debasishray16/StockPredictor

"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.

Stock Prediction System

To run streamlit application:

1. Go to file directory ..Debasish Test\Previous Model

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.

2. For running final project.

• Go to directory in final project.

cd "C:\Users\Debasish Ray\Desktop\stock\StockPredictor\final_project"

• Run the app file in streamlit.

streamlit run app.py

• Go to directory in stock_frontend

cd "stock_frontend"

• Run the scripts.

npm run start

• Then ,start the server by navigating in the file. (final_project\stock_frontend\data_backend)

cd data_backend

• Run node server

node server.js

Results

Note: This project is still in production and will not resemble the final product.

Note (Information)

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