Data Mining Application to Credit Reliability

Authors

  • Edgard Kenny Venegas Palacios Escuela Profesional de Matemática. Facultad de Ciencias. Universidad Nacional de Ingeniería
  • Joseph Luis Kahn Casapía Escuela Profesional de Matemática. Facultad de Ciencias. Universidad Nacional de Ingenierí

Keywords:

Data mining, inear regression, bayesian methods, decision trees, artificial neural networks

Abstract

Classification models in Data Mining, focused on predicting whether a bank customer is reliable or
not to receive a credit, they present a score and precision with simple training for RL: 0:21% and 0:79%, NB:
0:74% and 0:76%, AD: 0:70% and 0:69%, RNA: 0:74% and 0:73%, cross-trained for RL: 0:20% and 0:79%,
NB: 0:73% and 0:72%, AD: 0:68% and 0:72%, RNA: 0:73% and 0:75%. In addition to using RL to find relationships
between the variables of the available database.

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References

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Published

2021-06-18

How to Cite

Venegas Palacios, E. K., & Kahn Casapía, J. L. (2021). Data Mining Application to Credit Reliability. REVCIUNI, 18(1), 37–46. Retrieved from https://revistas.uni.edu.pe/index.php/revciuni/article/view/967

Issue

Vol. 18 No. 1 (2015)

Section

Artículos

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