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https://github.com/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.
https://github.com/debasishray16/stockpredictor
artificial-intelligence css html5 javascript keras-neural-networks linear-regression-python lstm-neural-networks machine-learning random-forest random-forest-classification reactjs streamlit tailwind-css tensorflow-models yfinance-data-warehouse
Last synced: 3 months ago
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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.
- Host: GitHub
- URL: https://github.com/debasishray16/stockpredictor
- Owner: debasishray16
- Created: 2024-03-02T16:22:42.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-10-10T00:22:21.000Z (3 months ago)
- Last Synced: 2024-10-10T08:43:14.770Z (3 months ago)
- Topics: artificial-intelligence, css, html5, javascript, keras-neural-networks, linear-regression-python, lstm-neural-networks, machine-learning, random-forest, random-forest-classification, reactjs, streamlit, tailwind-css, tensorflow-models, yfinance-data-warehouse
- Language: Python
- Homepage: https://ticker-prediction-app-tpa.streamlit.app/
- Size: 75.2 MB
- Stars: 1
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Stock Prediction System
LATEST UPDATE:
TO RUN LATEST SITE:
GO INTO FILE DIRECTORY "site_integration"
```cmd
cd "site_integration"
```
To run streamlit application:
1. Go to file directory ..Debasish Test\Previous Model
```bash
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.
```cmd
cd "C:\Users\Debasish Ray\Desktop\stock\StockPredictor\final_project"
```
- Run the app file in streamlit.
```bash
streamlit run app.py
```
- Go to directory in stock_frontend
```bash
cd "stock_frontend"
```
- Run the scripts.
```bash
npm run start
```
- Then ,start the server by navigating in the file.
(final_project\stock_frontend\data_backend)
```cmd
cd data_backend
```
- Run node server
```bash
node server.js
```
**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
```shell
docker run debasishray/streamlit-app:v1.0
docker stop debasishray/streamlit-app:v1.0
```
## 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.
```bash
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//
docker push ghcr.io/debasishray16/stockpredictor/webapp:latest
```