Open Access
| Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01034 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/e3sconf/202343001034 | |
| Published online | 06 October 2023 | |
- Median Customer Churn Rates by Industry 2022, https://customergauge.com/blog/ average-churn-rate-by-industry [Google Scholar]
- Amrita Doshi, Intl. J. Adva. Engg. Manag 3, (2021) [Google Scholar]
- Xinyu Miao, Haoran Wang, Customer Churn Prediction on Credit Card Services using Random Forest Method, in the Proceedings of 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) 648, (2022) [Google Scholar]
- Al Najjar, Dana, Al-Rousan, Hazem, J. Theor. Appl. Elect. Comm. Res 17, (2022) [Google Scholar]
- H. Guliyev, F. Y. Tatoglu, J. Appl. Micr. Econo 1, (2021) [Google Scholar]
- K. Demirberk, Comm. Facul. Sci. Univ. Ankara 70, (2021) [Google Scholar]
- R. Rajamohamed, Manokaran, Clust. Computing 21, (2018) [Google Scholar]
- S. Kumar, V. Ravi, V. Siddeshwar, One-class support vector machine based under sampling: Application to churn prediction and insurance fraud detection, in the Proceedings of 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (2015) [Google Scholar]
- Muneer, A. A. Rao, Alghamdi, Amal, M. Taib, Shakirah, Almaghthwi, Ahmed, Ghaleb, Ebrahim, Indon. J. Elect. Engg. Comp. Scie 26 (2022) [Google Scholar]
- Ning Wang, Dong-xiao Niu, Credit card customer churn prediction based on the RST and LS-SVM, in the Proceedings of the 6th International Conference on Service Systems and Service Management, Xiamen, China (2009) [Google Scholar]
- Nie, Guangli, Rowe, Wei, Zhang, Lingling, Tian, Yingjie, Shi, Yong, Exp. Syst. Appl. 38 (2011) [Google Scholar]
- Kumar, Dudyala, Ravi, Vadlamani, Intl. J. Data Anal. Techn. Strat 1 (2008) [Google Scholar]
- G. Wang, L. Liu, Y. Peng, G. Nie, G. Kou, Y. Shi, Predicting Credit Card Holder Churn in Banks of China using Data Mining and MCDM, in the Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Canada (2010) [Google Scholar]
- Lemos, Renato, Silva, Thiago, Tabak, Benjamin, Neur. Comp. Appl 34 (2022) [Google Scholar]
- Benlan, Shi, Yong, Wan, Qian, Zhao, Proce. Comp. Scie 31 (2014) [Google Scholar]
- M. Rahman, V. Kumar, Machine Learning Based Customer Churn Prediction In Banking, in the Proceedings of the 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India (2020) [Google Scholar]
- R. P. Ram Kumar, P. Sanjeeva, S. F. Lazarus, D. V. Krishna, Intl. J. Inno. Tech. Explor. Engg 8, 11S2 (2019) [Google Scholar]
- R. Boorugu, G. Ramesh, A Survey on NLP based Text Summarization for Summarizing Product Reviews, in Proceedings of the 2nd International Conference on Inventive Research in Computing Applications (ICIRCA2020), 9183355 (2020) [Google Scholar]
- M. N. Mohammad, Ch. U. Kumari, A. S. D. Murthy, B. O. L. Jagan, K. Saikumar, Mater. Today Proc 45 (2021) [Google Scholar]
- M. Thejaswee, V. Srilakshmi, K. Anuradha, G. Karuna, Performance Analysis of Machine Learning Algorithms for Text Classification, in Proceedings of the Advanced Informatics for Computing Research (ICAICR 2020), A. K. Luhach, D. S. Jat, K. H. Bin Ghazali, Gao, P. Lingras, (eds), Comm. Comp. Inform. Sci. Springer, Singapore 1393 (2021) [Google Scholar]
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