Methods for Student Scholarship Prediction Using Data Mining and Machine Learning: A Review

Abstract

From a literature review to a scholarship forecast utilizing Machine Learning and Data Mining techniques, this review article has it all. Also included is a brief explanation of the ML/DM tools utilized by the academics. Data sets are also characterized as crucial in ML/DM approaches. In the IT sector, machine learning is booming in popularity. In recent years, data mining and machine learning have emerged as potent tools with wide-ranging applications in industries as diverse as business, education, and information technology. Using all of these methods, we can tackle the various ML/DM algorithms. For the purpose of detecting the continuation of each student's scholarship, algorithms like Naïve Bayes, Decision Tree, and k-NN provide more accurate results. In the end, the suggested model would compile a list of deserving scholarship recipients, and we've already discussed how accurate each method was.