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An Artificial intelligence is the branch of machine learning. For the execution of this project, we use different machine learning algorithms linear regression, multiple regression algorithms and some LSTM concepts are applied. Linear Regression helps us to perform and deal with two features of stocks that are the opening and closing value for a particular day. This project will help buyers to get their best price of their expected stocks and maximize the profits. This helps them to know the peoples in advance that the decision they are making is correct or incorrect for the price of stock which it bidding. Investment is a business model that peoples are interested to follow in this new era of world. Real estate stock prices are not only a concern for buyers and sellers, but also reflect the current state of the economy. Different factors are observed during the prediction of the price of stock such as number of opening price, closing price patterns and graphs since weeks. The most important factors are the changes which are going to reflect in different graphs for all certain stock market companies, these companies having labeled features for a certain stock market analysis.
"Stock Price Prediction Various Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 6, page no.1032 - 1037, June-2023, Available :http://www.ijrti.org/papers/IJRTI2306153.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator