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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Predicting Possible Loan Default Using Machine Learning

Ms. Isha Reddy, Ms. Madhavi Nirati, Dr.K. Venkatesh Sharma, ,
Department of Computer Science and Engineering, CVR College of Engineering, Vastunagar, Mangalpally, Ibrahimpatnam, T.S., India – 501510

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.

Ms. Isha Reddy,Ms. Madhavi Nirati,Dr.K. Venkatesh Sharma."Predicting Possible Loan Default Using Machine Learning". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.9, Issue 12,pp.244-252, December - 2022, URL :,

Keywords : Machine Learning, Loan Default, AdaBoost classifier, Bagged classifier, Gradient Boost Classifier.

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