Stock Market Prediction

The stock market is a highly dynamic environment where anticipating price movements is both challenging and valuable for investors, traders, and financial analysts. This project introduces a stock price forecasting system built on Long Short-Term Memory (LSTM) networks, implemented using Python and deployed with Streamlit. Historical stock data obtained from Yahoo Finance is utilized to train the model, enabling the prediction of future price trends. The application provides an interactive platform where users can enter stock tickers, select date ranges, and configure model parameters with ease. Model accuracy is assessed through error metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The system also generates visualizations of historical trends, prediction outputs, and forward projections to enhance user interpretation. The project highlights the effectiveness of deep learning approaches in financial time-series forecasting while offering an accessible, user-friendly interface for stock analysis