Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach
<|reference_start|>Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach: 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.<|reference_end|>
@article{shaikhsurab2024enhancing,
    title={Enhancing Customer Churn Prediction in Telecommunications: An Adaptive
  Ensemble Learning Approach},
    author={Mohammed Affan Shaikhsurab, Pramod Magadum},
    journal={arXiv preprint arXiv:2408.16284},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16284},
    primaryClass={cs.LG}
}