International Journal of Education and Management Engineering(IJEME)
ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)
Published By: MECS Press
IJEME Vol.12, No.4, Aug. 2022
Comparative Analysis of Data mining Methods to Analyze Personal Loans Using Decision Tree and Naïve Bayes Classifier
Full Text (PDF, 698KB), PP.33-42
The data mining classification techniques and analysis can enable banks to move precisely classify consumers into various credit risk group. Knowing what risk group a consumer falls into would allows a bank to fine tune its lending policies by recognizing high risk groups of consumers to whom loans should not be issued, and identifying safer loans that should be issued on terms commensurate with the risk of default. So research en for classification and prediction of loan grants. The attributes are determined that have greatest effect in the loan grants. For this purpose C4.5, CART and Naïve Bayes are compared and analyzed in this research. This concludes that a bank should not only target the rich customers for granting loan but it should assess the other attributes of a customer as well which play a very important part in credit granting decisions and predicting the loan defaulters.
Cite This Paper
Menuka Maharjan, "Comparative Analysis of Data mining Methods to Analyze Personal Loans Using Decision Tree and Naïve Bayes Classifier", International Journal of Education and Management Engineering (IJEME), Vol.12, No.4, pp. 33-42, 2022. DOI:10.5815/ijeme.2022.04.04
Pratik Gosar,Paras Kapadia ,Niharika,Maheswori,Pramila Chawan K. Chopde, "A study of a Classification Based Credit Risk Analysis Algorith," Intenational Journal of Engineering and Advanced Technology, vol. 1, no. 2249-8958, p. 3, April 2012.
Sarika Chaudary Sanjay Kumar Maliki, "Comparative Study of Decision Tree Algorithms For Data Analysis," International Journal of Research in Computer Engineering and Electronics, p. 8, June-2013.
A.Yilmaz camurcu Serhat Ozekes, "Classification and Prediction in a Data Mining Application," Journal of Marmarafor Pure Applied Science, pp. 169-174, 2002.
Yu,Zhong Xiao-Lin, "An Overview of Personal Credit Scoring:Techniques and Future Work," International Journal of Intelligence Science, pp. 181-189, August 2012.
Daniela Schiopu Irina Ionita, "Using Principal Component Analysis in Loan Granting," pp. 88-96, 2010.
Bora Aktan Husey Incea, "A Comparison of data Mining Techniques For Credit Scoring In Banking:A Managerial Perspective," Journal of Business Economics and Management, pp. 233-240, march 2009. 
Arun K.Pujari, Data Mining Techniques. Hyderabad, India: Universities press private limited.
P.-N. T.-N. T. Pang-Ning Tan. [Online]. Available: https://wwwusers.cs.umn.edu/~kumar001/dmbook/sol.pdf.
P. D. H. Hofmann. [Online]. Available http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29
W. H. Inman, Building Data Warehouse, QED/Wiley, Hoboken, NJ, USA, 2005
M. D. M. Sousa and R. S. Figueiredo, “Credit analysis using data mining: application in the case of a credit union,” Journal of Information Systems and Technology Management, vol. 11, no. 2, pp. 379–396, 2014.
R. Arora and S. Suman, “Comparative analysis of classification algorithms on different datasets using WEKA,” International Journal of Computer Applications, vol. 54, no. 13, pp. 21–25, 2012.
Xiao, W., Zhao, Q., and Fei, Q., 2006, "a comparative study of data mining methods in consumer loans credit scoring management." Journal of systems science and systems engineering, 15(4), 419-435
Y.U. Ryu, W.T. Yue, “Firm bankruptcy prediction: experimental comparison of isotonic separation and other classification approaches”, IEEE Trnasactions on Systems, Man & Cybernetics: Part A 35 (5) (2005) 727–737
Nikhil Madane, Siddharth Nanda,”Loan Prediction using Decision tree”, Journal of the Gujrat Research History,Volume 21 Issue 14s, December 2019.