Ziynet PAMUK, Yüksel YURTAY, Oğuz YAVUZYILMAZ

Establishing the Potential Clients Using Artificial Neural Networks

Today, technologies retrieving forward-looking information from the existing data are available. In this study, whether the clients would open a deposit account was estimated using the data in the marketing campaign of a bank in Portugal for its clients. The purpose of the study was to create a decision support system to determine the potential clients in future. The data set collected from 4.512 subjects consists of 16 input attributes (job, age, balance, etc.) and 1 output attribute (yes/no). In the study, the 6-fold cross validation method was used. The data obtained from 3.760 people were used for the training process and the data obtained from 752 people were used for the testing process. As classifiers; Feed Forward Neural Networks (FFNN), Probabilistic Neural Network (PNN) and k Nearest Neighbor (kNN) were used. At the end of the study, success ratios of different algorithms were compared by Receiver Operating Characteristics (ROC) analysis method. Feed forward neural network yielded the best result with an accuracy rate of 95.74%.

Keywords:

Bank data deposit, artificial neural network, k nearest neighbor algorith,

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[1] Moro S. , Laureano R., Cortez P. (2011). Using data mining for bank direct marketing: an application of the crisp-dm methodology. European Simulation and Modelling Conference. [2] Moro S.(2013). A data mining approach for bank telemarketing using the rminer package and R tool. Working Paper. ISCTE - Instituto Universitário de Lisboa. [3] Elsalamony Hany A., Elsayad Alaa. (2013). M. Bank direct marketing based on neural network. International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-6, August 2013. [4] Qeethara Kadhim Al-Shayea. (2013). Evaluating Marketing Campaigns of Banking Using Neural Networks, Proceedings of the World Congress on Engineering 2013, Vol II. [5] Oztemel E., Artificial Neural Networks, Papatya Publishing, İstanbul, 2003. [6] Kayhan Gulez. www.yildiz.edu.tr/~gulez/3k1n.pdf. 2015. [7] Beale, M.H., Hagan, M.T., Demuth, H.B. Neural Network Toolbox. 383, 2012. [8] Specht, D. F.(1990). Probabilistic neural networks, Neural Networks, Vol. 3 (1), pp 109-118. [9] Nearest Neighbors Tutorial people.revoledu.com/kardi/tutorial/KNN/HowTo_KNN.html. 2015. [10] Özkan, Y., Data Mining Methods. Papatya Publishing, İstanbul, 2013. [11] http://tr.wikipedia.org/wiki/ROC. 2015. [12] Fawcett, T.(2006). An introduction to ROC analysis, Pattern Recognition Letters, 27, 861-874.