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IJSTR >> Volume 9 - Issue 4, April 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Research On Bitcoin Price Prediction Using Machine Learning Algorithms

[Full Text]

 

AUTHOR(S)

Lekkala Sreekanth Reddy, Dr.P. Sriramya

 

KEYWORDS

Bitcoin , crypto currency, Decision Tree, K-Means Algorithm, Lasso Algorithm, nave Bayes algorithm, Prediction, Random Forest.

 

ABSTRACT

In this paper, we proposed to predict the Bitcoin price accurately taking into consideration various parameters that affect the Bitcoin value. By gathering information from different reference papers and applying in real time ,I found the advantages and disadvantages of bitcoin price prediction. Each and every paper has its own set of methodologies of bitcoin price prediction. Many papers has accurate price but some other don’t, but the time complexity is higher in those predictions, so to reduce the time complexity here in this paper we use an algorithm linked to artificial intelligence named LASSO(least absolute shrinkage selection operator. The other papers used different algorithms like SVM(support vector machine),coinmarkupcap, Quandl, GLM, CNN(Convolutional Neural Networks)and RNN(Recurrent neural networks) etc.. which do not have a great time management, but in LASSO finding of the results from a larger database is quick and fast..so for this purpose we draw a comparison between other algorithms and the LASSO algorithm, this survey paper helps the upcoming researchers to make an impact in the their papers. The process happens in the paper is first moment of the research, we aim to understand and find daily trends in the Bitcoin market while gaining insight into optimal features surrounding Bitcoin price. Our data set consists of various features relating to the Bitcoin price and payment network over the course of every years, recorded daily. By preprocessing the dataset, we apply the some data mining techniques to reduce the noise of data. Then the second moment of our research, using the available information, we will predict the sign of the daily price change with highest possible accuracy.

 

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