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)


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.


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


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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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