Publication Date: 2024/03/06
Abstract: This research study focuses on the prediction of loan approval in a bank by utilizing logistic regression and support vector machine (SVM) algorithms. Logistic regression achieves an accuracy of 83.78%, while SVM achieves an accuracy of 83%. The dataset used for training and testing the models consists of various features including income, credit history, employment status, and loan amount. Both algorithms exhibit promising performance in accurately predicting loan approval outcomes. These findings indicate that logistic regression and SVM can serve as effective tools for banks to assess the probability of loan approval, thereby assisting in their decision-making process. Further analysis and comparison of these models can offer valuable insights for optimizing loan approval prediction systems in the banking industry.
Keywords: Loan Approval, Logistic Regression, Support Vector Machine (SVM).
DOI: https://doi.org/10.5281/zenodo.10785580
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24FEB1618.pdf
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