Effectual Predicting Telecom Customer Churn using Deep Neural Network
Bhawna Nigam1, Himanshu Dugar2, Niranjanamurthy M3
1Dr. Bhawna Nigam, Assistant Professor, Department of Information Technology, Institute of Engineering & Technology, Devi Ahilya University, Indore (M.P), India.
2Mr. Himanshu Dugar, Student, Bachelor of Engineering in Computer Science, Institute of Engineering and Technology, (IET DAVV), Indore (M.P), India.
3Dr. Niranjanamurthy M, Assistant Professor, Department of Computer Applications, M. S. Ramaiah Institute of Technology, Bangalore (Karnataka), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 121-127 | Volume-8 Issue-5, June 2019 | Retrieval Number: D6745048419/19©BEIESP
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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: Telecom industry has seen a phenomenal growth throughout the world in recent times. Today companies in this sector are putting their best efforts to retain their churning customers by satisfying them with offers and discounts. It is due to the fact that, acquiring a new customer is far more expensive than retaining an existing one. Deep neural network learns on its own in a supervised manner and thus can be used in this regard efficiently. In this paper we have used the H2o package of deep learning to predict telecom customer churn. H2o package stem from a multi-layer artificial neural network. Number of hidden layers, epoch, number of neurons, hidden dropout ratio, input dropout ratio and activation function have been varied to achieve high sensitivity value. Sensitivity is the percentage of churners who are correctly predicted as churning customers. Our model has achieved sensitivity of 85% and thus the results are satisfactory.
Keywords: Telecom Churn Prediction, Deep Learning, Deep Neural Network, H2o Platform, Customer Churn.
Scope of the Article: Deep Learning