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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Telecom customer churn prediction model : Analysis of machine learning techniques for churn prediction and factor identification in telecom sector

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pp. 613–630Vol. 45Issue 2March 2024DOI: 10.47974/JIOS-1599XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1599
Pages:
613–630

Abstract

This research project leverages exploratory data analysis (EDA) on a telecom company’s customer data to predict user churn. Utilizing Python and its libraries, including Pandas, NumPy, Matplotlib, and Scikit-Learn, it identifies key parameters crucial for accurate predictions. The results and predictions are visualized using the Flask framework and Power BI analytics tool. In the highly competitive telecommunications sector, customer churn poses a significant challenge, with an annual churn rate of 15-25%. This study addresses the escalating phenomenon of consumers freely switching between providers, leading to financial losses for businesses. By employing machine learning models, it aims to forecast potential churners, enabling companies to focus targeted retention efforts and mitigate losses. The tool’s efficacy lies in its ability to analyze patterns among churned users, offering a solution to a pressing issue in the telecom industry. Beyond its immediate application, this tool can be extended to other industries, providing valuable insights for customer retention strategies.

Keywords

Churn predictionRetentionTelecomCRMMachine learningImbalance dataUpsamplingCustomer churnRandom forest modelDecision tree modelEDA

Subject Classifications

94-XX

References

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