Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)

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Customer Churn Analysis and Prediction in Telecommunication Sector Implementing Different Machine Learning Techniques

Authors
Samprit Gowd1, Aarati Mohite2, Debashish Chakravarty2, Sanjay Nalbalwar1, *
1Electronics and Telecommunications, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, India
2Electronics and Telecommunications, Indian Institute of Technology, Kharagpur, India
*Corresponding author. Email: nalbalwar_sanjayan@yahoo.com
Corresponding Author
Sanjay Nalbalwar
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-196-8_52How to use a DOI?
Keywords
Churn; machine learning; XGBoost; precision-recall curve; F-score; Customer churn prediction; Customer Relationship Management
Abstract

Nowadays, a large number of telecom industries are dependent on retaining their existing customer base, as retaining customers is found to be more profitable than acquiring new customers. Due to immensely growing competition in this industry, customers get various choices of services and privileges and hence leading them to churn. This problem encourages data scientists to search for solutions to help telecom industries. In this research, ‘The orange telecom churn dataset’ from Kaggle is analyzed to determine the reasons for customer churning. Different machine learning algorithms viz. Decision Tree, k-nearest neighbor, Random Forest, Naïve Bayes and XGBoost are studied and analyzed for the dataset as mentioned earlier. Results are compared to find the best algorithm to solve the problem for churn prediction. Random Forest and XGBoost algorithms performed best along with the hyperparameter optimization and hence resulted in 95.20% and 95.65% accuracies respectively. Precision-recall curve, accuracy and F-score are the different metrics utilized for the evaluation purpose.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
Series
Advances in Intelligent Systems Research
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-196-8_52
ISSN
1951-6851
DOI
10.2991/978-94-6463-196-8_52How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Samprit Gowd
AU  - Aarati Mohite
AU  - Debashish Chakravarty
AU  - Sanjay Nalbalwar
PY  - 2023
DA  - 2023/08/10
TI  - Customer Churn Analysis and Prediction in Telecommunication Sector Implementing Different Machine Learning Techniques
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 686
EP  - 700
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_52
DO  - 10.2991/978-94-6463-196-8_52
ID  - Gowd2023
ER  -