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Explaining customer churn prediction in telecom industry using tabular machine learning models
Sumana Sharma Poudel, Suresh Pokharel, Mohan Timilsina
AI Enhanced· 70%
AI summary
70% confidenceThe paper explores the application of tabular machine learning models in predicting customer churn in the telecom industry, achieving improved accuracy compared to traditional methods.
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Abstract
Key findings
- Tabular machine learning models outperform traditional methods in predicting customer churn in the telecom industry.
- The study identifies key factors contributing to customer churn, including billing and service-related issues.
- The proposed models demonstrate high accuracy in predicting churn, with an average AUC-ROC score of 0.85.
Keywords
TelecommunicationsData scienceArtificial intelligenceMachine learning
Identifiers
- Journal
- Machine Learning with Applications
- Year
- 2024