Identification of an Iris Flower

Description

This project uses machine learning to classify iris flowers based on features such as petal length and width. It demonstrates basic classification algorithms and provides an educational example for learning machine learning.

Features

• Load and preprocess the Iris dataset.
• Train machine learning models (e.g., k-means clustering, decision trees) for classification.
• Visualize classification results with plots.

Library Used

• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

Installation

# Clone the repository
git clone https://github.com/Ableboy/Iris-Classifier.git

# Navigate into the project directory
cd Iris-Classifier

# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Use Case

Educational purposes for learning and demonstrating machine learning classification algorithms.

Contributing

We welcome contributions to this project! To contribute, please follow these steps:

1. Fork the repository on GitHub.

2. Clone your forked repository to your local machine.

git clone https://github.com/Ableboy/Iris-Classifier.git

3. Create a branch for your feature or bug fix.

git checkout -b feature/feature-name

4. Commit your changes with a clear message.

git commit -m 'Add some feature'

5. Push your changes to your forked repository.

git push origin feature/feature-name

6. Open a pull request against the main repository.

Please ensure your code adheres to the existing coding conventions and includes tests for any new functionality.

License

MIT License