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dc.contributor.author
Siaminos, Georgios
en
dc.date.accessioned
2019-07-12T10:28:11Z
dc.date.available
2019-07-13T00:00:11Z
dc.date.issued
2019-07-12
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29416
dc.rights
Default License
dc.subject
Machine learning
en
dc.subject
Cryptocurrency
en
dc.title
Predicting the value of cryptocurrencies using machine learning time series analysis time series analysis time
en
heal.type
masterThesis
en_US
heal.classification
Data Science
en
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2019-05-01
heal.abstract
This dissertation was written as a part of the MSc in Data Science at the International Hellenic University. The main goal of this project is the prediction of Bitcoin price and movement using advanced machine learning techniques. Financial and blockchain features were used to train our algorithms and discover insights and patterns that describe Bitcoin movements during time. Furthermore, a sentiment analysis was deployed so to identify how news affect bitcoin price movement. Twitter posts were collected through Twitter search API with Tweepy open source library assistance. Python is the programming language that was used to fulfil the dissertation’s coding part. Machine learning algorithms such as Support Vector Machines, Extra trees, Gradient Boosting and Neural Networks were deployed to tackle our main problem.
en
heal.advisorName
Diamantaras, Konstantinos
en
heal.committeeMemberName
Diamantaras, Konstantinos
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US


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