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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.

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Stock Ticker Analysis and Prediction System using Stacked LSTM

Description

  • Our project works on concepts of deep learning to predict values based on time-series model. It includes use of LSTM (Long Short Term Memory) with XG-Boost to enhance the performance of prediction.

  • This repository invloves deployment of model with two-interfaces. One with Streamlit-App [Link] and React App

Install and Run the project

1. Running project for first time

To run our site. Follow the steps -

  • Navigate to site_integration folder.
 cd "site_integration"
  • Run following command if project is cloned for first time. This will install necessary node_modules folder in current folder.
npm install
npm run start
  • Simultaneously, Run a command in another terminal to start waitress_server.py file in /backend/waitress_server.py folder.
python waitress_server.py

terminal_Screenshot

Site Image

website_Preview

2. Running project on local system

No need to type npm install. Just type

cd "site_integration"
npm run start
  • Simultaneously, Run a command in another terminal to start waitress_server.py file in /backend/waitress_server.py folder.
python waitress_server.py

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

Steps to deploy on Github Packages

  1. Create a replica of Docker image with different tag.
  2. Check the image created.
  3. Authenticate by using PAT (Personal Access Token).
  4. Push that image in GitHub Packages.
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

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

docker run debasishray/streamlit-app:v1.0
docker stop debasishray/streamlit-app:v1.0

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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.

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