Systematic Review on Churn Prediction Systems in Telecommunications-Reference-Cited by-同舟云学术

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Systematic Review on Churn Prediction Systems in Telecommunications

Published:2022 Issue: Volume: Page:983-995
ISSN:1876-1100
Container-title:Proceedings of Third International Conference on Communication, Computing and Electronics Systems
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Author:

Naidu Gireen,Zuva Tranos,Sibanda Elias Mmbongeni

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-8862-1_64

Reference27 articles.

1. Umayaparvathi V (2012) Applications of data mining techniques in telecom churn prediction 42(20):5–9

2. Jain H, Khunteta A, Srivastava S (2021) Telecom churn prediction and used techniques, datasets and performance measures: a review. Telecommun Syst 76(4):613–630. https://doi.org/10.1007/s11235-020-00727-0

3. Parmar P (2021) Telecom Churn Prediction Model using XgBoost Classifier and Logistic Regression Algorithm May:1100–1105

4. Mishra A, Reddy US (2018) A novel approach for churn prediction using deep learning. 2017 IEEE International conference on computational intelligence and computing research. ICCIC 2017, Jan 2019, pp. 1–4. https://doi.org/10.1109/ICCIC.2017.8524551

5. Senthilnayaki B, Swetha M, Nivedha D (2021) Customer Churn Prediction. Iarjset 8(6):527–531. https://doi.org/10.17148/iarjset.2021.8692

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3. Application of Machine Learning in Predicting Customer Satisfaction of Telecom Service Providers;2022 4th International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM);2022-11-22

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