Enhancing Telecom Churn Prediction Using a Fine-Tuned Bagging-Based Deep Neural Network

Customer churn prediction is essential for sustaining business performance and competitive advantage in the telecommunications industry. Early identification of churn enables service providers to design effective retention strategies. This study proposes a fine-tuned bagging-based ensemble deep neural network for telecom churn prediction. The proposed model is evaluated against several ensemble and standard machine learning classifiers using two imbalanced telecom datasets. Advanced oversampling and hybrid balancing techniques are applied to address class imbalance, and model performance is assessed using multiple evaluation metrics. Experimental results show that the ensemble DNN, particularly when combined with SMOTE-ENN, achieves the strongest overall and most balanced performance on both datasets compared to the evaluated approaches, demonstrating its robustness for telecom churn prediction.