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Implementing Supervised Machine Learning Algorithms on an Investigation of Customer Mobility in the Telecom Sector

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Anuradha Boya
Maddela Parameswar
R. Suhasini
Modugula Siva Jyothi
D. T. V. Dharmajee Rao
B. Thanuja
Published in
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering
Publisher
Springer Nature Singapore

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Abstract

This chapter delves into the critical issue of customer churn in the telecom sector and the role of supervised machine learning algorithms in predicting and mitigating it. The author explores the application of data mining techniques to analyze customer behavior and identify patterns that indicate a likelihood of churn. The chapter compares the performance of various machine learning models, including Logistic Regression, Decision Trees, Random Forest, Gradient Boosting Machines, Support Vector Machines, and Neural Networks, in predicting customer churn. It also discusses the importance of evaluating these models using metrics such as precision, recall, accuracy, and the ROC curve. The chapter concludes with a summary of the findings and their implications for telecom companies looking to improve customer retention strategies.
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previous chapter Robust Fusion of Multimodality Medical Images with Fidelity Driven Optimization and Detail -Preservation
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Title
Implementing Supervised Machine Learning Algorithms on an Investigation of Customer Mobility in the Telecom Sector
Authors
Anuradha Boya
Maddela Parameswar
R. Suhasini
Modugula Siva Jyothi
D. T. V. Dharmajee Rao
B. Thanuja
Copyright Year
2026
Publisher
Springer Nature Singapore
Book
Proceedings of the 7th International Conference on Communications and Cyber Physical Engineering

Print ISBN: 978-981-9502-68-4

Electronic ISBN: 978-981-9502-69-1

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-981-95-0269-1_4
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