Facebook pixel tracking

The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

Contact Info
Email info@thesai.org
Website thesai.org
Follow Us
Contact Info
Email info@thesai.org
Follow Us
Research Article | Open Access |

Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study

Author 1: Hend Sayed Author 2: Manal A. Abdel-Fattah Author 3: Sherif Kholief
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 9, No. 11 · Published 2018 · Cited by 24

DOI: https://doi.org/10.14569/IJACSA.2018.091196

Download PDF Cite Call for Papers

Abstract

This study was conducted based on an assumption that Spark ML package has much better performance and accuracy than Spark MLlib package in dealing with big data. The used dataset in the comparison is for bank customers transactions. The Decision tree algorithm was used with both packages to generate a model for predicting the churn proba-bility for bank customers depending on their transactions data. Detailed comparison results were recorded and conducted that the ML package and its new DataFrame-based APIs have better-evaluating performance and predicting accuracy.

Keywords

Churn prediction Big data Machine learning Apache Spark ML package MLlib package Decision tree

How to Cite this Article

Sayed, H., Abdel-Fattah, M. A., & Kholief, S. (2018). Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study. International Journal of Advanced Computer Science and Applications, 9(11). https://doi.org/10.14569/IJACSA.2018.091196

Sayed, Hend, et al.. "Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study." International Journal of Advanced Computer Science and Applications, vol. 9, no. 11, 2018, https://doi.org/10.14569/IJACSA.2018.091196.

@article{Sayed2018,
  title     = {Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {11},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Hend Sayed and Manal A. Abdel-Fattah and Sherif Kholief},
  doi       = {10.14569/IJACSA.2018.091196},
  url       = {https://doi.org/10.14569/IJACSA.2018.091196}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

Back to Issue
IJACSA Journal Cover

IJACSA