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Title : A Study on Machine Learning Based Student Scholarship Prediction
Author : DUDEKULA RESHMA, SREEKANTAM VASUDHA, K BALAJI SUNIL CHANDRA
In today's world, top-tier educational institutions are increasingly opting for predictive analytics tools. In order to get implementation insights, generate high-quality performance, and create relevant records for all areas of education, predictive analytics applied system-covering super-analytics. One of the most important metrics for gauging a teacher's effectiveness in the classroom is the grade they get. Researchers have offered a plethora of different forms of mechanical knowledge acquisition about domain name techniques for instructional objectives throughout the last decade. Improving performance via grade prediction presents unique challenges when dealing with imbalanced data sets. Consequently, it offers a comprehensive evaluation of machine learning methods for enhancing the prediction accuracy of first-semester course grading guidelines. It is possible to emphasise two modules. We start by checking how well six popular tool-learning methods— including Naive
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