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Analysis and Prediction of Stock Market Mining using Machine Learning Clustering Technique

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 7
Year of Publication: 2021
Authors:
Zahraa Elsayed Mohamed, El-Amin Kamal El-Din El-Mesalamy
10.5120/ijca2021921366

Zahraa Elsayed Mohamed and El-Amin Kamal El-Din El-Mesalamy. Analysis and Prediction of Stock Market Mining using Machine Learning Clustering Technique. International Journal of Computer Applications 183(7):39-44, June 2021. BibTeX

@article{10.5120/ijca2021921366,
	author = {Zahraa Elsayed Mohamed and El-Amin Kamal El-Din El-Mesalamy},
	title = {Analysis and Prediction of Stock Market Mining using Machine Learning Clustering Technique},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2021},
	volume = {183},
	number = {7},
	month = {Jun},
	year = {2021},
	issn = {0975-8887},
	pages = {39-44},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume183/number7/31943-2021921366},
	doi = {10.5120/ijca2021921366},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Stock market plays a vital role in a country’s economy and it is an important consideration in all the fields due to its potential financial gain. This paper shows that data mining and unsupervised machine learning technique could be used to guide an investor’s decisions. A model has been built using data mining future stock price, whether stock price go high or low can be predicted. Moreover, the best clustering indicators in Egypt Stock Exchange for all the 30 companies (EGX30) during first half year of 2019 has been identified.

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Keywords

Unsupervised Machine Learning, Data Mining, Clustering, Stock Market, EGX 30 Index