Predicting Possible Loan Default Using Machine Learning

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Published: Dec 30, 2022
DOI: https://doi.org/10.22362/ijcert/2022/v9/i12/v9i1202
Keywords:
Machine Learning, Loan Default, AdaBoost classifier, Bagged classifier, Gradient Boost Classifier.

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Isha Reddy
Madhavi Nirati
K. Venkatesh Sharma

Abstract

Loan lending has been an important business activity for both individuals and financial institutions. Profit and loss of financial lenders to an extent depend on loan repayment. Loan default prediction is a crucial process that should be carried out by financial lenders to help them find out if a loan can default or not. The aim of this paper is to use data mining techniques to bring out insight from data then build a loan prediction model using machine learning algorithms and find the best-suited model for the given dataset. The four algorithms used are Decision Tree Classifier, Random Forest Classifier, AdaBoost classifier, Bagged classifier, and Gradient Boost Classifier. The results show that the bagging classifier is the most stable model with the highest mean of weighted F1 scores and the least variance.

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How to Cite
[1]
Isha Reddy, Madhavi Nirati, and K. Venkatesh Sharma, “Predicting Possible Loan Default Using Machine Learning”, Int. J. Comput. Eng. Res. Trends, vol. 9, no. 12, pp. 244–252, Dec. 2022.
Issue
Vol. 9 No. 12 (2022): December (2022) Issue
Section
Research Articles
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