This project introduces a user-friendly web application designed to leverage the power of Long Short-Term Memory (LSTM) networks for predicting future stock prices. This intuitive tool empowers users to gain valuable insights into potential market trends and make informed investment decisions.
- LSTM-based Predictions: The core of the application utilizes a trained LSTM model, capable of analyzing historical stock data and learning complex patterns to forecast future price movements.
- User-friendly Interface: The web app offers a simple and interactive interface where users can easily input their desired stock symbol and timeframe, allowing for quick and convenient access to predictions.
- Visualization Tools: The application provides informative visualizations of historical price data, along with the predicted price trend, enabling users to grasp the forecast intuitively.
- Streamlit
- Tensorflow
- Python
- sklearn
- plotly
/project-root
||-backstream.py
||-ta.h5
||-ta.ipynb
- backstream.py : Main Web App
- ta.h5 : lstm based Model
- ta.ipynb : Notebook in which data is analysed and model has been created