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DOI: 10.14569/IJACSA.2019.0100785
PDF

Visualization and Analysis in Bank Direct Marketing Prediction

Author 1: Alaa Abu-Srhan
Author 2: Bara’a Alhammad
Author 3: Sanaa Al zghoul
Author 4: Rizik Al-Sayyed

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 7, 2019.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Gaining the most benefits out of a certain data set is a difficult task because it requires an in-depth investigation into its different features and their corresponding values. This task is usually achieved by presenting data in a visual format to reveal hidden patterns. In this study, several visualization techniques are applied to a bank’s direct marketing data set. The data set obtained from the UCI machine learning repository website is imbalanced. Thus, some oversampling methods are used to enhance the accuracy of the prediction of a client’s subscription to a term deposit. Visualization efficiency is tested with the oversampling techniques’ influence on multiple classifier performance. Results show that the agglomerative hierarchical clustering technique outperforms other oversampling techniques and the Naive Bayes classifier gave the best prediction results.

Keywords: Bank direct marketing; prediction; visualization; oversampling; Naive Bayes

Alaa Abu-Srhan, Bara’a Alhammad, Sanaa Al zghoul and Rizik Al-Sayyed, “Visualization and Analysis in Bank Direct Marketing Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 10(7), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100785

@article{Abu-Srhan2019,
title = {Visualization and Analysis in Bank Direct Marketing Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100785},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100785},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {7},
author = {Alaa Abu-Srhan and Bara’a Alhammad and Sanaa Al zghoul and Rizik Al-Sayyed}
}



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.

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