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Customer annual income prediction using resampling approach

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dc.contributor.author Latif, Subhenur
dc.contributor.author Lecturer, Zakia Zaman
dc.date.accessioned 2019-05-14T07:14:03Z
dc.date.accessioned 2019-05-27T09:59:33Z
dc.date.available 2019-05-14T07:14:03Z
dc.date.available 2019-05-27T09:59:33Z
dc.date.issued 2018-06-21
dc.identifier.isbn 978-1-5386-1888-2
dc.identifier.uri http://hdl.handle.net/20.500.11948/3567
dc.description.abstract Due to the diversified nature of enormous datasets of real world, the challenging task of data mining provides interesting and powerful insights that may contribute in greater aspects. The aim of this research work is to properly classify a large, highly dependent and complex dataset to predict customer income range from other demographic attributes. The baseline evaluation phase is confronted with overfitting problem. In order to improve classification performance, well established techniques were applied here but satisfactory result in significant level is obtained with resampling method. Useful measures have been considered to evaluate and validate the models. The positive ramification of this study is it identifies the limitations of supervised classification problem for those datasets which consist of highly dependent feature vectors and also incorrect class information. en_US
dc.language.iso en_US en_US
dc.publisher IEEE en_US
dc.subject Predictive models en_US
dc.subject Computational modeling en_US
dc.subject Data models en_US
dc.subject Forestry en_US
dc.subject Training en_US
dc.subject Blogs en_US
dc.subject Machine learning algorithms en_US
dc.title Customer annual income prediction using resampling approach en_US
dc.type Other en_US

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