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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: 7 days 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 (7 months ago)
- Default Branch: main
- Last Pushed: 2024-09-26T14:10:45.000Z (7 days ago)
- Last Synced: 2024-09-26T21:04:53.792Z (7 days 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:
- Size: 74.9 MB
- Stars: 1
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Stock Prediction System
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
```
## 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
```shell
docker run debasishray/streamlit-app:v1.0
docker stop debasishray/streamlit-app:v1.0
```