In this study, the predictability of cryptocurrencies prices are analyzed at the daily and hourly level, through the use of machine learning methods including the use of sentiment analysis and LSTM Recurrent Neural Networks. This project carries out an investigation to assess the relationship between cryptocurrency closing price trends and online sentiment trends. The calculations for the online sentiment is done by retrieving tweets from Twitter and performing sentiment analysis on the textual data. There is an implementation to train a Recurrent Neural Network to make predictions for a future closing price of a cryptocurrency, using elements of the price and sentiment data to train the model. The predicted results and the calculated sentiment data along with some other useful data is displayed on this project's web application, which was the technical parts to the work done.