A professional strategy for Bitcoin and Ethereum using Machine Learning for Investors

Abstract

Forecasting economic goods market recoveries is challenging due to the volatility and uncertainty of the market's structure. Since the introduction of machine learning and increased computer power, programmable forecasting approaches have been found to be particularly effective in predicting stock values. Random Decision Forest, K - Nearest Neighbors, and Linear Regression were among the natural processing techniques employed in this study to forecast the costs of popular crypto currencies like Bit coin and Ethereum over the next few years. The model is given financial data on stock prices from the start of the day to the end of the day. Bitcoin's value may or may not improve in the future because it has been on the market for a decade. Ethereum was created in 2015 and is now the second most popular crypto currency on the market. The value of Ethereum is nearly comparable to that of Bit coin. After a few years, it is doubtful whether bit coin will be available on the market. As a result, buyers seek forecasts in order to invest in the right crypto currency and profit from it. The user can use this forecast to anticipate the future of both crypto currency prices. To assess the value of crypto currencies, we used three forecasted analytic techniques, and we can compare the accuracy of the three algorithms to see which one is the best.