Stock Picker using Machine Learning
Nambirajan M1, Rajdev R2, Santhosh R3, Sharon Raja D4, Sivakamasundari G5
1Nambirajan M, Final year Engineering Graduate in the Department of Computer Science and Engineering at National Engineering college, Kovilpatti, Tamil Nadu, India.
2Rajdev R, Final year Engineering Graduate in the Department of Computer Science and Engineering at National Engineering college, Kovilpatti, Tamil Nadu, India.
3Santhosh R, Final year Engineering Graduate in the Department of Computer Science and Engineering at National Engineering college, Kovilpatti, Tamil Nadu, India.
4Sharon Raja D, Final year Engineering Graduate in the Department of Computer Science and Engineering at National Engineering college, Kovilpatti, Tamil Nadu, India.
5Sivakamasundari G, Assistant Professor(SG), Computer science and engineering, National Engineering College, Kovilpatti, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3912-3914 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9341038620/2020©BEIESP | DOI: 10.35940/ijrte.F9341.038620
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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 main objective of this paper is to build a model to predict the value of stock market prices from the previous year’s data. This project starts with collecting the stock price data and pre-processing the data. 12 years dataset is used to train the model by the Random Forest classifier algorithm. Backtesting is the most important part of the quantitative strategy by which the accuracy of the model is obtained. Then the current data is collected from yahoo finance and the data is fed to the model. Then the model will predict the stock that is going to perform well based on its learning from the historical data. This model predicted the stocks with great accuracy and it can be used in the stock market institution for finding the good stock in that index.
Keywords: Stock prediction, Machine Learning, Stock market, S&P500, Random forest classifier, Long-term trading.
Scope of the Article: Machine Learning.