Iris Species Classification Web App 🌸

A Flask-based web application that predicts Iris flower species using machine learning. The application uses a trained model to classify Iris flowers into three species: Setosa, Versicolor, and Virginica, based on their sepal and petal measurements.

Features ✨

• Interactive web interface for input measurements
• Real-time predictions using machine learning
• Simple and intuitive design

Demo 🚀

Installation 🛠️

1. Clone the repository:

git clone https://github.com/kozuedoingregression/Iris-Species-Classification.git
cd Iris-Species-Classification

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

Usage 💻

1. Start the Flask application:

python app.py

2. Enter the following measurements in centimeters:

   • Sepal Length
   • Sepal Width
   • Petal Length
   • Petal Width

3. Click "Predict" to see the classification result

4. Run tests

python -m unittest testing.py

Project Structure 📁

iris-classification/
├── database/
│   ├── Iris.xls
├── model/
│   ├──IrisFlowerClaffification.pkl
├── templates/
│   ├── index.html
├── app.py              # Flask application
├── requirements.txt
├── testing.py

Model Performance 📊

• The model was trained using scikit-learn's Logistic Regression on the Iris Dataset.
• Accuracy: 97%
• NoteBook

Technical Details 🔧

• Framework: Flask
• Machine Learning: scikit-learn
• Model: Logistic Regression
• Dataset: Iris Dataset
• Frontend: HTML, CSS, JavaScript

Requirements 📋

• Python 3.8+
• Flask
• scikit-learn
• pandas
• numpy

Development 👨‍💻

To contribute to this project:

1. Fork the repository
2. Create a new branch
3. Make your changes
4. Submit a pull request

Contact 📧

For questions or feedback, please contact:

• X: kozue
• GitHub: kozuedoingregression