Stock Market Analysis and Prediction using Machine Learning
Amaan Aijaz1, Komal Rastogi2, TK SivaKumar3
1Amaan Aijaz, Department of Computing Technologies, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Komal Rastogi, Department of Computing Technologies, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
3Dr. TK Sivakumar, Department of Computing Technologies, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 05 June 2022 | Revised Manuscript received on 17 June 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 54-60 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B70830711222 | DOI: 10.35940/ijrte.B7083.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The stock market is a field which has spurred the interest of not only researchers, but, ordinary people as well over the years. It has encouraged scientists to develop better predictive models. The stock market is not as simple as it might seem initially. It is a transformative, non-straight dynamical and complex system. The main objectives of the project are to analyze the advantages and disadvantages of using Machine Learning techniques for the purpose of predicting values and comparing different algorithms, along with integrating the best model in a Web Application which will help users to be able to improve their decision making strategies by trying to give them an insight on what could possibly happen in the short term of things. Machine learning can be described as a technology that enables a system to learn on its own through real-world interactions with the help of data and hence be able to recognize similar interactions which the machine initially learned. Machine Learning over the past decade has quite subtly become to become a part of one’s everyday life due to its key role in numerous vital applications.
Keywords: ARIMA, Linear Regression, Machine Learning, Stock Market, Support Vector Machine, Web Application.
Scope of the Article: Machine Learning