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English Information

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Authors
# Name
1 William beckhauser(beckhauserwilliam@gmail.com)
2 Renato Fileto(r.fileto@ufsc.br)

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Reference
# Reference
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22 Perisˇic ́, A., Pahor, M.: Clustering mixed-type player behavior data for churn prediction in mobile games. Central European journal of operations research 31(1), 165–190 (2023)
23 Perisˇic ́, A., Pahor, M.: Rfm-lir feature framework for churn prediction in the mobile games market. IEEE Transactions on Games 14(2), 126–137 (2022). https://doi.org/10.1109/TG.2021.3067114
24 Shobana, J., Gangadhar, C., Arora, R.K., Renjith, P., Bamini, J., devidas Chincholkar, Y.: E-commerce customer churn prevention using machine learning-based business intelligence strategy. Measurement: Sensors 27, 100728 (2023)
25 Sobreiro, P., Martinho, D.D.S., Alonso, J.G., Berrocal, J.: A slr on customer dropout prediction. IEEE access 10, 14529–14547 (2022)
26 Suh, Y.: Machine learning based customer churn prediction in home appliance rental business. Journal of big Data 10(1), 41 (2023)
27 Tran, H., Le, N., Nguyen, V.H.: Customer churn prediction in the banking sector using machine learning- based classification models. Interdisciplinary Journal of Information, Knowledge & Management 18 (2023)
28 Wenger, M.: Strategic Business Models in the Online Food Delivery Industry-Detailed Analysis of the- ” Order and Delivery” Business Model. Master’s thesis, Universidade NOVA de Lisboa (Portugal) (2021)
29 Zhong, J., Li, W.: Predicting customer churn in the telecommunication industry by analyzing phone call transcripts with convolutional neural networks. In: 3rd Intl. Conf. on Innova- tion in Artificial Intelligence. p. 55–59. ICIAI 2019, ACM, New York, NY, USA (2019). https://doi.org/10.1145/3319921.3319937