Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Stock trading is a very complex and dynamic phenomenon. People buy and sell stocks for the purpose of investments. Stock market trading can be beneficial if done in a wise and planned manner. With the help of technology, we can solve this cumbersome problem. The goal is to review different models and identify the best model that learns from the market data using machine learning techniques and forecast future trends in stock price movement. In this paper, 6 different supervised learning methods are analyzed and observations are made, on the type of method that can be implemented, after analyzing the available data and conditions.
"A Comparitive Study and Analysis of some Supervised Learning Techniques used for Stock Forecasting", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 7, page no.852 - 857, July-2022, Available :http://www.ijrti.org/papers/IJRTI2207126.pdf
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ISSN:
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