Abstract
In various service-based industries such as telecom industry, life insurance, hospitality, banking, and gaming, Churn Prediction plays an important role. Companies are trying to establish means for predicting potential clients to turnover in the telecom sector. Therefore, it is crucial to identify the factors that rising the churn of customers and take the appropriate steps and reduce the churn. Hence the purpose of our research is to establish the model of churn prediction. The cycle where one user leaves one company and enters another is called churn. This paper would explore how to identify customers who could churn, using machine learning techniques to forecast, and helping to represent large datasets in graph form.
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References
C. Wang, R. Li, P. Wang, Z. Chen, Partition cost sensitive CART based on customer value for telecom customer churn prediction, in 2017 36th Chinese Control Conference (CCC), September 2017
N. Lu, H. Lin, J. Lu, G. Zhang, A customer churn prediction in telecom industry using boosting. Customer behaviour in telecommunications. IEEE Trans. Ind. Inform. 10(2) (2014)
P. Li, S. Li, T. Bi, Y. Liu, Telecom customer churn prediction method based on cluster stratified sampling logistic regression. IEEE (2014)
A. Idris, A. Khan, Ensemble based Efficient churn prediction model for telecom, in International Conference on Frontiers of Information Technology (FIT) (2015), pp. 5680–5684
G. Xia, H. Wang, Y. Jiang, Application of customer churn prediction based on weighted selective ensembles, in International Conference on Systems and Informatics (ICSAI 2016), November 2016, pp. 513–519
M. Rohini, P. Devaki, Analysis of customer churn by big data clustering. Int. J. Innovative Res. Computer Commun. Eng. 5(3) (2017)
N, Saini, Churn prediction in telecommunication industry using decision tree. Streamed Info. Ocean. 1(1) (2016)
A.A.Q. Ahmed, D. Maheswari, Churn prediction on huge telecom data using hybrid firefly based classification Churn prediction on huge telecom data. Egypt. Inform. J. 18(3), 215–220 (2017)
M. Akmal, Factors Causing Customer Churn: A Qualitative Explanation of Customer Churns In Pakistan Telecom Industry (2017)
K. Dahiya, S. Bhatia, Customer Churn Analysis in Telecom Industry (IEEE, 2015). 978-1-4673-7231-2/15
Praveen et al., Churn prediction in telecom industry using R. IJETR. 3(5) (2015). ISSN 2321-0869
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Mukhopadhyay, D., Malusare, A., Nandanwar, A., Sakshi, S. (2021). An Approach to Mitigate the Risk of Customer Churn Using Machine Learning Algorithms. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_13
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DOI: https://doi.org/10.1007/978-981-15-7106-0_13
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