  In this paper we apply neural networks and Artificial Intelligence (AI) to
historical records of high-risk cryptocurrency coins to train a prediction
model that guesses their price. This paper's code contains Jupyter notebooks,
one of which outputs a timeseries graph of any cryptocurrency price once a CSV
file of the historical data is inputted into the program. Another Jupyter
notebook trains an LSTM, or a long short-term memory model, to predict a
cryptocurrency's closing price. The LSTM is fed the close price, which is the
price that the currency has at the end of the day, so it can learn from those
values. The notebook creates two sets: a training set and a test set to assess
the accuracy of the results.
  The data is then normalized using manual min-max scaling so that the model
does not experience any bias; this also enhances the performance of the model.
Then, the model is trained using three layers -- an LSTM, dropout, and dense
layer-minimizing the loss through 50 epochs of training; from this training, a
recurrent neural network (RNN) is produced and fitted to the training set.
Additionally, a graph of the loss over each epoch is produced, with the loss
minimizing over time. Finally, the notebook plots a line graph of the actual
currency price in red and the predicted price in blue. The process is then
repeated for several more cryptocurrencies to compare prediction models. The
parameters for the LSTM, such as number of epochs and batch size, are tweaked
to try and minimize the root mean square error.
