Academic Journal
STOCK MARKET ANALYSIS USING MACHINE LEARNING
| العنوان: | STOCK MARKET ANALYSIS USING MACHINE LEARNING |
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| المؤلفون: | Kumar, TS Vinay, Rakesh, Prof.Spoorthi, rai, Manas, C,, Yathish, Kumar,, Ankit |
| المصدر: | International Journal of Advanced Research in Computer Science; Vol. 11 (2020): VOLUME 11 SPECIAL ISSUE 1, MAY 2020; 142-144 ; 0976-5697 ; 10.26483/ijarcs.v11i0 |
| بيانات النشر: | International Journal of Advanced Research in Computer Science |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Stock Market, Time series Analysis, Fundamental Analysis, Statistical Analysis, Linear Regression |
| الوصف: | In this vast finance driven world stock trading is one of the most important and profitable activity. Stock forecast is the strategy for attempting to foresee the future estimation of an organisation stock or other money related resource of an organisation on a trade showcase . People invest in Stock markets based on suggestions which indirectly are based on predictions. Many methods like time-series analysis, statistical analysis and fundamental analysis are used in an attempt to predict but none of them are favourable for perfect prediction due to the volatile nature of the stock market. The programming language used in this context for analysis and prediction is Python and the prediction algorithm used in this context is Linear Regression to predict stock prices for various companies. This paper explains the prediction of stock using Machine Learning. |
| نوع الوثيقة: | article in journal/newspaper conference object |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | http://www.ijarcs.info/index.php/Ijarcs/article/view/6572/5299; http://www.ijarcs.info/index.php/Ijarcs/article/view/6572 |
| DOI: | 10.26483/ijarcs.v11i0.6572 |
| الاتاحة: | http://www.ijarcs.info/index.php/Ijarcs/article/view/6572 https://doi.org/10.26483/ijarcs.v11i0.6572 |
| Rights: | Copyright (c) 2020 International Journal of Advanced Research in Computer Science |
| رقم الانضمام: | edsbas.AA1C3948 |
| قاعدة البيانات: | BASE |
| DOI: | 10.26483/ijarcs.v11i0.6572 |
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