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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.16284 |
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| _version_ | 1866917762012545024 |
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| author | Shaikhsurab, Mohammed Affan Magadum, Pramod |
| author_facet | Shaikhsurab, Mohammed Affan Magadum, Pramod |
| contents | Customer churn, the discontinuation of services by existing customers, poses a significant challenge to the telecommunications industry. This paper proposes a novel adaptive ensemble learning framework for highly accurate customer churn prediction. The framework integrates multiple base models, including XGBoost, LightGBM, LSTM, a Multi-Layer Perceptron (MLP) neural network, and Support Vector Machine (SVM). These models are strategically combined using a stacking ensemble method, further enhanced by meta-feature generation from base model predictions. A rigorous data preprocessing pipeline, coupled with a multi-faceted feature engineering approach, optimizes model performance. The framework is evaluated on three publicly available telecom churn datasets, demonstrating substantial accuracy improvements over state-of-the-art techniques. The research achieves a remarkable 99.28% accuracy, signifying a major advancement in churn prediction.The implications of this research for developing proactive customer retention strategies withinthe telecommunications industry are discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16284 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach Shaikhsurab, Mohammed Affan Magadum, Pramod Machine Learning Customer churn, the discontinuation of services by existing customers, poses a significant challenge to the telecommunications industry. This paper proposes a novel adaptive ensemble learning framework for highly accurate customer churn prediction. The framework integrates multiple base models, including XGBoost, LightGBM, LSTM, a Multi-Layer Perceptron (MLP) neural network, and Support Vector Machine (SVM). These models are strategically combined using a stacking ensemble method, further enhanced by meta-feature generation from base model predictions. A rigorous data preprocessing pipeline, coupled with a multi-faceted feature engineering approach, optimizes model performance. The framework is evaluated on three publicly available telecom churn datasets, demonstrating substantial accuracy improvements over state-of-the-art techniques. The research achieves a remarkable 99.28% accuracy, signifying a major advancement in churn prediction.The implications of this research for developing proactive customer retention strategies withinthe telecommunications industry are discussed. |
| title | Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.16284 |