https://github.com/paulpascal/g9_iris_predictor
A simple machine learning project to predict the species of an Iris flower based on its features. This project was developed by **Group9** as part of the **DevOps Master IA 1** course.
https://github.com/paulpascal/g9_iris_predictor
Last synced: 11 months ago
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A simple machine learning project to predict the species of an Iris flower based on its features. This project was developed by **Group9** as part of the **DevOps Master IA 1** course.
- Host: GitHub
- URL: https://github.com/paulpascal/g9_iris_predictor
- Owner: paulpascal
- Created: 2024-07-16T22:58:40.000Z (almost 2 years ago)
- Default Branch: develop
- Last Pushed: 2024-07-18T11:22:30.000Z (almost 2 years ago)
- Last Synced: 2024-07-19T06:15:48.235Z (almost 2 years ago)
- Language: Python
- Size: 34.2 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Iris Species Predictor
A simple machine learning project to predict the species of an Iris flower based on its features.
## Project Information
This project was developed by **Group9** as part of the **DevOps Master IA 1** course. The team members are:
- **Paul ALOGNON-ANANI**
- **Amal TANI NOUR**
- **Celestin PEHAN**
## Requirements
- Docker
- Docker Compose
## Setup
1. Clone the repository.
2. Build and run the Docker container:
```bash
docker-compose up --build
```
3. Access the application at `http://localhost:5000`.
## Deployment
Deployment is handled via GitHub Actions. Push to the `main` branch triggers the deployment to AWS EC2.
## Testing
Run tests with:
```bash
pytest tests
```