The telecommunications industry is greatly concerned about customer churn due to dissatisfaction with service. This has started investing in the development of machine learning (ML) models for prediction extract, examine and visualize their customers’ historical information from a vast amount big data which will assist further understand needs take appropriate actions control churn. However, hi...

Telecommunications is one of the most dynamic sectors in market, where customer base an important pawn receive safe revenues, so to focus attention paid maintaining them with active status. Migrating customers from network another varies among telecommunication companies depending on different factors such as call quality, pricing plan, minute consumption, data, sms facilities, billing issues, ...

Customer churn prediction is a challenging domain of research that contributes to customer retention strategy. The predictive performance existing machine learning models, which are often adopted by communities, appear be at bottleneck, partly due models' poor feature extraction capability. Therefore, novel algorithm, hybrid neural network with self-attention enhancement (HNNSAE), proposed in t...

In this work, we presented the strategies and techniques that we have developed for predicting the near-future churners and win-backs for a telecom company. On a large-scale and real-world database containing customer profiles and some transaction data from a telecom company, we first analyzed the data schema, developed feature computation strategies and then extracted a large set of relevant f...