This project utilizes Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices. For data with timeframes recurrent neural networks (RNNs) come in handy but recent researches have shown that LSTM, networks are the most popular and useful variants of RNNs.

I have used Keras to build a LSTM to predict stock prices using historical closing price and trading volume and visualize both the predicted price values over time and the optimal parameters for the model.

• How to apply deep learning techniques: Long Short Term Memory Neural Network algorithms.
• How to use keras-tensorflow library.
• How to collect and preprocess given data.
• How to analyze model's performance.
• How to optimise Long Short Term Memory Neural Network algortithm, to ensure increase in postive results.

This project uses the following software and Python libraries:

• Python3
• NumPy
• pandas
• Keras
• Tensor-flow
• Jupyter Notebook