  Mobile Internet user credit assessment is an important way for communication
operators to establish decisions and formulate measures, and it is also a
guarantee for operators to obtain expected benefits. However, credit evaluation
methods have long been monopolized by financial industries such as banks and
credit. As supporters and providers of platform network technology and network
resources, communication operators are also builders and maintainers of
communication networks. Internet data improves the user's credit evaluation
strategy. This paper uses the massive data provided by communication operators
to carry out research on the operator's user credit evaluation model based on
the fusion LightGBM algorithm. First, for the massive data related to user
evaluation provided by operators, key features are extracted by data
preprocessing and feature engineering methods, and a multi-dimensional feature
set with statistical significance is constructed; then, linear regression,
decision tree, LightGBM, and other machine learning algorithms build multiple
basic models to find the best basic model; finally, integrates Averaging,
Voting, Blending, Stacking and other integrated algorithms to refine multiple
fusion models, and finally establish the most suitable fusion model for
operator user evaluation.
