Prediction of Stock Prices using Random Forest and Support Vector Machines
S. Arun Kumar1, Abhishek Jha2, Shashank Shekhar3, Ashutosh Kumar Singh4
1Mr. Arun Kumar, Asst. Professor, Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India.
2Abhishek Jha, B.Tech Student, Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India.
3Shashank Shekhar, B.Tech Student, Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India.
4Ashutosh Kumar Singh, B.Tech Student, Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 473-477 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7026118419/2019©BEIESP | DOI: 10.35940/ijrte.D7026.118419
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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: Prediction of stock markets is the act of attempting to determine the future value of an inventory of a business or other financial instrument traded on an economic exchange.Effectively foreseeing the future cost of a stock will amplify the benefits of the financial specialist.This article suggests a model of machine learning to forecast the price of the stock market.During the way toward considering various techniques and factors that should be considered, we found that strategy, for example, random forest, support vector machines were not completely used in past structures. In this article, we will present and audit an increasingly suitable strategy for anticipating more prominent exactness stock oscillations.The primary thing we thought about was the securities exchange estimating informational index from yahoo stocks. We will audit the utilization of random forest after pre-handling the data, help the vector machine on the informational index and the outcomes it produces.The powerful stock gauge will be a superb resource for financial exchange associations and will give genuine options in contrast to the difficulties confronting the stock speculator.
Keywords: Multiple Instance Learning, Support Vector Machine, Random Forest, Data Set, Stock Market.
Scope of the Article: Machine Learning.