Mobile phone customers churn prediction using elman and Jordan Recurrent Neural Network

The number of mobile phone user increases consistently year by year. While gaining new customer is harder than maintaining existing one, various churn predictor engine has been developed to fulfill this purpose. The implementation of Recurrent Neural Network in predicting churn is still new to this field. Same goes for Reinforcement Learning used which is the Q-learning. For that reason, this project main purpose is to develop two famous Recurrent Neural Networks; Elman and Jordan, and also equipping them with Q-Learning; to predict the probabilities of mobile phone churning rates. The scope of this project is to evaluate the performance between ERNN and JRNN. Both ERNN and JRNN algorithm had been tested using data gathered from mobile phone users and it is found that JRNN had shown to perform better in churn prediction.