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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study
Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.091196
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 11, 2018.
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
Hend Sayed, Manal A. Abdel-Fattah and Sherif Kholief, “Predicting Potential Banking Customer Churn using Apache Spark ML and MLlib Packages: A Comparative Study” International Journal of Advanced Computer Science and Applications(IJACSA), 9(11), 2018. http://dx.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},
doi = {10.14569/IJACSA.2018.091196},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091196},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {11},
author = {Hend Sayed and Manal A. Abdel-Fattah and Sherif Kholief}
}
