Modeling with logistic regression for credit grant analysis

Authors

DOI:

https://doi.org/10.33448/rsd-v11i7.29761

Keywords:

Data mining; ROC curve; Probability.

Abstract

With the advancement of Big Data and the growing number of large masses of data in the most diverse areas of study, data mining techniques become increasingly necessary to obtain accurate and robust statistical information. This study aimed to show the efficiency of logistic regression as a data mining technique in obtaining a useful and statistically effective model in the analysis of customers for granting bank credit. The data comes from the Machine Learning Repository’s at the University of California-Irvin UCI. The database was divided into two groups: training and testing. The adjusted model was selected using the stepwise method in the R program. The model met the expectations of goodness of fit, with an accuracy of approximately 72% in discriminating non-defaulting from non-defaulting customers, sensitivity of 87% of the 140 non-defaulting customers, the model was correct 122 and specificity of 38%. The ROC curve had an area of 0.847, suggesting an effective fit.

Author Biography

Sílvio Fernando Alves Xavier Júnior, Universidade Estadual da Paraíba

Licenciado em Matemática (UFPE). Possui Mestrado em Biometria e Estatísitica Aplicada (UFRPE). Doutorado em Biometria e Estatística Aplicada (UFRPE). Realizou estágio sanduíche na Texas A & M University (duração de 6 meses), United States, Biological and Agricultural Engineering Department. Coordenador do curso de Estatística (CCT/UEPB), presidente do colegiado do curso de Estatística. Membro do PROFMAT - UEPB. Áreas de interesse: Estatística Aplicada, Probabilidade e Inferência Estatística, MF-DFA, Markov Chain, PSO, Entropia e Análise de Tendências.

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Published

19/05/2022

How to Cite

BESERRA, R. S. .; BARBOSA, N. F. M. .; PEIXOTO, A. P. B. .; MORAIS XAVIER, Érika F. .; JALE, J. S. .; XAVIER JÚNIOR, S. F. A. Modeling with logistic regression for credit grant analysis. Research, Society and Development, [S. l.], v. 11, n. 7, p. e15211729761, 2022. DOI: 10.33448/rsd-v11i7.29761. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/29761. Acesso em: 26 nov. 2024.

Issue

Vol. 11 No. 7

Section

Exact and Earth Sciences

License

Copyright (c) 2022 Rafaella Santos Beserra; Nyedja Fialho Morais Barbosa; Ana Patricia Bastos Peixoto; Érika Fialho Morais Xavier; Jader Silva Jale; Sílvio Fernando Alves Xavier Júnior

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