Research Trends in Customer Churn Prediction: A Data Mining Approach-Reference-Cited by-同舟云学术

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Research Trends in Customer Churn Prediction: A Data Mining Approach

Published:2021 Issue: Volume: Page:227-237
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Tianyuan Zhang,Moro Sérgio

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-72657-7_22

Reference91 articles.

1. Abbasimehr, H.: A neuro-fuzzy classifier for Customer churn prediction. Int. J. Comput. Appl. 19(8), 35–41 (2011)

2. Ahmad, A.K., Aljoumaa, K.: Customer churn prediction in telecom using machine learning in big data platform. J. Big Data 6, 28 (2019)

3. Amin, A., et al.: Customer churn prediction in telecommunication industry: with and without counter-example. In: Mexican International Conference Artificial Intelligence, pp. 206–218 (2014)

4. Amin, A., et al.: A prudent based approach for customer churn prediction. In: International Conference: Beyond Databases, Architectures and Structures, pp. 320–332 (2015)

5. Amin, A., et al.: Comparing oversampling techniques to handle the class imbalance problem: a customer churn prediction case study. IEEE Access 4, 7940–7957 (2016)

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1. A Model-Agnostic Interpretability Approach to Predicting Customer Churn in the Telecommunications Industry;Infolitika Journal of Data Science;2024-05-27

2. Customer Churn Prediction Using Machine Learning Algorithms;International Journal of Scientific Research in Science and Technology;2024-04-03

3. Leveraging Data Analytics in Azure for Effective Churn Management;2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE);2024-02-22

4. Customer Churn Prediction in Telecommunication and Banking using Machine Learning: A Systematic Literature Review;2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS);2024-01-28

5. Predictive Analytics in Marketing Using Artificial Intelligence;Lecture Notes in Networks and Systems;2024

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