debasishray16/StockPredictor

This project basically aims to provide a visual representation and comparative analysis of close price data related to different company ticker. It involves an interactive dashboard for users to display analysis and prediction of stocks data by using LSTM + XG-Boost 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