Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 13 Sayı: 1, 335 - 345, 24.03.2024
https://doi.org/10.17798/bitlisfen.1408349

Öz

Kaynakça

  • [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] 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] 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] 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] 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.
  • [6] F. Kayaalp, “Review of customer churn analysis studies in telecommunications industry,” Karaelmas Science & Engineering Journal, vol. 7, no. 2, pp. 696-705 2017.
  • [7] B.Prabadevi, R. Shalini, and B. R. Kavitha, “Customer churning analysis using machine learning algorithms,” International Journal of Intelligent Networks, vol. 4, pp. 145-154, 2023, DOI: https://doi.org/10.1016/j.ijin.2023.05.005.
  • [8] A. Muneer, R. F. Ali, A. Alghamdi, S. M Taib, A. Almaghthawi, and E. A Ghaleb, “Predicting customers churning in banking industry: A machine learning approach,” Indones. J. Electr. Eng. Comput. Sci, vol.26, no.1, pp. 539-549, 2022, DOI: http://doi.org/10.11591/ijeecs.v26.i1.
  • [9] J.Britto, R.Gobinath, “A detailed review for marketing decision making support system ın a customer churn prediction”, Int. J. Sci. Technol. Res,vol. 9, no. 4, pp. 3698-3702, 2020.
  • [10] H. Guliyev, T F. Y.atoğlu, “Customer churn analysis in banking sector: Evidence from explainable machine learning models,” Journal of Applied Microeconometrics, vol. 1, no. 2, pp. 85-99, 2021, DOI: 10.53753/jame.1.2.03.
  • [11] H. Tran, N. Le, and V. H.Nguyen, “Customer churn prediction in the banking sector using machine learning-based classification models,” Interdisciplinary Journal of Information, Knowledge & Management, vol. 18, pp. 87-105, 2023, DOI: https://doi.org/10.28945/5086.
  • [12] O. Kaynar, M. F. Tuna, Y. Görmez, , and M. A Deveci, “Makine öğrenmesi yöntemleriyle müşteri kaybı analizi,” Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, vol. 18, no. 1, pp. 1-14, 2017.
  • [13] R. A. de Lima Lemos, T. C. Silva, and B. M. Tabak, “Propension to customer churn in a financial institution: a machine learning approach,” Neural Comput. Appl., vol. 34, no. 14, pp. 11751–11768, 2022, DOI: https://doi.org/10.1007/s00521-022-07067-x.
  • [14] Y. Suh, “Machine learning based customer churn prediction in home appliance rental business,” J. Big Data, vol. 10, no. 1, 2023., DOI: https://doi.org/10.1186/s40537-023-00721-8.
  • [15] S. Naseer, S. M. Fati, A. Muneer, and R. F. Ali, “IAceS-deep: Sequence-based identification of acetyl Serine sites in proteins using PseAAC and deep neural representations,” IEEE Access, vol. 10, pp. 12953–12965, 2022, DOI: https://doi.org/10.1109/access.2022.3144226
  • [16] A. Muneer and S. M. Fati, “Efficient and automated herbs classification approach based on shape and texture features using deep learning,” IEEE Access, vol. 8, pp. 196747–196764, 2020, DOI: https://doi.org/10.1109/access.2020.3034033
  • [17] T.Chen, C. Guestrin, Xgboost: Reliable large-scale tree boosting system. In Proceedings of the 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA (pp. 13-17), 2015.
  • [18] V.G. Costa, C.E Pedreira,. “Recent advances in decision trees: an updated survey,” Artif Intell Rev 56, pp. 4765–4800, 2023. DOI: https://doi.org/10.1007/s10462-022-10275-5
  • [19] L. Breiman, Random Forests. Machine Learning, vol. 45, no.1 pp. 5–32, 2001. DOI: https://doi.org/10.1023/A:1010933404324
  • [20] S. Kiliçarslan and E. Dönmez, “Improved multi-layer hybrid adaptive particle swarm optimization based artificial bee colony for optimizing feature selection and classification of microarray data,” Multimed. Tools Appl., 2023, DOI: https://doi.org/10.1007/s11042-023-17234-4
  • [21] N.Sahani, R. Zhu, J. H.Cho, and C. C Liu, “Machine Learning-based Intrusion Detection for Smart Grid Computing: A Survey,” ACM Transactions on Cyber-Physical Systems, vol. 7, no. 2, pp. 1-31, 2023.

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

Yıl 2024, Cilt: 13 Sayı: 1, 335 - 345, 24.03.2024
https://doi.org/10.17798/bitlisfen.1408349

Öz

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.

Kaynakça

  • [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] 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] 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] 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] 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.
  • [6] F. Kayaalp, “Review of customer churn analysis studies in telecommunications industry,” Karaelmas Science & Engineering Journal, vol. 7, no. 2, pp. 696-705 2017.
  • [7] B.Prabadevi, R. Shalini, and B. R. Kavitha, “Customer churning analysis using machine learning algorithms,” International Journal of Intelligent Networks, vol. 4, pp. 145-154, 2023, DOI: https://doi.org/10.1016/j.ijin.2023.05.005.
  • [8] A. Muneer, R. F. Ali, A. Alghamdi, S. M Taib, A. Almaghthawi, and E. A Ghaleb, “Predicting customers churning in banking industry: A machine learning approach,” Indones. J. Electr. Eng. Comput. Sci, vol.26, no.1, pp. 539-549, 2022, DOI: http://doi.org/10.11591/ijeecs.v26.i1.
  • [9] J.Britto, R.Gobinath, “A detailed review for marketing decision making support system ın a customer churn prediction”, Int. J. Sci. Technol. Res,vol. 9, no. 4, pp. 3698-3702, 2020.
  • [10] H. Guliyev, T F. Y.atoğlu, “Customer churn analysis in banking sector: Evidence from explainable machine learning models,” Journal of Applied Microeconometrics, vol. 1, no. 2, pp. 85-99, 2021, DOI: 10.53753/jame.1.2.03.
  • [11] H. Tran, N. Le, and V. H.Nguyen, “Customer churn prediction in the banking sector using machine learning-based classification models,” Interdisciplinary Journal of Information, Knowledge & Management, vol. 18, pp. 87-105, 2023, DOI: https://doi.org/10.28945/5086.
  • [12] O. Kaynar, M. F. Tuna, Y. Görmez, , and M. A Deveci, “Makine öğrenmesi yöntemleriyle müşteri kaybı analizi,” Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi, vol. 18, no. 1, pp. 1-14, 2017.
  • [13] R. A. de Lima Lemos, T. C. Silva, and B. M. Tabak, “Propension to customer churn in a financial institution: a machine learning approach,” Neural Comput. Appl., vol. 34, no. 14, pp. 11751–11768, 2022, DOI: https://doi.org/10.1007/s00521-022-07067-x.
  • [14] Y. Suh, “Machine learning based customer churn prediction in home appliance rental business,” J. Big Data, vol. 10, no. 1, 2023., DOI: https://doi.org/10.1186/s40537-023-00721-8.
  • [15] S. Naseer, S. M. Fati, A. Muneer, and R. F. Ali, “IAceS-deep: Sequence-based identification of acetyl Serine sites in proteins using PseAAC and deep neural representations,” IEEE Access, vol. 10, pp. 12953–12965, 2022, DOI: https://doi.org/10.1109/access.2022.3144226
  • [16] A. Muneer and S. M. Fati, “Efficient and automated herbs classification approach based on shape and texture features using deep learning,” IEEE Access, vol. 8, pp. 196747–196764, 2020, DOI: https://doi.org/10.1109/access.2020.3034033
  • [17] T.Chen, C. Guestrin, Xgboost: Reliable large-scale tree boosting system. In Proceedings of the 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA (pp. 13-17), 2015.
  • [18] V.G. Costa, C.E Pedreira,. “Recent advances in decision trees: an updated survey,” Artif Intell Rev 56, pp. 4765–4800, 2023. DOI: https://doi.org/10.1007/s10462-022-10275-5
  • [19] L. Breiman, Random Forests. Machine Learning, vol. 45, no.1 pp. 5–32, 2001. DOI: https://doi.org/10.1023/A:1010933404324
  • [20] S. Kiliçarslan and E. Dönmez, “Improved multi-layer hybrid adaptive particle swarm optimization based artificial bee colony for optimizing feature selection and classification of microarray data,” Multimed. Tools Appl., 2023, DOI: https://doi.org/10.1007/s11042-023-17234-4
  • [21] N.Sahani, R. Zhu, J. H.Cho, and C. C Liu, “Machine Learning-based Intrusion Detection for Smart Grid Computing: A Survey,” ACM Transactions on Cyber-Physical Systems, vol. 7, no. 2, pp. 1-31, 2023.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Hakan Kaya 0000-0002-0812-4839

Erken Görünüm Tarihi 21 Mart 2024
Yayımlanma Tarihi 24 Mart 2024
Gönderilme Tarihi 22 Aralık 2023
Kabul Tarihi 28 Şubat 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 13 Sayı: 1

Kaynak Göster

IEEE H. Kaya, “Using Machine Learning Algorithms to Analyze Customer Churn with Commissions Rate for Stocks in Brokerage Firms and Banks”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 13, sy. 1, ss. 335–345, 2024, doi: 10.17798/bitlisfen.1408349.



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

Bitlis Eren Üniversitesi Lisansüstü Eğitim Enstitüsü        
Beş Minare Mah. Ahmet Eren Bulvarı, Merkez Kampüs, 13000 BİTLİS        
E-posta: fbe@beu.edu.tr