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Predicting student performance is a crucial aspect of academic planning and early intervention. This study aims to develop a predictive model that evaluates the impact of demographic information, attendance, study habits, socioeconomic background, and prior academic records on student outcomes. The research employs machine learning algorithms including Decision Trees, Random Forest, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and deep learning models to analyze large-scale educational data. Comprehensive data preprocessing and feature engineering techniques are applied to improve model performance. Statistical analysis is also conducted to assess the relevance of each factor in academic achievement. The model is trained and tested on historical student datasets and evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Results show that machine learning techniques can generate reliable predictions, supporting educators in identifying at-risk students and implementing personalized learning interventions. The findings underline the value of data-driven strategies in enhancing educational outcomes, improving student retention, and guiding curriculum development.

Copyright © 2025 P. Hema Sri. This is an open access article distributed under the Creative Commons Attribution License.