Authors: Rajat Srivastava, Mr. Ankit Singh, Sneha Mehrotra, Shaifali Singh, Shreyansh Srivastav
Abstract: There’s a lot more to student performance than just marks. Some kids barely pass written exams but shine in group projects or sports. The problem is, most colleges still judge students almost entirely by their test scores. That’s like judging a fish by its ability to climb a tree. By the time a teacher realizes someone’s struggling, that student might already be failing or even thinking of dropping out. So what if we could spot trouble earlier — way before the report card says it all? That’s what this paper is about. We used machine learning to sift through student data — attendance, past grades, even family background — and predict who might fall behind. Not just for the sake of prediction, but to actually give teachers a heads-up so they can step in and help. The results were pretty solid. Our model caught most at-risk students with over 90% accuracy. Not perfect, but a lot better than waiting till the end of the semester.
