Recurrent neural network applied to forecasting stock prices based on financial statistics and historical data.
This project served foremost as a learning application of deep learning and statistical methods. The model utilizes inputs from a specified number of previous timesteps to project a future value of the statistics. The model architecture includes LSTM, dense, and dropout layers. Primary libraries used include Tensorflow, Keras, Pandas, and NumPy. Code written from scratch.