Churn Prediction Using Machine Learning and Recommendations Plans for Telecoms

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"Churn Prediction Using Machine Learning and Recommendations Plans for Telecoms"
written by Khulood Ebrah, Selma Elnasir,
published by Journal of Computer and Communications, Vol.7 No.11, 2019

has been cited by the following article(s):

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[1]	Smart Customer Churn Management System Using Machine Learning Procedia Computer Science, 2024 DOI:10.1016/j.procs.2024.05.139

[2]	An Efficient Churn Prediction model using ML supervised and semi supervised Learning Techniques 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), 2024 DOI:10.1109/I2CT61223.2024.10543994

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[5]	Power Engineering and Intelligent Systems Lecture Notes in Electrical Engineering, 2024 DOI:10.1007/978-981-99-7383-5_5

[6]	A classification application for using learning methods in bank costumer's portfolio churn Journal of Forecasting, 2024 DOI:10.1002/for.3038

[7]	Machine Learning Predictive Models Analysis on Telecommunications Service Churn Rate Green Intelligent Systems and Applications, 2023 DOI:10.53623/gisa.v3i1.249

[8]	Analysis of Telecom Churn using Machine Learning Techniques 2022 International Conference on Artificial Intelligence and Data Engineering (AIDE), 2022 DOI:10.1109/AIDE57180.2022.10060222

[9]	Impact of Hyperparameters on Deep Learning Model for Customer Churn Prediction in Telecommunication Sector Mathematical Problems in Engineering, 2022 DOI:10.1155/2022/4720539

[10]	Churn prediction in telecommunication industry using kernel Support Vector Machines PLOS ONE, 2022 DOI:10.1371/journal.pone.0267935

[11]	Research on Network Video Customer Churn Based on Cox Model Operations Research and Fuzziology, 2022 DOI:10.12677/ORF.2022.124146

[12]	Analysis of Telecom Churn using Machine Learning Techniques 2022 International Conference on Artificial Intelligence and Data Engineering (AIDE), 2022 DOI:10.1109/AIDE57180.2022.10060222

[13]	Computer Networks and Inventive Communication Technologies Lecture Notes on Data Engineering and Communications Technologies, 2021 DOI:10.1007/978-981-15-9647-6_73

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