Credit Scoring Analysis: Case Study of Using Weka

Authors

  • Frane Škegro Hrvatski Telekom d.d., Croatia
  • Jovana Zoroja Faculty of Economics and Business, University of Zagreb, Croatia
  • Vanja Šimićević Centre for Croatian Studies, University of Zagreb, Croatia

Keywords:

data base, credit risk, data mining, knowledge discovery, granting credits

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.

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

Chuang, Y.F., Chia, S.H., Wong, J.Y. (2013), “Customer Value Assessment of Pharmaceutical Marketing in Taiwan”, Industrial Management and Data Systems, Vol. 113 No. 9, pp. 1315-1333.

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Patel, H.G., Sarvakar, K. (2014), “Research Challenges and Comparative Study of Various Classification Technique Using Data Mining”, International Journal of Latest Technology in Engineering, Management & Applied Science, Vol. 3 No. 9, pp. 170-176.

UCI Machine Learning Repository, German data set, available at: https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data) (21 March 2017)

Yap, B.W., Ong, S.H., Husain, N.H.M. (2011), “Using Data Mining to Improve Assessment of Credit Worthiness via Credit Scoring Models”, Expert Systems with Applications, Vol. 38 No. 10, pp. 13274-1328.

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Published

2017-10-31

How to Cite

Škegro, F., Zoroja, J., & Šimićević, V. (2017). Credit Scoring Analysis: Case Study of Using Weka. ENTRENOVA - ENTerprise REsearch InNOVAtion, 3(1), 100–105. Retrieved from https://hrcak.srce.hr/ojs/index.php/entrenova/article/view/14057

Issue

Section

Mathematical and Quantitative Methods