"Stock Predictor" project basically aims to provide a visual representation and analysis of data related to time-series data which is constantly changing. This provides a dashboard to user displaying current trends and stocks data which uses ML like "LSTM" and "Random Forest" model.
LATEST UPDATE:
TO RUN LATEST SITE:
GO INTO FILE DIRECTORY "site_integration"
cd "site_integration"
To run streamlit application:
streamlit run app.py
To Access frontend side of project, cd into Utkarsh Test.
To Access backend side of project, cd into Debasish Test and Amal Test.
To Access and run project as a whole, cd into final_project.
cd "C:\Users\Debasish Ray\Desktop\stock\StockPredictor\final_project"
streamlit run app.py
cd "stock_frontend"
npm run start
cd data_backend
node server.js
Note: This project is still in production and will not resemble the final product.
For this project, we have included a different repository with different models trained on different epoch cycles and parameters, which are usable and integratable in this project. Link to Model's Repository
docker run debasishray/streamlit-app:v1.0
docker stop debasishray/streamlit-app:v1.0
docker tag debasishray/streamlit-app:v1.0 webapp
docker tag webapp ghcr.io/debasishray16/stockpredictor/webapp:latest
docker image ls
# For authentication
echo "pat-value" | docker login ghcr.io -u debasishray16 --password-stdin
# ghcr.io/<username>/<repository>
docker push ghcr.io/debasishray16/stockpredictor/webapp:latest