Bitcoin (BTC) shares many characteristics with traditional stocks, but it is much more volatile since the cryptocurrency market is unregulated. The high volatility makes BTC a very high risk-high reward investment and predictive analysis can be very useful to obtain good returns and minimize risk. Taking Cocco et al. [1] as the primary reference, this thesis tries to reproduce their findings by building two BTC price forecasting models, Long Short-Term Memory (LSTM) and Bayesian Neural Network (BNN), and finding that the Mean Absolute Percentage Error (MAPE) is lower for the initial BNN model in comparison to the initial LSTM model. In addition to forecasting the value of BTC, a metric called trend% is developed to gauge the models’ ability to capture the trend of how the price varies from one timestep to the next and used to compare the trend prediction performance. It is found that both initial models make random predictions for the trend. Improvements like removing the stochastic component from the data and forecasting returns as opposed to price values show that both models show comparable performance in terms of both MAPE and trend%. The thesis concludes by discussing the future work that can be done to potentially improve the above models. One of the possibilities mentioned is to use on-chain data from the BTC blockchain coupled with the real-world knowledge of BTC exchanges and feed this as input features to the models.