Enhanced GAN-Based Model for Telecom Customer Churn Prediction

To address the performance degradation of classification models caused by imbalanced class distributions in customer churn prediction, this paper proposes a Generative Adversarial Network (GAN)-based method for telecom user churn prediction by leveraging user behavioral characteristics. First, we introduce the telecom customer churn dataset and data preprocessing methods. Then, the constructed GAN model is utilized to generate high-quality synthetic samples of churn users. A weighted fusion prediction model is designed for churn classification. Experimental results demonstrate that the churn prediction model trained on the GAN-reconstructed dataset significantly improves performance, outperforming other models in accuracy, precision, recall, and F1-score. This approach effectively supports the precise formulation of customer retention strategies and optimized resource allocation, yielding notable economic benefits.