Authors: Ms.T. Nandhini Supervisor Assistant Professor, M.Mugesh Kumar, M.Snekan, R.Tamilselvan
Abstract: This project presents a machine learning-based methodology for student performance prediction with Random Forest and Feature Engineering. Academic institutions are becoming more dependent on data-driven intelligence to enhance educational planning and student support. Conventional models usually do not capture various student characteristics like demographic, academic, and behavioral features, thus restricting predictive capabilities. In this paper, we overcome these limitations by suggesting an ensemble learning approach with sophisticated feature engineering to enhance the interpretability and flexibility of the prediction process. The Random Forest classifier is employed due to its high accuracy and stability, and the model is assessed using metrics like accuracy, precision, recall, and F1-score. Experimental results indicate that the new system surpasses conventional AFSA-based models in detecting at-risk students, allowing for early intervention approaches to improve academic performance.
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