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28 March 2023 Banks customer churn analysis and prediction using random forest
Haipeng Zhao
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Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256644 (2023) https://doi.org/10.1117/12.2667898
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China
Abstract
Banks want to predict whether their customers will leave the banks in the future, which is called bank customer churn prediction because they want to survive in the market and maximize their profits. To accomplish that, previous studies tried many methods but none of them works very well. However, with the development of the internet, more and more data become available for banks to collect and use. To use these data efficiently, banks could use some machine learning algorithms to predict customer churn. Among many machine learning algorithms, random forest is one of the best and it was used in this work. The random forest model adopted 100 decision trees. It predicts the customer churn and computes the feature importance for all the variables included in the dataset. As a result, it could achieve an accuracy score of 0.875, which makes this model a promising method for predicting customer churn.
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Haipeng Zhao "Banks customer churn analysis and prediction using random forest", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256644 (28 March 2023); https://doi.org/10.1117/12.2667898
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KEYWORDS
Random forests
Data modeling
Decision trees
Machine learning
Visualization
Visual process modeling
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