Iris Flower Prediction App 🌸

A beginner-friendly Streamlit web application to classify Iris flower species using a Random Forest model. Built for fast, interactive predictions and educational exploration of classic machine learning concepts.

---

🌟 Features

• Interactive Input: Adjust sepal and petal measurements with sidebar sliders.
• Live Predictions: Instantly predict the Iris species and view probability scores.
• Dataset Explorer: Preview the Iris dataset and feature statistics.
• Feature Importance: Visualize which features matter most in classification.
• Clean UI: Simple, responsive dashboard for desktop and mobile.

---

🌐 Live Demo

Try the app instantly, no installation required:
https://iris-flower-classifier-app-ckb78utfqdnawpqvn2amy6.streamlit.app/

---

🖼️ Screenshots

Add images to the images/ folder and update paths below.

• App Home:
  

• Prediction Result:
  

• Feature Importance Chart:
  

---

🛠️ Tech Stack

• Python 3.7+
• Streamlit – Interactive dashboards
• Pandas – Data manipulation
• Scikit-Learn – Random Forest classifier
• Matplotlib – Plotting and visualization

---

📦 Installation

1. Clone the repository:

   git clone https://github.com/muzammaldeveloper/iris-flower-classifier-streamlit.git
   cd iris-flower-classifier-streamlit

2. Install dependencies:

   pip install -r requirements.txt

---

🚀 Usage

Launch the app locally:

streamlit run app.py

• Use the sidebar sliders to input flower features.
• View the prediction and probabilities instantly.
• Explore dataset and feature importance charts.

---

📁 Project Structure

iris-flower-classifier-streamlit/
├── app.py                # Main Streamlit app
├── data/
│   └── iris.csv          # Iris dataset
├── images/
│   ├── app_home.png      # Screenshot: Home
│   ├── prediction_output.png    # Screenshot: Prediction
│   └── feature_importance.png   # Screenshot: Feature importance chart
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation

---

🔮 Future Improvements

• Add model comparison (SVM, KNN, etc.)
• Enable model retraining with custom data
• Deploy app on cloud platforms (Streamlit Cloud, Hugging Face Spaces)
• Add multi-language support
• Improve mobile responsiveness

---

🤝 Contributing

Contributions are welcome!
If you find a bug or want to add a feature:

1. Fork this repo.
2. Create a new branch (git checkout -b feature-name).
3. Commit your changes with clear messages.
4. Open a pull request describing your changes.

For questions or suggestions, feel free to open an issue.

---

📬 Contact

• GitHub: muzammaldeveloper
• Email: muzammal.contect@gmail.com

---

👨‍💻 About the Developer

Muzammal Hussain
Passionate AI developer and community builder from Pakistan. Focused on making machine learning accessible for everyone, especially learners in low-resource environments.

Connect, learn, and build with me!

---

Made with ❤️ using Python and Streamlit.