aspg-24-1039-1791482581 20261008180301 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Neutrosophic and Fuzzy Systems JNFS 2771-6430 2771-6449 2022 2 2 Vinay Padimi Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM University, Visakhapatnam, Andhra Pradesh, 530045, India Venkata Sravan Telu Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM University, Visakhapatnam, Andhra Pradesh, 530045, India Devarani Devi Ningombam Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM University, Visakhapatnam, Andhra Pradesh, 530045, India In peer-to-peer (P2P) lending, borrowers would access loans with lower interest rates than what they usually got from traditional lenders. People can directly borrow from the P2P platform with the rules that make them easy to borrow loans and invest free funds into P2P, which can benefit both borrowers and lenders. However, the easy way to borrow loans comes with risks. One of the major issues is that borrowers may default on the loan taken. In such cases, they can get loans quickly from P2P online platforms without any bank interferences. Thus, the lender can calculate his risk for loan default. In this project, we consider the P2P lending data to predict the loan default reassuring the lender to continue providing loans in the future. In our analysis, we consider the Logistic Regression, Naive Bayes, Random Forest, K Nearest Neighbour, and Decision tree to classify loan data based on their likelihood of default. The simulation result in our algorithm provides a significant accuracy of 94.6%. 2022 44 56 1039 https://creativecommons.org/licenses/by/4.0/ 10.54216/JNFS.020204 https://www.americaspg.com/journal/24/article/1039 https://www.americaspg.com/storage/7039.pdf