This is a Python project that uses Flask to create a web application that predicts the species of an iris flower based on its sepal length, sepal width, petal length, and petal width. The project uses a machine learning model that is trained on the Iris dataset, which is a well-known dataset in the machine learning community.
- Clone the repository: git clone https://github.com/username/project.git
- Install the required packages: pip install -r requirements.txt
- Run the Flask server: python app.py
- Open a web browser and go to http://localhost:5000
- Enter the values for sepal length, sepal width, petal length, and petal width.
- Click the "Submit" button to see the predicted species.
- Click the "Sample 1", "Sample 2", or "Sample 3" button to fill in the form with pre-defined values.
If you would like to contribute to this project, please follow these steps:
- Fork the repository.
- Create a new branch: git checkout -b feature/your-feature
- Make your changes and commit them: git commit -m "Add your message here"
- Push to the branch: git push origin feature/your-feature
- Submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
- Thank you to Name for their contributions to this project.
- This project was inspired by Name.