Article
ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING REGRESSION TECHNIQUES
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 regression-based 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 R² 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 data-driven approach, enabling early identification of at-risk students and supporting improved academic planning. Overall, this system provides an efficient and scalable solution for enhancing student performance analysis and decision-making in educational environments.
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