Stock Price Prediction
N P Samarth1, Gowtham V Bhat2, Hema N3
1Mr. N P Samarth, Department of Information Science and Engineering, RNS Institute of Technology, Bengaluru (Karnataka), India.
2Mr. Gowtham V Bhat, Department of Information Science and Engineering, RNS Institute of Technology, Bengaluru (Karnataka), India.
3Mrs. Hema N, Assistant Professor, Department of ISE, Information Science and Engineering, RNS Institute of Technology, Bengaluru (Karnataka), India.
Manuscript received on 05 December 2019 | Revised Manuscript received on 13 December 2019 | Manuscript Published on 31 December 2019 | PP: 425-429 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10421292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1042.1292S19
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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: Stock trading is a very crucial activity in the world of Finance and is a supporting structure for many companies. Predicting the future value of a stock is the main goal of stock price prediction project. In this paper, we have used machine learning algorithms to predict future stock prices of a company. Stock prediction by the stock brokers is mainly done using the time series or the technical and fundamental analysis but as these techniques are very unreliable and limited, we propose making use of intelligent techniques such as machine learning. Python is a programming language which can be used to implement machine learning algorithms with its numerous inbuilt libraries. We propose an approach that uses machine learning algorithms and will be trained on the historical stock data that is available and gain intelligence, later it uses the knowledge acquired for predicting the stock prices accurately. Random Forest Regression is one of the machine learning technique that is used for stock price prediction for small and large capitalizations also in different markets employing both up-to-minute and daily frequencies.
Keywords: Machine Learning, Random Forest Regression, Stock Market, Predictions.
Scope of the Article: Regression and Prediction