Precision Marketing Of Telecom Mobile Internet Users Based On Machine Learning

With the popularity of 4G networks and the birth of 5G technology,the mobile Internet is now experiencing an era of explosive growth.However,the rapid growth of mobile Internet users has not brought corresponding direct benefits to telecom operators.Telecom operators should start from the rich data they bring and explore new marketing models.Based on the behavior data of telecom customers accessing the mobile Internet,this paper uses machine learning and data mining technology to model and analyze users,predicting the target users to conduct precise marketing,and realize the win-win situation of telecom operators' profitability and improve the user experience.This article mainly carries out innovative research from three parts.The first part is based on the data characteristics of mobile Internet access behavior,from the three aspects of access content,access frequency,and access date based on the idea of RFM(Recency Frequency Monetary).Based on the R,M feature construction method,with processing the frequency information by TFIDF(Term Frequency Inverse Document Frequency)and weighting date5 the feature construction method of improving RFM is proposed.The second part is mainly based on the characteristics of continuous variables,a combination of ReliefF and Dbscan Filter-based feature selection algorithm-REDB algorithm.The feature selection algorithm uses the clustering idea to extract the redundant features while extracting the irrelevant features,and has higher computational efficiency and better versatility.The third part of this paper takes the precision marketing of the automotive industry in the telecom industry as an example.After the data is processed according to the user's access behavior data,the fusion model of logistic regression,random forest and neural network is established to predict the user,and model on the experimental data set has good accuracy.