Enhanced deep feed forward neural network model for the customer attrition analysis in banking sector
Автор: Sandeepkumar hegde, Monica R. Mundada
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 7 vol.11, 2019 года.
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In the present era with the development of the innovation and the globalization, attrition of customer is considered as the vital metric which decides the incomes and gainfulness of the association. It is relevant for all the business spaces regardless of the measure of the business notwithstanding including the new companies. As per the business organization, about 65% of income comes from the customer's client. The objective of the customer attrition analysis is to anticipate the client who is probably going to exit from the present business association. The attrition analysis also termed as churn analysis. The point of this paper is to assemble a precise prescient model using the Enhanced Deep Feed Forward Neural Network Model to predict the customer whittling down in the Banking Domain. The result obtained through the proposed model is compared with various classes of machine learning algorithms Logistic regression, Decision tree, Gaussian Naïve Bayes Algorithm, and Artificial Neural Network. The outcome demonstrates that the proposed Enhanced Deep Feed Forward Neural Network Model performs best in accuracy compared with the existing machine learning model in predicting the customer attrition rate with the Banking Sector.
Enhanced Deep Feed Forward Neural Network, Customer Attrition, Machine Learning, Predictive Model, Banking Sector
Короткий адрес: https://sciup.org/15016605
IDR: 15016605 | DOI: 10.5815/ijisa.2019.07.02
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