Using Machine Learning Algorithms to Analyze Customer Churn with Commissions Rate for Stocks in Brokerage Firms and Banks

Abstract

Stock commission rates of banks and brokerage firms are a critical factor for investors. These rates affect the cost of stock investments. In this article, we will discuss the importance of stock commission rates of brokerage firms and banks and how they are determined. To enhance a slightly different approach to customer churn management, data set derived from a banks and brokorage firm has been analyzed. The data set which contains 7816 entries and 14 columns features has been derived from a publicly open-access database and reflects transactions of the firm. Decision Tree, Random Forest, K-NN, Gaussion NB and XGBoost algorithms have been used as analyzing methods and performance of the analysis has been evaluated via three accuracy measures. Two approaches are included for model creation. According to the first analysis results, the Gaussion NB, for second approach the K-NN algorithms gave the best result.

Keywords

• Customer churn
• stock commission rates
• brokerage firms and banks
• machine learning

References

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