Iris Flowers Machine Learning Project

This project involves testing different machine-learning techniques on the Iris flower dataset. The techniques applied include classification, clustering, and the Decision Tree algorithm. Three different notebooks are built, each applying a different technique with detailed explanations.

Table of Contents

• Project Overview
• Features
• Technologies Used
• Installation
• Usage
• Contributing
• License
• Contact

Project Overview

The goal of this project is to demonstrate the application of various machine learning techniques on the Iris flower dataset. Each notebook in this repository explores a different machine-learning approach, providing detailed explanations and results.

Features

• Applies multiple machine learning techniques to the Iris flower dataset.
• Provides detailed explanations and results for each technique.
• Easy-to-follow Jupyter notebooks.

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/husseini2000/Iris-flowers.git

2. Navigate to the project directory:

   cd Iris-flowers

3. Install the required libraries:

   pip install -r requirements.txt

Usage

1. Open the Jupyter notebooks in your preferred environment (e.g., Jupyter Notebook, JupyterLab, VS Code).
2. Run the notebooks to see the different machine-learning techniques applied to the Iris flower dataset.

Contributing

Contributions are welcome! If you have any suggestions or improvements, please create a pull request or open an issue.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

• Al-Husseini Abdelaleem
• Email: husseiniahmed2015@gmail.com
• LinkedIn: linkedin.com/in/al-husseiniabdelaleem