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@article{202846,
        author = {Anjali Dnyandev Khande and Rohan Shrimant Chavan and Sanket Santhosh Autade and Aditi shivlal zankar},
        title = {Machine Learning Based Student Achievement Tracking System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7471-7475},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202846},
        abstract = {The rapid growth of digital technologies in the education  sector has created new opportunities for improving the way student performance is monitored and evaluated. Traditional methods of tracking student achievement mainly depend on examination scores, attendance records, and manual observation by teachers. Although these methods are useful, they are often time-consuming, less accurate in identifying learning patterns, and unable to provide early predictions about student performance. To overcome these limitations, the application of Machine Learning (ML) in educational systems has gained significant attention in recent years.},
        keywords = {},
        month = {May},
        }