Companies that give credit cards to clients face some problems such as non-payment, which is why companies need to control such debts, so as to minimize the risk of recovery of the investment, as a result of debtor clients. In this article, the lazy learning algorithm KNN with the method of statistical evaluation Clas- FriedmanAligned-ST was used, to help us to predict the degree of nonpayment of debts, in order to optimize and improve the prediction performed by data mining algorithms. The database used for this work contains 30000 records, each defined by 25 attributes, of which a significant sample of 5439 instances was taken, with 24 fields. A data processing model is developed, the results are discussed; And concludes with the benefits of evolutionary computing application.
|Translated title of the contribution||Prediction of non-payment of credit card customers, with application of the k-nearest neighbors algorithm and Clas-FriedmanAligned-ST|
|Title of host publication||15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology|
|Subtitle of host publication||Global Partnership for Development and Engineering Education, LACCEI 2017|
|Editors||Humberto Alvarez, Maria M. Larrondo Petrie|
|Publisher||Latin American and Caribbean Consortium of Engineering Institutions|
|State||Published - 2017|
|Event||15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017 - Boca Raton, United States|
Duration: 19 Jul 2017 → 21 Jul 2017
|Name||Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology|
|Conference||15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017|
|Period||19/07/17 → 21/07/17|
Bibliographical notePublisher Copyright:
© 2017 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.