Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{171457,
        author = {Avinash Patil and Akshay Chavan and Ajay Chougule and Pratik Khade and Prof. S. J. Chougule},
        title = {Student Dropout System for School Education},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {8},
        pages = {4-7},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171457},
        abstract = {The "Student Dropout System for School Education" project aims to develop a predictive model that identifies and addresses the factors leading to student dropouts in educational institutions. By leveraging data from student demographics, academic performance, attendance, and socio-economic background, the system uses machine learning algorithms to predict the likelihood of a student dropping out. The goal is to provide early interventions by alerting teachers, counselors, and administrators about at-risk students, thereby enabling targeted support measures to improve retention rates. This system enhances the ability to provide personalized educational experiences, helping to minimize dropouts and improve overall student success.},
        keywords = {},
        month = {December},
        }