INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (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)
ABSTRACT
Predicting a small cap company growth is always a difficult task but by using some technology we can predict it successfully. First we need to collect data from various sources and create graphs for data then predict the stock growth according to the market to the market conditions and company performance.
To predict the stock growth we need to use some algorithms like linear regression algorithm, support vector machine learning algorithm and naïve bayes algorithms. These three algorithms are important to analyses the stock growth in a successful way. To predict the stock growth we need to use python coding language. The pre defined algorithms and libraries are helps to analyses the stock growth in a easier way.
In this journal we practically shown you the code of python and the successful execution images also.
Keywords:
smallcap, prediction, regression algorithm, machine learning, google colab.
Cite Article:
"PREDICTING A SMALL CAP COMPANY GROWTH USING PYTHON LIBRARIES", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 6, page no.g358-g372, June-2023, Available :http://www.ijnrd.org/papers/IJNRD2306638.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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