A Comprehensive Analysis of Churn Prediction in Telecommunications Using Machine Learning
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866911179047174144 |
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| author | Chen, Xuhang Lv, Bo Wang, Mengqian Xiang, Xunwen Wu, Shiting Luo, Shenghong Zhang, Wenjun |
| author_facet | Chen, Xuhang Lv, Bo Wang, Mengqian Xiang, Xunwen Wu, Shiting Luo, Shenghong Zhang, Wenjun |
| contents | Customer churn prediction in the telecommunications sector represents a critical business intelligence task that has evolved from subjective human assessment to sophisticated algorithmic approaches. In this work, we present a comprehensive framework for telecommunications churn prediction leveraging deep neural networks. Through systematic problem formulation, rigorous dataset analysis, and careful feature engineering, we develop a model that captures complex patterns in customer behavior indicative of potential churn. We conduct extensive empirical evaluations across multiple performance metrics, demonstrating that our proposed neural architecture achieves significant improvements over existing baseline methods. Our approach not only advances the state-of-the-art in churn prediction accuracy but also provides interpretable insights into the key factors driving customer attrition in telecommunications services. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22654 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Analysis of Churn Prediction in Telecommunications Using Machine Learning Chen, Xuhang Lv, Bo Wang, Mengqian Xiang, Xunwen Wu, Shiting Luo, Shenghong Zhang, Wenjun Applications Machine Learning Customer churn prediction in the telecommunications sector represents a critical business intelligence task that has evolved from subjective human assessment to sophisticated algorithmic approaches. In this work, we present a comprehensive framework for telecommunications churn prediction leveraging deep neural networks. Through systematic problem formulation, rigorous dataset analysis, and careful feature engineering, we develop a model that captures complex patterns in customer behavior indicative of potential churn. We conduct extensive empirical evaluations across multiple performance metrics, demonstrating that our proposed neural architecture achieves significant improvements over existing baseline methods. Our approach not only advances the state-of-the-art in churn prediction accuracy but also provides interpretable insights into the key factors driving customer attrition in telecommunications services. |
| title | A Comprehensive Analysis of Churn Prediction in Telecommunications Using Machine Learning |
| topic | Applications Machine Learning |
| url | https://arxiv.org/abs/2509.22654 |