Stock Prediction using Machine Learning a Review Paper
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10.5120/ijca2017913453 |
Nirbhey Singh Pahwa, Neeha Khalfay, Vidhi Soni and Deepali Vora. Stock Prediction using Machine Learning a Review Paper. International Journal of Computer Applications 163(5):36-43, April 2017. BibTeX
@article{10.5120/ijca2017913453,
author = {Nirbhey Singh Pahwa and Neeha Khalfay and Vidhi Soni and Deepali Vora},
title = {Stock Prediction using Machine Learning a Review Paper},
journal = {International Journal of Computer Applications},
issue_date = {April 2017},
volume = {163},
number = {5},
month = {Apr},
year = {2017},
issn = {0975-8887},
pages = {36-43},
numpages = {8},
url = {http://www.ijcaonline.org/archives/volume163/number5/27395-2017913453},
doi = {10.5120/ijca2017913453},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
Abstract
Every day more than 5000 trade companies enlisted in Bombay stock Exchange (BSE) offer an average of 24,00,00,000+ stocks, making an approximate of 2000Cr+ Indian rupees in investments. Thus analyzing such a huge market will prove beneficial to all stakeholders of the system. An application which focuses on the patterns generated in this stock trade over the period of time, and extracting the knowledge from those patterns to predict future behavior of the BSE stock market is essential. An application representing the information in visual form for user interpretation to buy and to sell a specific company’s stock is a key requirement.
Such an application based on machine learning algorithms is the right choice in current scenario. This paper surveys the machine learning algorithms suitable for such an application; as well it discusses what are the current tools and techniques appropriate for its implementation.
References
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Keywords
Machine learning, review paper, stock prediction, machine learning algorithms, supervised learning, unsupervised learning, supervised learning algorithms, regression, classification, regression algorithm, Support Vector Machine (SVM), Support Vector Regression (SVR), classification, linear regression, logistic regression, types of regression, types of classification, types of programming languages for machine learning, types of libraries for machine learning, types of libraries for graphing, types of libraries for analysis.