The Mobile Customers Occupational Recognition Naive Bayes Algorithm-based Integration And Debugging

With the rapid development of mobile communication, Internet business. Mobile phone has moved from a luxury into a daily necessities of life, is an indispensable part in our daily life. People whenever and wherever possible use of mobile phone, texting, surfing the Internet, and the user’s behavior as well as its basic personal information in the operator of record. How to through traffic fee, phone calls, SMS, online number four weighted factors to characterize user type of occupation. For integral operators in the industry, is an extremely important research topic and valuable.It is especially important to provide specific services and the appropriate amount of a specific group of people. Because of the different occupation have different tendency of consumption and consumption capacity, selection of fees and charges business is very different. So by the users of the past data, summed up the characteristics of different occupation of users, for users to predict unknown occupation, on market and business process is of great help, can save a lot of unnecessary promotion cost, improve promotion conversion rate.We use the simple Bias model to classify the user occupation information integrity. In the new workplace as an example, according to depict the workplace four relationship among factors and workplace occupation. To predict the two occupation information workplace is engaged in the real estate industry and other business services respectively. We will table mobile phone information workplace separately listed, to determine the mobile phone market a new terminal information generally range in price according to the related indicators of new workplace information integrity of the question of. When the new workplace and clustering center distance is small, the new workplace belong to this category. When the distance is large, the new workplace does not belong to this category.