Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)

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Risk Decision and Predicting of Customer Churn Based on Principal Component Analysis

Authors
Shiyu Cui1, *, Penghan Lai2, Yuwei Deng3, Xiaojiang Zheng4
1Information School, Yunnan University of Finance and Economics, Kunming, China
2New College, University of Toronto St George Campus, Toronto, Canada
3The USC Dornsife College of Letters Arts and Sciences, University of Southern California, California, USA
4School of Tourism Sciences, Beijing International Studies University, Beijing, China
*Corresponding author. Email: 201905001194@stu.ynufe.edu.cn
Corresponding Author
Shiyu Cui
Available Online 10 November 2022.
DOI
10.2991/978-94-6463-005-3_71How to use a DOI?
Keywords
Customer churn; Exploratory data analysis; feature engineering; feature selection; comparison of models
Abstract

This study will establish a predictive analytics model that uses churn prediction models to anticipate customer churn by evaluating their risk of churn. These models are successful in focusing customer retention marketing activities on the fraction of the customer base that is most prone to churn because they generate a short, prioritized list of probable defectors. We will start with exploratory data analysis in this paper. We will get a quick summary of the data using this way. The data is then further analyzed using feature engineering and feature selection. Finally, the target variables will be visualized using Principal Component Analysis (PCA). Using the Kolmogorov-Smirnov (KS) score test, features are picked after calculating the churn detection rate and comparing it to the average churn rate. The best part is then identified using Cross-Validated Recursive Feature Elimination. The accuracy, auc, and ks of each model were assessed after training, and the Gaussian naive Bayes, Logistic regression, and Neural network were finally picked by comparison. Model stacking is a technique for comparing model performance and ultimately deciding which model to utilize.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)
Series
Atlantis Highlights in Engineering
Publication Date
10 November 2022
ISBN
10.2991/978-94-6463-005-3_71
ISSN
2589-4943
DOI
10.2991/978-94-6463-005-3_71How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shiyu Cui
AU  - Penghan Lai
AU  - Yuwei Deng
AU  - Xiaojiang Zheng
PY  - 2022
DA  - 2022/11/10
TI  - Risk Decision and Predicting of Customer Churn Based on Principal Component Analysis
BT  - Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)
PB  - Atlantis Press
SP  - 693
EP  - 701
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-005-3_71
DO  - 10.2991/978-94-6463-005-3_71
ID  - Cui2022
ER  -