Telecommunication customer churn prediction based on multiple machine learning algorithms

Zhiyuan Gao; Yujun Shu; Xiangrui Zhang; Xinge Zhang

doi:10.1117/12.2656703

20 October 2022 Telecommunication customer churn prediction based on multiple machine learning algorithms

Zhiyuan Gao, Yujun Shu, Xiangrui Zhang, Xinge Zhang

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124514U (2022) https://doi.org/10.1117/12.2656703
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

Due to the large number of factors in churn prediction, it is challenging to choose and provide a model to do churn prediction and find the most important factors to deal with. In this paper, multiple machine learning methods will be combined to be compared to strengthen the validity of prediction. More specifically, there are four ways listed in this paper, which are Logistic Regression, Decision Tree, Light Gradient Boosting Machine, Support Vector Machine, K-means, and Random forest. The data selected from Kaggle will be the datasets in this paper to be analyzed. The data is preprocessed by turning figures into numbers so that it can be analyzed by mathematics way like polynomial or linear process. In addition, the important factors in telecom churn are also included through machine learning. The score of different kinds of machine learning will be given to choosing the best model. Experimental results indicate that Support Vector Machine with linear kernel is the best model, and the most important factors are contract and tenure.

Citation Download Citation

Zhiyuan Gao, Yujun Shu, Xiangrui Zhang, and Xinge Zhang "Telecommunication customer churn prediction based on multiple machine learning algorithms", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124514U (20 October 2022); https://doi.org/10.1117/12.2656703

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