An Analytical Approach for Stock Market Forecasting Based on Machine Learning

Authors

  • Swati D. Killekar  Computer Science and Engineering Department, KLS Gogte Institute of Technology, Belagavi, Belgaum, Karnataka, India
  • Dr. Sanjeev S. Sannakki  Professor Computer Science and Engineering Department, KLS Gogte Institute of Technology, Belagavi, Belgaum, Karnataka, India
  • Prashant Y. Niranjan  Assistant Professor Computer Science and Engineering Department, KLS Gogte Institute of Technology, Belagavi, Belgaum, Karnataka, India
  • Girish R. Deshpande  Assistant Professor Computer Science and Engineering Department, KLS Gogte Institute of Technology, Belagavi, Belgaum, Karnataka, India

Keywords:

Machine Learning, Stock Market Predication, Classification.

Abstract

Stock Market act as a mechanism for organizations to mean their capitals by introducing their organization shares to market and furthermore ends up being a helpful stage for investors to procure past the edge of interest rates of offered by banks. Main objective of Stock Market Prediction (SMP) is to forecast expected values of companies financial stocks. Recently SMP utilizes machine learning (ML) for prediction in light of estimations of current stock market for that they train their previous values. ML present numerous models for making prediction easier and authenticated. This paper reviews on different available SMP techniques mainly utilizing Regression and LSTM based ML for making prediction of stock values. Main aspects well thought-out for prediction are close, open, high, low and volume.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-04-30

Issue

Volume 7, Issue 2, March-April-2020

Section

Research Articles

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Copyright (c) IJSRSET

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Swati D. Killekar, Dr. Sanjeev S. Sannakki, Prashant Y. Niranjan, Girish R. Deshpande, " An Analytical Approach for Stock Market Forecasting Based on Machine Learning, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 2, pp.414-420, March-April-2020.