Abstract
Customer churn is a significant issue and one of the most pressing challenges for large businesses. Companies are working to create methods to predict prospective client churn because it has such a direct impact on their revenues, particularly in the banking and finance industry. As a result, identifying factors that contribute to customer churn is critical in order to take the required steps to reduce churn. It is usually preferable for a bank (or any other firm or organization) to keep its existing customers rather than strive to recruit new ones. In our proposed work, different standard classification algorithms and boosting methods were compared. To improve the efficiency of these models, grid search and genetic algorithm were utilized for hyperparameter tuning. After analysing the outcomes of all algorithms, XGBoost was found to be the best algorithm for the used data set because it produced the most accurate findings. We attained a train accuracy of 95.28% and a test accuracy of 95.25%.
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Kinge, A., Oswal, Y., Khangal, T., Kulkarni, N., Jha, P. (2022). Comparative Study on Different Classification Models for Customer Churn Problem. In: Agrawal, S., Gupta, K.K., Chan, J.H., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-9650-3_12
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