Finding the Hidden Pattern of Credit Card Holder's Churn: A Case of China.

Nie, G; Wang, G; Zhang, P; Tian, Y; Shi, Y

Finding the Hidden Pattern of Credit Card Holder's Churn: A Case of China.

Nie, G Wang, G Zhang, P

Tian, Y Shi, Y

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science, 2009, 5545 (1), pp. 561 - 569
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Nie, G	en_US
dc.contributor.author	Wang, G	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Tian, Y	en_US
dc.contributor.author	Shi, Y	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science, 2009, 5545 (1), pp. 561 - 569	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28905
dc.description.abstract	In this paper, we propose a framework of the whole process of churn prediction of credit card holder. In order to make the knowledge extracted from data mining more executable, we take the execution of the model into account during the whole process from variable designing to model understanding. Using the Logistic regression, we build a model based on the data of more than 5000 credit card holders. The tests of model perform very well.	en_US
dc.publisher	Elsevier	en_US
dc.relation.ispartof	Lecture Notes in Computer Science	en_US
dc.relation.isbasedon	10.1007/978-3-642-01973-9_63	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Finding the Hidden Pattern of Credit Card Holder's Churn: A Case of China.	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	5545	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.volume	5545	en_US

Abstract:

In this paper, we propose a framework of the whole process of churn prediction of credit card holder. In order to make the knowledge extracted from data mining more executable, we take the execution of the model into account during the whole process from variable designing to model understanding. Using the Logistic regression, we build a model based on the data of more than 5000 credit card holders. The tests of model perform very well.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/28905