Open AccessStock Market Value Prediction using Machine Learning Concept
Author(s) -
V. Nigamrutha*,
S. Anusuya
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f3908.049620
Subject(s) - stock market , stock exchange , stock (firearms) , support vector machine , computer science , python (programming language) , machine learning , artificial intelligence , stock market prediction , econometrics , economics , finance , programming language , mechanical engineering , paleontology , horse , engineering , biology
Stock market consists of various buyers and sellers. The stock market value is dynamic. It means the stock market value is changed day by day. Actually stock has been represented as shares. The owner of the share may be an individual or group of peoples. In this current economic condition stock market value prediction is the critical task because the data is dynamic. Stock market prediction means to find the future value of the stock on a financial exchange. The expected prediction output to be accurate, efficient and robust value. Traditionally the stock values are predicted by using stock related news. But it does not provide a better result. Wrong prediction of stock value leads to heavy loss. Machine learning concepts play a very important role in various domains. It is also used to predict the stock market value with the help of collected data. This paper describes about stock market value prediction using machine learning SVM (Support Vector Machine) technique. This proposed concept is implemented by python programming language. This machine learning concept produces better prediction result compared with other machine learning techniques.