MD.AHMED,KADIMI DEEPIKA,GUMMADI PAVANI NAGA LAKSHMI DURGA,CHANDIKA BALA NAGA VAMSI,ABDUL KHUDUS 03 03 Academic performance prediction has become an important area of research in the education sector as institutions aim to improve student success and learning outcomes Identifying academically weak students at an early stage allows educators to provide timely guidance mentoring and remedial support Traditional evaluation methods mainly rely on examination results attendance records and teacher observations which often fail to capture the complex factors that influence student performance With the availability of educational datasets and advancements in data analytics machine learning techniques provide a powerful approach for analyzing academic data and predicting future outcomes This project presents a Student Performance Prediction System that uses regressionbased machine learning models to analyze student academic data and predict final performance The system considers various factors such as study hours attendance percentage internal assessment marks and other academic indicators that influence student results The collected dataset is preprocessed to handle missing values normalize features and prepare the data for model training Machine learning algorithms such as Linear Regression and Random Forest Regression are applied to build predictive models The models are trained and evaluated using performance metrics including Mean Absolute Error MAE Root Mean Square Error RMSE and R Score to measure prediction accuracy Experimental results demonstrate that the Random Forest Regression model provides better prediction performance compared to Linear Regression due to its ability to capture complex relationships between variables The proposed system helps educational institutions move from traditional evaluation methods to a datadriven approach enabling early identification of atrisk students and supporting improved academic planning Overall this system provides an efficient and scalable solution for enhancing student performance analysis and decisionmaking in educational environments Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026