Academic Data-Based Prediction of Student Performance

TITLE	Academic Data-Based Prediction of Student Performance
ABSTRACT	Predicting student academic performance is crucial for educational institutions to identify at-risk students and tailor interventions that enhance learning outcomes. This paper presents a comprehensive data-driven approach to predict student performance using a Random Forest Regressor model trained on multiple subject scores and demographic features. The model leverages core subject scores, gender, part-time job status, absence days, extracurricular activities, weekly self-study hours, and career aspirations to estimate an overall student performance factor. Experimental results demonstrate the model’s effectiveness with a Root Mean Squared Error (RMSE) of 4.12 and an R² score of 0.87 on the test dataset. The proposed approach outperforms baseline models and provides a robust framework for early academic performance prediction. The paper also discusses system design, implementation details, and future directions for integrating predictive analytics into educational management systems.
AUTHOR	J. Noor Ahamed, Athira R Assistant Professor, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India Student of II MCA, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India
PUBLICATION DATE	2025-10-27
VOLUME	12
ISSUE	5
PDF	21_Academic Data-Based Prediction of Student Performance.pdf
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