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@article{197502,
        author = {Omkar Madchetti and Sarthak Nade and Ms. Archana Suryawanshi and Dr. Seema Chowhan},
        title = {Future Academic Prediction of Student Success: Data Driven Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6415-6421},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197502},
        abstract = {The challenge of determining academically poor performing students in advance continues to exist within educational institutions, mainly because of the use of assessment approaches based on grades that do not take into account the full spectrum of variables affecting the students' progress. In this paper, an automated identification framework using a machine learning approach that takes into consideration various behavioral, cognitive, and environmental variables is designed to identify such students. Our study evaluates several supervised classification algorithms using metrics such as accuracy, F1-score, precision, and recall, which can predict risks of academic difficulties up to 99%. The developed system was implemented in the form of an interactive dashboard providing capabilities such as real-time risk assessment, feature importance visualization, and personalized interventions based on the identified needs of individual students. The developed system allows users to make data-driven decisions for early prevention of negative academic consequences. Potential limitations of this research include limitations related to the generalization of used datasets in different institutional settings and computational requirements for implementing the developed algorithms.},
        keywords = {Academic Performance Prediction, Data-Driven Decision Making, Predictive Modeling in Education, Student Success Forecasting},
        month = {April},
        }