Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach
| Autor: | Shaikhsurab, Mohammed Affan, Magadum, Pramod |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 12 pages,2 figures |
| Databáze: | arXiv |
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