Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/183764
Authors: 
Škegro, Frane
Zoroja, Jovana
Šimičević, Vanja
Year of Publication: 
2017
Citation: 
[Title:] Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Dubrovnik, Croatia, 7-9 September 2017 [Publisher:] IRENET - Society for Advancing Innovation and Research in Economy [Place:] Zagreb [Year:] 2017 [Pages:] 88-93
Abstract: 
The goal of the paper is to present the overview of methodology of using credit scoring analysis with software Weka. German credit dataset was used in order to develop a decision tree with J.48 algorithm. We present characteristics of the dataset and the main results with the focus to the interpretation of Weka output. Paper could be useful for the users of Weka that aim to use it for credit scoring analysis.
Subjects: 
data base
credit risk
data mining
knowledge discovery
granting credits
JEL: 
D81
C80
Document Type: 
Conference Paper
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