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ABC Based Neural Network Approach for Churn Prediction in Telecommunication Sector

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Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2 ( ICTIS 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 84))

Abstract

Customer churn prediction has always been an important aspect of every business. Most of the companies have dedicated churn management teams which work for both churn prevention and churn avoidance. In both of the scenarios it is highly required to identify customers who may change their service providers. In this paper we have tried to propose a neural network based model to predict customer churn in telecommunication industry. We have than used Artificial Bee Colony (ABC) algorithm for neural network training and observed a substantial improvement in accuracy. To prove the efficacy of our model we have compared it against Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization algorithm (ACO). Simulation result shows that ABC trained neural network is more accurate than others in predicting customer churn in telecommunication sector.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Motilal Nehru National Institute of Technology Allahabad, Allahabad, UP, India

    Priyanka Paliwal & Divya Kumar

Authors
  1. Priyanka Paliwal
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  2. Divya Kumar
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Correspondence to Priyanka Paliwal .

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Editors and Affiliations

  1. Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Sabar Institute of Technology for Girls, Ahmedabad, Gujarat, India

    Amit Joshi

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Paliwal, P., Kumar, D. (2018). ABC Based Neural Network Approach for Churn Prediction in Telecommunication Sector. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-319-63645-0_38

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  • DOI: https://doi.org/10.1007/978-3-319-63645-0_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63644-3

  • Online ISBN: 978-3-319-63645-0

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