@Article{jai.2021.026851,
AUTHOR = {Hao Chen, Qian Tang, Yifei Wei, Mei Song},
TITLE = {Churn Prediction Model of Telecom Users Based on XGBoost},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {3},
YEAR = {2021},
NUMBER = {3},
PAGES = {115--121},
URL = {http://www.techscience.com/jai/v3n3/46668},
ISSN = {2579-003X},
ABSTRACT = {As the cost of accessing a telecom operator’s network continues to 
decrease, user churn after arrears occurred repeatedly, which has brought huge 
economic losses to operators and reminded them that it is significant to identify 
users who are likely to churn in advance. Machine learning can form a series of 
judgment rules by summarizing a large amount of data, and telecom user data 
naturally has the advantage of user scale, which can provide data support for
learning algorithms. XGBoost is an improved gradient boosting algorithm, and in
this paper, we explore how to use the algorithm to train an efficient model and use 
this model one month in advance to predict whether users will churn. Our work is 
mainly divided into two aspects: (1) By completing data exploration, feature 
engineering and data preprocessing, we obtained a data set that can be used to train 
a prediction model and features that can effectively predict user churn. And using 
these features and data sets, two prediction models were trained based on Random 
Forest and XGBoost. (2) According to the business needs of telecom operators, 
we continuously evaluated and optimized these models. And by comparing the test 
results of the two models, we proved that the XGBoost model performs better for 
the precision and recall of user churn.},
DOI = {10.32604/jai.2021.026851}
}