Predicting Telecommunication User Favorite Package by Using Deep Neural Network

With the popularity of mobile phone, telecom companies have launched more and more package services. It is very difficult to choose telecom user package suitable for him or her. This paper provides a method to build a model based on deep neural network (DNN) for multi-classification, which has high accuracy to help user to pick out a favorite one from dozens of packages. The model is trained out from telecom user big data. First, the telecom big data are preprocessed to be completed integrity, normalized, balanced., and then divided into training data set, validating data set and testing data set. Second, feature engineering needs to be done in order to improve prediction model. Two types of feature engineering are presented in this work to compare which is better. Manual feature engineering is a way to build new features from expertise, while auto feature engineering is from third-party library. Third, an experimental process is design out to obtain prediction models, and use them to predict the testing data set. Finally, some conclusions are obtained by analyzing the experimental results. So, this paper proposes a multi-classification model to recommend a suitable package to a telecom user, which is trained out from telecom big data with high accuracy, more practical. Moreover, this work show that feature engineering and data preprocessing are helpful to obtain better machine learning model.