This project focuses on predicting stock prices using Long Short-Term Memory (LSTM) networks implemented in PyTorch. The goal is to create a predictive model that can forecast future stock prices based on historical data.
LSTM networks are well-suited for sequence data, making them a popular choice for time series forecasting tasks like stock price prediction. The project showcases how to preprocess the data, design and train an LSTM model, and evaluate its predictive performance.
The main objective of this project is to demonstrate the application of LSTM neural networks for predicting stock prices. The project evaluates the LSTM model's performance using the Mean Squared Error (MSE) and visualizations of the predicted stock prices compared to the actual prices. The results demonstrate how well the model generalizes to unseen data.