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Machine Learning Models for Prediction of Loan Risk: A Review

Abhinav Pahariya, Shruti Bijawat

Abstract


At present every real scenario are transferred into digital form to reduce paperwork and human efforts. Same way the use of Machine Learning models on these applications for prediction is increased day by day. For accurate prediction knowledge of these ML models and its key features is required. Datasets which we take as input in these ML models are of different types and give different outcome for each ML model. Decision of the correct ML model is crucial for a dataset. Here we take an example of Loan Risk and try to find out the relationship between key features of ML models for a dataset. It helps us to decide a good ML model for a given ML application.


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References


Leo Breiman. “Bagging predictors”. Machine Learning. 1996; 24(1996): 123–140.

Wo-Chaing Lee. “Genetic Programming Decision Tree for Bankruptcy Prediction.” 2006 Joint Conference on Information Sciences, JCIS 2006. October 8–11, 2006, Kaohsiung, Taiwan, ROC. Atlantis Press; 2006.

Analytics vidhya. (2013–2020). What is Machine Learning? A Friendly Introduction for Aspiring Data Scientists and Managers [Online]. Available from https://www.analyticsvidhya.com/

machine-learning/.

Tjen Sien Lim, Wei-Yin Loh, Yu-Shah Shih. “A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms”. Machine Learning. 2000; 40 (2000): 203–228.

Anchal Goyal, Ranpreet Kaur. “Accuracy Prediction for Loan Risk Using Machine Learning Models”. International Journal of Computer Science Trends & Technology. January-February 2016; 4 (1): 52–57. (https://paperzz.com/doc/6945141/accuracy-prediction-for-loan-risk-using-machine-learning-...)

K. Bache and M. Lichman, UCI machine learning repository. University of California, School of Information and Computer Science (https://www.scirp.org/(S(i43dyn45teexjx455qlt3d2q))/

reference/ReferencesPapers.aspx?ReferenceID=1222177)

Towards Data Science. Machine Learning [Online]. Available from https://towardsdatascience

.com/machine-learning/home

Geeks for Geeks (03 Dec, 2021). Machine learning [Online]. Available from https://www.geeksforgeeks.org/machine-learning/

Jason Brownleek. “Compare Machine Learning Models”. Lecture notes. Deep Learning for time series. Machine Learning Mastery Pty. Ltd-Australia-2018.


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