Prediction of Gold Price Movement Using Discretization Procedure

  • Debanjan Banerjee
  • Arijit GhosalEmail author
  • Imon Mukherjee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)


Accurate prediction of commodity prices by using machine learning techniques is considered as a significant challenge by the researchers and investors alike. The main objective of the proposed work is to highlight that discretized features provide more accuracy compared to the continuous features for predicting the gold price movement in either positive or negative direction. This work utilizes three unique techniques for measuring performance of the discretization procedure. These techniques are “percentage of accuracy”, “receiver operating characteristics or ROC” and “the area under the ROC curve or AUC.”


Feature discretization Logistic regression Random forest Machine learning 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Debanjan Banerjee
    • 1
  • Arijit Ghosal
    • 2
    Email author
  • Imon Mukherjee
    • 3
  1. 1.Department for the Management Information SystemsSarva Siksha Mission KolkataKolkataIndia
  2. 2.Department of Information TechnologySt. Thomas’ College of Engineering & Technology, KolkataKolkataIndia
  3. 3.Department of Computer Science and EngineeringIndian Institute of Information Technology, KalyaniKalyaniIndia

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