A Study of Classification Based Credit Risk Analysis Algorithm
Ketaki Chopde1, Pratik Gosar2, Paras Kapadia3, Niharika Maheshwari4, Pramila M. Chawan5
1Ketaki Chopde, Computer Technology Department, Veermata Jijabai Technological Institute, Mumbai, India.
2Pratik Gosar, Computer Technology Department, Veermata Jijabai Technological Institute Mumbai, India.
3Paras Kapadia, Computer Technology Department, Veermata Jijabai Technological Institute, Mumbai, India.
4Niharika Maheshwari, Computer Technology Department, Veermata Jijabai Technological Institute, Mumbai, India.
5Pramila M Chawan, Associate Prof., Computer Technology Department, Veermata Jijabai Technological Institute, Mumbai, India.
Manuscript received on March 02, 2012. | Revised Manuscript received on March 31, 2012. | Manuscript published on April 30, 2012. | PP: 142-144 | Volume-1 Issue-4, April 2012 | Retrieval Number: D0308041412/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Almost all business organizations these days generate large amounts of data regarding their work. Simply stated, data mining refers to extracting or “mining” knowledge from large amounts of data. The information thus extracted can be used by organizations in decision making process. In this paper, we study the data mining techniques used for credit risk analysis, in particular the decision tree technique.
Keywords: Data Mining, Credit risk analysis, Decision tree.