Prediction of Telecom Churn using Comparative Analysis of Three Classifiers of Artificial Neural Network
Youngkeun Choi1, Jae Won Choi2
1Youngkeun Choi*, Division of Business Administration, Sangmyung University, Seoul, Korea.
2Jae Won Choi, Department of Computer Science, University of Texas at Dallas, Richardson, TX, USA.
Manuscript received on July 06, 2020. | Revised Manuscript received on July 15, 2020. | Manuscript published on August 10, 2020. | PP: 17-20 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J73390891020 | DOI: 10.35940/ijitee.J7339.0891020
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© 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: The purpose of this study is to evaluate existing individual neural network-based classifiers to compare performance measurements to improve the accuracy of deviance predictions. The data sets used in this white paper are related to communication deviance and are available to IBM Watson Analytics in the IBM community. This study uses three classifiers from ANN and a split validation operator from one data set to predict the departure of communications services. Apply different classification techniques to different classifiers to achieve the following accuracy with 75.63% for deep running, 77.63% for perceptron, and 77.95% for autoMLP. With a limited set of features, including the information of customer, this study compares ANN’s classifiers to derive the best performance model. In particular, the study shows that telecom service companies with practical implications to manage potential departures and improve revenue.
Keywords: Artificial neural network, Telecom service, Churn; Deep learning, Perceptron, Auto MLP.
Scope of the Article: Deep Learning