Stock Price Prediction using Deep Learning

This project applies deep learning techniques, including LSTM and feedforward neural networks, to predict stock prices. The LSTM model is specifically used for time series forecasting, leveraging its ability to process sequential data and remember past trends. The repository includes various preprocessing steps like handling missing values, scaling, and feature engineering. The project also compares different deep learning architectures, including CNNs (Convolutional Neural Networks), for stock price prediction and discusses the trade-offs between these methods.