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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2013, 18(3), 392-398; https://doi.org/10.3390/mca18030392

A Comparative Study of Artificial Neural Networks and Logistic Regression for Classification of Marketing Campaign Results

Department of Statistics, Hacettepe University, 06800, Beytepe, Ankara, Turkey
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Published: 1 December 2013
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Abstract

In this study, we focus on Artificial Neural Networks which are popularly used as universal non-linear inference models and Logistic Regression, which is a well known classification method in the field of statistical learning; there are many classification algorithms in the literature, though. We briefly introduce the techniques and discuss the advantages and disadvantages of these two methods through an application with real-world data set related with direct marketing campaigns of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit or not after campaigns.
Keywords: Artificial Neural Networks; Logistic Regression; Classification; Marketing Artificial Neural Networks; Logistic Regression; Classification; Marketing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Koç, A.A.; Yeniay, Ö. A Comparative Study of Artificial Neural Networks and Logistic Regression for Classification of Marketing Campaign Results. Math. Comput. Appl. 2013, 18, 392-398.

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