Analysis and Application of Broadband Off-grid User Prediction Model Based on Data Mining

With the fast development of the communication industry in China, the users of a telecom carrier may transfer their business into another better telecom carrier. Therefore, such potential loss may make the telecom carrier more and more challenging. How to apply data mining technique in the prediction of broadband off-grid users and further the suitable decision to make is an increasingly popular problem. In this paper, a batch of consumer behavior data, i.e., call records and Internet data, are first extracted, transformed and integrated, which are utilized to generate user feature information; then a novel data mining method based on random forest is proposed to build a robust off-grid user prediction model in telecom enterprise and compared with decision tree and support vector machine. The experiments on the real user data of telecom show that the proposed model can efficiently predict most of potential off-grid users in a shorter time. At the same time, it also provides more accurate marketing strategies timely.