Bank Direct Marketing Based on Neural Network
Hany. A. Elsalamony1, Alaa. M. Elsayad2
1Hany. A. Elsalamony, Maths Department, Faculty of Science, Helwan University, Cairo, Egypt.
2Alaa. M. Elsayad, Electrical Engineering Department, Engineering College, Salman Bin Abdul Aziz University, Wadi Addwasir, Saudi Arabia.
Manuscript received on July 26, 2013. | Revised Manuscript received on August 15, 2013. | Manuscript published on August 30, 2013. | PP: 392-400 | Volume-2, Issue-6, August 2013. | Retrieval Number: F2115082613/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: All bank marketing campaigns are dependent on customers’ huge electronic data. The size of these data source is impossible for a human analyst to come up with interesting information that will help in the decision-making process. Data mining models are completely helping in performance of these campaigns. This paper introduces applications of recent and important models of data mining; Multilayer perceptron neural network (MLPNN) and Ross Quinlan new decision tree model (C5.0). The objective is to examine the performance of MLPNN and C5.0 models on a real-world data of bank deposit subscription. The purpose is increasing the campaign effectiveness by identifying the main characteristics that affect a success (the deposit subscribed by the client) based on MLPNN and C5.0. The experimental results demonstrate, with higher accuracies, the success of these models in predicting the best campaign contact with the clients for subscribing deposit. The performances are measured by three statistical measures; classification accuracy, sensitivity, and specificity.
Keywords: Bank Marketing, Data Mining, Neural Network, C5.0.