Indian Stock Price Prediction Using Machine Learning Algorithm and Sentiment Analysis
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Experimental Result Paper | Computer Engineering | India | Volume 11 Issue 3, March 2022 | Popularity: 5.1 / 10


     

Indian Stock Price Prediction Using Machine Learning Algorithm and Sentiment Analysis

Harshwardhan Patil, Rahul Patil


Abstract: Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e. g., national elections, natural calamity etc. Investors in the stock market seek to maximize their profits for which they require tools to analyze the prices and trend of various stocks. Machine learning algorithms have been used to devise new techniques to build prediction models that can forecast the prices of stock and tell about the market trend with good accuracy. Many prediction models have been proposed to incorporate all the major factors affecting the price of stocks. This project focuses random forest and sentiment-based prediction to decide buy or sell of the stock.


Keywords: Stock Market Prediction; Machine Learning Algorithm


Edition: Volume 11 Issue 3, March 2022


Pages: 1365 - 1368


DOI: https://www.doi.org/10.21275/SR22315122205


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Harshwardhan Patil, Rahul Patil, "Indian Stock Price Prediction Using Machine Learning Algorithm and Sentiment Analysis", International Journal of Science and Research (IJSR), Volume 11 Issue 3, March 2022, pp. 1365-1368, https://www.ijsr.net/getabstract.php?paperid=SR22315122205, DOI: https://www.doi.org/10.21275/SR22315122205

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