We built and evaluated models for predicting hourly ridership of the NYC subway system, considering both the total ridership of the system and the ridership at each station. We find that an LSTM performs best for modeling the total ridership and that a CNN approach works best for the many-station case. We incorporate novel data visualization techniques to illustrate our dataset and our model's predictions.