UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 9 Issue 9
September-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2209405


Registration ID:
502600

Page Number

e33-e41

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Title

Stock price prediction using Machine Learning

Authors

Abstract

Nowadays, a large amount of data is available everywhere. Therefore, it is very important to analyze this data to extract some useful information and develop an algorithm based on this analysis. This can be achieved through data mining and machine learning. Machine learning is an integral part of artificial intelligence, which is used to design algorithms based on data trends and historical relationships between data. ML is used in various fields such as bioinformatics, intrusion detection, Information retrieval, game playing, marketing, malware detection, image deconvolution, and so on.

Key Words

Regression, ARIMA, prediction, auto-correlation

Cite This Article

"Stock price prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.e33-e41, September-2022, Available :http://www.jetir.org/papers/JETIR2209405.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Stock price prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 9, page no. ppe33-e41, September-2022, Available at : http://www.jetir.org/papers/JETIR2209405.pdf

Publication Details

Published Paper ID: JETIR2209405
Registration ID: 502600
Published In: Volume 9 | Issue 9 | Year September-2022
DOI (Digital Object Identifier):
Page No: e33-e41
Country: Pune, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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