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Forecasting of Cryptocurrency Rates Using Machine Learning for Bitcoin Market

Student: Bondarenko Georgy

Supervisor: Victor Popov

Faculty: Graduate School of Business

Educational Programme: Business Informatics (Bachelor)

Year of Graduation: 2018

This work is devoted to the prediction of the Bitcoin rate using machine learning models. The aim of the work is to test the hypothesis about the possibility of predicting the rate of cryptocurrencies using machine learning methods based on news data. To achieve this, a software product, that implements the selected machine learning models, were created. The work consists of introduction, main part and conclusion. The main part of the work is divided into three chapters. The first Chapter describes the existing research on the prediction of Bitcoin using machine learning methods. Their advantages and disadvantages are analyzed. The second Chapter provides a description of all stages of the application of machine learning models. It also provides a description of the selected models and metrics to evaluate them. The third Chapter considers the practical application of machine learning models to solve the problem of Bitcoin growth prediction, analyzes the results and provides recommendations for further development. The result of this work is a complete description of the process of applying machine learning models to predict the rate of Bitcoin using news data. A script implementing the described approach was also obtained. It provides sufficiently accurate forecasts that allow to make a profit. Keywords: investment, machine learning, cryptocurrency, Bitcoin, text analysis, NLP, NLTK, Bayesian models, linear models, SVM, python 3.

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