Stock price prediction is a critical task in the financial domain, with implications for investors, traders, and businesses. This project aims to leverage the power of artificial neural networks to analyze historical time series data and predict the closing stock prices of a given financial instrument.
The LSTM neural network, a type of recurrent neural network (RNN), will be the core of the stock price prediction model. LSTMs are well-suited for time series data as they can capture long-term dependencies and patterns in sequences, making them effective in forecasting stock prices. It is a variant of the recurrent neural network (RNN) designed to overcome some of the limitations of traditional RNNs, especially when dealing with long-term dependencies in sequences.
Run the following command using pip
!pip install flask pickle yfinance sklearn numpy keras tensorflow
