Iris Flower Classifier

This is a beginner-friendly machine learning project that classifies iris flowers into three species (Setosa, Versicolor, and Virginica) based on their sepal and petal measurements.

Project Overview

The Iris dataset is a classic dataset in machine learning and statistics. It contains 150 samples from three species of iris flowers:

• Setosa
• Versicolor
• Virginica

Each sample has four features:

• Sepal length
• Sepal width
• Petal length
• Petal width

Features

• Data exploration and visualization
• Data preprocessing and scaling
• Model training using Random Forest Classifier
• Model evaluation with accuracy metrics
• Prediction functionality for new samples

Requirements

• Python 3.7+
• scikit-learn
• pandas
• matplotlib
• seaborn

Installation

1. Clone this repository:

   git clone <repository-url>
   

2. Navigate to the project directory:

   cd iris_classifier
   

3. Install the required packages:

   pip install -r requirements.txt
   

Usage

Run the classifier:

python iris_classifier.py

The script will:

1. Load and explore the Iris dataset
2. Visualize the data distributions
3. Train a Random Forest model
4. Evaluate the model performance
5. Show an example prediction

Run the Web Interface

To run the web interface:

python web_app.py

Then open your browser to http://127.0.0.1:5000 to use the interactive classifier.

Project Structure

• iris_classifier.py: Main script containing the classifier implementation
• simple_iris_classifier.py: Simplified version of the classifier
• web_app.py: Web interface for the classifier
• requirements.txt: List of required Python packages
• README.md: Project documentation
• CONTRIBUTING.md: Guide for contributing to the project
• iris_data_visualization.png: Visualization of the dataset
• iris_correlation_matrix.png: Feature correlation heatmap
• iris_confusion_matrix.png: Model evaluation confusion matrix
• iris_feature_importance.png: Feature importance chart

Learning Outcomes

By working on this project, you will learn:

• How to load and explore datasets using pandas
• Data visualization techniques with matplotlib and seaborn
• Data preprocessing and feature scaling
• How to train and evaluate machine learning models
• Model evaluation metrics and techniques
• How to make predictions with trained models

Possible Extensions

• Try different classification algorithms (SVM, KNN, Logistic Regression)
• Implement cross-validation for more robust evaluation
• Add more visualization types
• Create a simple web interface using Flask or Streamlit
• Experiment with hyperparameter tuning

Contributing

This project is designed for educational purposes. Feel free to:

1. Fork the repository
2. Make improvements
3. Submit pull requests

License

This project is open source and available under the MIT License.

Iris_Classifier