Affiliations 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 :https://ijcert.org/ems/ijcert_papers/V9I1202.pdf,
 Adewusi, A.O., Oyedokun, T.B., Bello, M.O.:
Application of artificial neural network to loan recovery
prediction. International Journal of Housing Marke Analysis
 Chambers, B., Zaharia, M.: Spark: The definitive guide:
Big data processing made simple. ” O?Reilly Media, Inc.”
 Hamid, A.J., Ahmed, T.M.: Developing prediction
model of loan risk in banks using data mining. Machine
Learning and Applications: An International Journal
(MLAIJ) Vol 3(1) (2016)
 Hassan, A.K.I., Abraham, A.: Modeling consumer loan
default prediction using neural netware. In: 2013
INTERNATIONAL CONFERENCE ON COMPUTING,
ELECTRICAL AND ELECTRONIC ENGINEERING
(ICCEEE). pp. 239–243. IEEE (2013)
 Klaas, J.: Loan default model trap.
 Lai, L.: Loan default prediction with machine learning
techniques. In: 2020 International Conference on Computer
Communication and Network Security (CCNS). pp. 5–9.
 Marqu´es Marzal, A.I., Garc´?a Jim´enez, V., S´anchez
Garreta, J.S.: Exploring the behaviour of base classifiers in
credit scoring ensembles (2012)
 Meer, K.: Machine learning models for mortgage default
prediction in pakistan. In: 2021 International Conference on
Artificial Intelligence (ICAI). pp. 164–169. IEEE (2021)
 Murray, J.: Default on a loan, united states business law
and taxes guide national credit act (2005). act no. 34 of
2005, republic of south africa (2011)
 Odegua, R.: Predicting bank loan default with extreme
gradient boosting. arXiv preprint arXiv:2002.02011 (2020)
 Patel, B., Patil, H., Hembram, J., Jaswal, S.: Loan
default forecasting using data mining. In: 2020 International
Conference for Emerging Technology (INCET). pp. 1–4.
 Reddy, M.J., Kavitha, B.: Neural networks for
prediction of loan default using attribute relevance analysis.
In: 2010 International Conference on Signal Acquisition and
Processing. pp. 274–277. IEEE (2010)
 Rendle, S.: Factorization machines. In: 2010 IEEE
International conference on data mining. pp. 995–1000.
 Sheikh, M.A., Goel, A.K., Kumar, T.: An approach for
prediction of loan approval using machine learning
algorithm. In: 2020 International Conference on Electronics
and Sustainable Communication Systems (ICESC). pp.
490–494. IEEE (2020)
 Turkson, R.E., Baagyere, E.Y., Wenya, G.E.: A
machine learning approach for predicting bank credit
worthiness. In: 2016 Third International Conference on
Artificial Intelligence and Pattern Recognition (AIPR). pp.
1–7. IEEE (2016)
 Wang, B., Liu, Y., Hao, Y., Liu, S.: Defaults
assessment of mortgage loan with rough set and svm. In:
2007 International Conference on Computational
Intelligence and Security (CIS 2007). pp. 981–985. IEEE
 Loan Default Prediction Using Spark Machine
Learning Algorithms Aiman Muhammad Uwais and and
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