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A Comparative Study to the Bank Market Prediction

  • Soumadip Ghosh
  • Arnab Hazra
  • Bikramjit ChoudhuryEmail author
  • Payel Biswas
  • Amitava Nag
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10934)

Abstract

Bank market prediction is an important area of data mining research. In the present scenario, we are given with huge amounts of data from different banking organizations, but we are yet to achieve meaningful information from them. Data mining procedures will help us extracting interesting knowledge from this dataset to help in bank marketing campaigns. This work introduces analysis and applications of the most important techniques in data mining. In our work, we use Multilayer Perception Neural Network (MLPNN), Decision Tree (DT) and Support Vector Machine (SVM). The objective is to examine the performance of MLPNN, DT and SVM techniques on a real-world data of bank deposit subscription. The experimental results demonstrate, with higher accuracies, the success of these models in predicting the best campaign contact with the clients for subscribing deposit. The performance is evaluated by some well-known statistical measures such as accuracy, Root-mean-square error, Kappa statistic, TP-Rate, FP-Rate, Precision, Recall, F-Measure and ROC Area values.

Keywords

Data mining Classification Multilayer Perception Neural Network Decision tree Support Vector Machine 

References

  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Burlington (2000)zbMATHGoogle Scholar
  2. 2.
    Pujari, A.K.: Data Mining Techniques, 1st edn. Universities Press (India) Private Limited, Hyderabad (2001)Google Scholar
  3. 3.
    Quinlan, J.R.: Simplifying decision trees. Int. J. Man Mach. Stud. 27(3), 221–234 (1987)CrossRefGoogle Scholar
  4. 4.
    Breiman, L., Freidman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees Belmont. Wadsworth, Belmont (1984)Google Scholar
  5. 5.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  6. 6.
    Osuna, E., Freund, R., Girosi, F.: An improved training algorithm for support vector machines. In: IEEE NNSP 1997, Amelia Island, FL, pp. 24–26, September 1997Google Scholar
  7. 7.
    Moro, S., Laureano, R., Cortez, P.: Using data mining for bank direct marketing: an application of the CRISP-DM methodology. In: Novais, P., et al. (Eds.), Proceedings of the European Simulation and Modelling Conference – ESM 2011, Guimarães, Portugal, pp. 117–121, October 2011Google Scholar
  8. 8.
    Hu, X.: A data mining approach for retailing bank customer attrition analysis. Appl. Intell. 22(1), 47–60 (2005)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Ling, C.X., Li, C.: Data mining for direct marketing: problems and solutions. In: Proceedings of the 4th KDD Conference, pp. 73–79. AAAI Press (1998)Google Scholar
  10. 10.
    Turban, E., Sharda, R., Delen, D.: Decision Support and Business Intelligence Systems, 9th edn. Prentice Hall Press, Upper Saddle River (2010)Google Scholar
  11. 11.
    Witten, I., Frank, E.: Data Mining – Practical Machine Learning Tools and Techniques, 2nd edn. Elsevier, New York (2005)zbMATHGoogle Scholar
  12. 12.
    Chitra, K., Subashini, B.: Data mining techniques and its applications in banking sector. Int. J. Emerg. Technol. Adv. Eng. 3(8), 219–226 (2013). ISSN 2250-2459, ISO 9001:2008 Certified JournalGoogle Scholar
  13. 13.
    Rafiqul Islam, M., Ahsan Habib, M.: A data mining approach to predict prospective business sectors for lending in retail banking using decision tree. Int. J. Data Min. Knowl. Manag. Process (IJDKP) 5(2), 13–22 (2015)CrossRefGoogle Scholar
  14. 14.
    Armstrong, J.S., Collopy, F.: Error measures for generalizing about forecasting methods: empirical comparisons. Int. J. Forecast. 8, 69–80 (1992)CrossRefGoogle Scholar
  15. 15.
    Carletta, J.: Assessing agreement on classification tasks: the Kappa statistic. Comput. Linguist. 22(2), 249–254 (1996). MIT Press, CambridgeGoogle Scholar
  16. 16.
    Stehman, S.V.: Selecting and interpreting measures of thematic classification accuracy. Rem. Sens. Environ. 62(1), 77–89 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Soumadip Ghosh
    • 1
  • Arnab Hazra
    • 2
  • Bikramjit Choudhury
    • 3
    Email author
  • Payel Biswas
    • 4
  • Amitava Nag
    • 3
  1. 1.Department of ITAcademy of Technology, AedconagarHooghlyIndia
  2. 2.Department of CSEAcademy of Technology, AedconagarHooghlyIndia
  3. 3.Department of ITCentral Institute of TechnologyKokrajhar, BTADIndia
  4. 4.Department of Computer ScienceJogesh Chandra Chaudhuri CollegeKolkataIndia

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