Using Machine Learning Algorithms to Analyze Customer Churn with Commissions Rate for Stocks in Brokerage Firms and Banks-Reference-Cited by-同舟云学术

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

Published:2024-03-24 Issue:1 Volume:13 Page:335-345
ISSN:2147-3129
Container-title:Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
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Author:

Kaya Hakan¹^ORCID

Affiliation:

1. İstanbul Okan Üniversitesi

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.

Publisher

Bitlis Eren Universitesi Fen Bilimleri Dergisi

Reference21 articles.

1. [1] M. A. H. Farquad, V. Ravi, and S. B. Raju, “Data mining using rules extracted from SVM: An application to churn prediction in bank credit cards,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5908 LNAI, pp. 390–397, 2009.

2. [2] N. A. Akbar, A. Sunyoto, M. Rudyanto Arief, and W. Caesarendra, “Improvement of decision tree classifier accuracy for healthcare insurance fraud prediction by using Extreme Gradient Boosting algorithm,” in Proceedings-2nd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2020, pp. 110–114, 2020, DOI: 10.1109/ICIMCIS51567.2020.9354286.

3. [3] S. M. Fati, A. Muneer, N. A. Akbar, and S. M. Taib, “A continuous cuffless blood pressure estimation using tree-based pipeline optimization tool,” Symmetry, vol. 13, no. 4, 2021, DOI: 10.3390/sym13040686.

4. [4] A. Muneer and S. M. Fati, “A comparative analysis of machine learning techniques for cyberbullying detection on twitter,” Future Internet, vol. 12, no. 11, pp. 1–21, 2020, DOI: 10.3390/fi12110187.

5. [5] M. Al-Ghobari, A. Muneer, and S. M. Fati, “Location-aware personalized traveler recommender system (lapta) using collaborative filtering knn,” Computers, Materials and Continua, vol. 69, no. 2, pp. 1553–1570, 2021, DOI: 10.32604/cmc.2021.016348.