intended to help educators identify students who may require additional academic support and plan appropriate interventions. Model performance is evaluated using appropriate classification or regression metrics. Since actual experimental measurements depend on the selected dataset and implementation, the proposed framework does not assume unverified accuracy values. The study also discusses data privacy, fairness, interpretability, and the limitations of predictive educational systems.
DOI: http://doi.org/
