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              <text>An Approach for Credit Card Churn Prediction Using Gradient Descent</text>
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              <text>Artificial neural network; Banking sector; Churn prediction; Feature engineering; Stochastic gradient descent</text>
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              <text>A very important asset for any company in the business sectors such as banking, marketing, etc. are its customers. For them to stay in the game, they have to satisfy their customers. Customer retention plays an important role in attracting and retaining the customers. Customer retention means to keep the customer satisfied so that they do not stop using their service/product in the domain of banking; the banks provide various kinds of services to the customers especially in the electronic banking sector. For this study, we have selected the service of credit card. For a bank to give a loan or amount on credit basis, the e-bank should make sure if its customers are eligible and can repay their money. The purpose of this project is to implement a neural network model to classify the churners and non-churners.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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              <text>Saanchay P.M.; Thomas K.T.</text>
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              <text>Smart Innovation, Systems and Technologies, Vol-251, pp. 689-697.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-16-3945-6_68" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-16-3945-6_68&lt;/a&gt;
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              <text>ISSN: 21903018; ISBN: 978-981163944-9</text>
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              <text>Saanchay P.M., Christ (Deemed To Be University), Lavasa, India; Thomas K.T., Christ (Deemed To Be University), Lavasa, India</text>
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