Paper Title
Stock Price Visualizing and Forecasting
Article Identifiers
Authors
M JOTHIKA SUMALI , M VANDANA , M UMA SHANKARI , NALLAPARAJU B N VENKATA PRABHAVATHI DEVI , Prof. B PRAJNA
Keywords
Stock price, yfinance, Machine learning, forecasting, prediction, visualizing, Dash framework python.
Abstract
The stock market offers one of the biggest returns on the market, but it is exceedingly difficult to predict stock prices because there are no set guidelines for doing so. Although they are volatile in nature, share prices and other statistical factors may be seen, which aids savvy investors in carefully selecting the company they wish to put their profits in. We may create dynamic graphs of financial data for a particular company using tabular data provided by the yfinance Python module by using this straightforward project idea. In addition, we can forecast future stock prices using a machine learning system. The project is a wonderful introduction to Python/data science for newcomers and an useful refresher for experts who have experimented with Python/ML in the past.
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How To Cite (APA)
M JOTHIKA SUMALI, M VANDANA, M UMA SHANKARI, NALLAPARAJU B N VENKATA PRABHAVATHI DEVI, & Prof. B PRAJNA (March-2023). Stock Price Visualizing and Forecasting. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), c836-c842. https://ijnrd.org/papers/IJNRD2303295.pdf
Issue
Volume 8 Issue 3, March-2023
Pages : c836-c842
Other Publication Details
Paper Reg. ID: IJNRD_189236
Published Paper Id: IJNRD2303295
Downloads: 000121976
Research Area: Computer Science & TechnologyÂ
Country: visakhapatnam, andhra pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2303295.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303295
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.
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Current Issue: Volume 10 | Issue 10 | October 2025
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