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@article{191516,
        author = {V.HEMA VARSHINI and Mrs.A. ANANDHI},
        title = {STUDENT SCHOLARSHIP RECOMMENDATION AND ELIGIBILITY ANALYSIS USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7224-7227},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191516},
        abstract = {Scholarships play a crucial role in supporting students from economically and academically diverse backgrounds. However, the traditional scholarship selection process relies heavily on manual evaluation and fixed rule-based criteria, which often leads to inefficiency, lack of transparency, and delayed decision-making. With the increasing number of applicants, it becomes difficult for institutions to fairly and accurately assess eligibility using conventional approaches. This project proposes an AI-Based Student Scholarship Recommendation and Eligibility Analysis System using Machine Learning techniques. The system analyzes student academic performance, financial background, and profile completeness to automatically determine scholarship eligibility. A Decision Tree algorithm is used to model eligibility rules and provide explainable decision paths. The proposed system improves accuracy, reduces human bias, and enhances transparency by clearly explaining eligibility outcomes. Experimental results demonstrate that the system efficiently classifies students as eligible or not eligible},
        keywords = {},
        month = {January},
        }