SVM-Based Education Loan Approval Prediction: An Orthogonal Polynomial Kernel Approach

This study pioneers the application of orthogonal polynomials, specifically Chebyshev, Legendre, Laguerre, and Hermite polynomials, as kernel functions in Support Vector Machines (SVMs) to develop robust models for automating education loan approvals in Indian banks. The results demonstrate that these polynomial-based models achieve a satisfactory level of accuracy, rendering them viable for practical implementation. Notably, this research fills a gap in the literature by focusing exclusively on orthogonal polynomials in the context of education loan approval prediction, with a specific emphasis on the Indian banking sector. To the best of our knowledge, no prior study has explored this niche segment using these mathematical concepts.