Study of machine learning methods for customer churn prediction in telecommunication company

被引：4

作者：

Sniegula, Anna ^{[1
]}

Poniszewska-Maranda, Aneta ^{[2
]}

Popovic, Milan ^{[1
]}

机构：

[1] Univ Lodz, Dept Informat Econ, Lodz, Poland

[2] Lodz Univ Technol, Inst Informat Technol, Lodz, Poland

来源：

IIWAS2019: THE 21ST INTERNATIONAL CONFERENCE ON INFORMATION INTEGRATION AND WEB-BASED APPLICATIONS & SERVICES | 2019年

关键词：

machine learning; predictive analytics; neural networks; decision trees; k-means clustering; client churn prediction;

D O I：

10.1145/3366030.3366109

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The paper presents the results of investigation which machine learning techniques are most suited for customer churn prediction. Different approaches were compared, starting from the simple K-means method, through decision trees, ending with the artificial neural network. The authors trained the models with each method and predicted whether a customer is going to leave the current telecommunication company.

引用

页码：640 / 644

页数：5

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