Abstract

'Bitcoin Price Prediction’, the ruler of cryptocurrency plays important role in blockchain technology. - In this project, we proposed to predict the Bitcoin price accurately taking into consideration various parameters that affect the Bitcoin value. 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 year, recorded daily. Features such as the opening price, highest price, lowest price, closing price, volume of Bitcoin, volume of currencies, and weighted price were taken into consideration so as to predict the closing price of the next day. Random forest model designed and implemented on scikit learn frameworks to build predictive analysis and evaluated them by computing various measures such as the RMSE (root mean square error) and r (Pearson's correlation coefficient) on test data. Flask framework was used to make prediction in webpages and Beautiful Soup is used to scrap the data from 'URL': 'https://bitinfocharts. The future prediction of bitcoin is predicted as a result from today real time data. Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. Moreover, the research experiments are repeated several times to achieve the best results by employing hyperparameter tuning of each algorithm. This involves selecting an appropriate kernel and suitable data normalization technique for SVR.