Title: A customer churn prediction model in telecom industry using Improved_XGBoost

Authors: P. Swetha; R.B. Dayananda

Addresses: Department of CSE, K.S. School of Engineering and Management, Visvesvaraya Technological University, Bangalore, India ' Department of CSE, K.S. Institute of Technology, Bangalore, India

Abstract: Telecom industry has become part of human's daily routine, and the rapid increase over the last two decades results in tremendous competition among telecom service providers. Service providers should be aware of the features that make the customer to churn. In this research work, we developed a churn prediction model named Improved_XGBoost, with XGBoost as base model feature function is developed for efficient data handling. We evaluate our proposed model with two established and popular datasets, i.e., South Asia GSM and churn-big dataset. Furthermore, our proposed model achieved almost absolute accuracy of more than 99% considering the various performance metric such as accuracy, precision, recall, and F1-measure.

Keywords: telecommunication; churn prediction; customer churn; XGBoost.

DOI: 10.1504/IJCC.2023.130903

International Journal of Cloud Computing, 2023 Vol.12 No.2/3/4, pp.277 - 294

Received: 17 Jun 2020
Accepted: 17 Dec 2020

Published online: 14 May 2023 *

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