STUDENT DROPOUT PREDICTION USING MACHINE LEARNING TECHNIQUES
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Abstract
Abstract: Student dropout poses a major problem for India's colleges and universities, with ripple effects on the economy, job market, and academic achievement. Our research suggests using machine learning to predict which students might drop out by looking at their personal detail’s financial situation, and grades. We plan to build a model that gives early warnings to teachers and school leaders using methods like logistic regression, support vector machines, and random forests. Our data comes from undergrad students in various programs and includes info on their grades, money situation, and enrollment details. This study shows how schools can use data to make smart choices, cut down on dropouts, and keep more students in class.
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