Design and Implementation of a System for Comparative Analysis of Learning Architectures for Churn Prediction

Telecom companies are increasing their efforts in customer retention because acquiring new customers often costs much more than retaining existing ones. Therefore, it is important for operators to predict customer churns rapidly and accurately. Machine learning (ML) has been widely used for predictive churn modeling. However, classical ML methods require manual feature selection and time-consuming...