Stock Trend Prediction using LSTM and Streamlit

In recent years, the field of stock trend prediction has witnessed a surge in the application of deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, owing to their superior ability to handle time series data. This paper presents a web-based application developed using Python and Streamlit that predicts stock trends using an LSTM model trained on historical stock prices. Data is fetched using the yfinance API and preprocessed using pandas and NumPy. The model is developed with Keras and TensorFlow, and includes visualizations such as moving averages and actual vs. predicted price trends. This system aims to provide retail investors with accessible, interpretable, and real-time predictions to support informed investment decisions.