This deep learning project involves the usage of time series forcastig to predict stock prices. It uses the Long Short Term Memory (LSTM) RNN network architecture to predict future stock prices of a company. LSTM models are capable of handling long term dependencies and because they can store large amounts of historical data, they are frequently used in sequence prediction problems.
Here, factors like open price, close price and volume of stocks traded were taking into consideration and fed to the DL model to predict close price of stocks in 2021.