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http://hdl.handle.net/20.500.12207/5916
Title: | Predict churning customers–An explorative study |
Authors: | Ferreira, Tomás Pita, Pedro Brito, Isabel Sofia |
Keywords: | Churning customer Classification algorithms Features |
Issue Date: | Jun-2022 |
Publisher: | IEEE Xplorer |
Citation: | T. Ferreira, P. Pita and I. S. Brito, "Predict Churning Customers – An Explorative Study," 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), Madrid, Spain, 2022, pp. 1-4, https://doi.org/10.23919/CISTI54924.2022.9820260 |
Abstract: | Some banks and business managers are facing the problem of customer credit card attrition. Therefore, it was necessary to identify new strategies for banks and business managers to keep their customers satisfied. In this paper, we analyze the data from a fictitious data source available on Kaggle, to find out the reason behind this and to predict customers who are likely to drop off so the banks and business managers can proactively provide them better services. To accomplish this, we used eight classification algorithms and the obtained results from some algorithms are very promising. |
Peer reviewed: | yes |
URI: | https://hdl.handle.net/20.500.12207/5916 |
metadata.dc.identifier.doi: | https://doi.org/10.23919/CISTI54924.2022.9820260 |
ISBN: | 978-989-33-3436-2 |
Appears in Collections: | D-ENG - Artigos em revistas indexadas à WoS/Scopus |
Files in This Item:
File | Description | Size | Format | |
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Predict Churning Customers_PDFA.pdf | 363.21 kB | Adobe PDF | View/Open Request a copy |
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