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"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.

Jupyter Notebook 32.73% HTML 0.01% CSS 0.06% JavaScript 0.31% Python 66.61% C 0.06% PowerShell 0.21% Batchfile 0.01%
htmlcssjs javascript react reactjs tailwindcss typescript artificial-intelligence css3 html5 lstm lstm-neural-networks random-forest

stockpredictor's Introduction

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.

  1. 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

Frontend Integration

Backend Integration

Backend Integration

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

stockpredictor's People

Contributors

debasishray16 avatar gamecoder08 avatar necromancer71803 avatar

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