IDEAS home Printed from https://ideas.repec.org/a/eco/journ1/2017-02-90.html
   My bibliography  Save this article

Using Data Mining Techniques for Detecting the Important Features of the Bank Direct Marketing Data

Author

Listed:
  • Tuba Parlar

    (Vocational School of Antakya, Department of Computer Technology, Mustafa Kemal University, Antakya, Hatay, Turkey)

  • Songul Kakilli Acaravci

    (Faculty of Economics and Administrative Sciences, Department of Finance and Accounting, Mustafa Kemal University, Hatay, Turkey.)

Abstract

Collection of customer information is seen necessary for development of the marketing strategies. Developing technologies are used very effectively in bank marketing campaigns as in many field of life. Customer data is stored electronically and the size of this data is so immense that to analyse it manually with a team of human analysts is impossible. In this paper, data mining techniques are used to interpret and define the important features to increase the campaign's effectiveness, i.e., if the client subscribes the term deposit. The bank marketing dataset from the University of California at Irvine Machine Learning Repository has been used for the proposed paper. We consider two feature selection methods namely information gain and Chi-square methods to select the important features. The methods are compared using a supervised machine learning algorithm of Naive Bayes. The experimental results show that reduced set of features improves the classification performance

Suggested Citation

  • Tuba Parlar & Songul Kakilli Acaravci, 2017. "Using Data Mining Techniques for Detecting the Important Features of the Bank Direct Marketing Data," International Journal of Economics and Financial Issues, Econjournals, vol. 7(2), pages 692-696.
    • Handle: RePEc:eco:journ1:2017-02-90
    as

    Download full text from publisher

    File URL: http://www.econjournals.com/index.php/ijefi/article/download/4580/pdf
    Download Restriction: no
    File URL: http://www.econjournals.com/index.php/ijefi/article/view/4580/pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jinping Hu, 2023. "Customer feature selection from high-dimensional bank direct marketing data for uplift modeling," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 160-171, June.
    2. Mahsa Samsami & Ralf Wagner, 2021. "Investment Decisions with Endogeneity: A Dirichlet Tree Analysis," JRFM, MDPI, vol. 14(7), pages 1-19, July.
    3. Fatma Önay Koçoğlu & Şakir Esnaf, 2022. "Machine Learning Approach and Model Performance Evaluation for Tele-Marketing Success Classification," International Journal of Business Analytics (IJBAN), IGI Global, vol. 9(5), pages 1-18, January.

    More about this item

    Keywords

    Bank Marketing; Feature Selection; Machine Learning Methods; Data Mining; Chi-square; Information Gain;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • Y10 - Miscellaneous Categories - - Data: Tables and Charts - - - Data: Tables and Charts
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eco:journ1:2017-02-90. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.