🌸 Flower Classification (Iris Dataset)

This project implements flower classification using the famous Iris dataset.
It explores different machine learning approaches and includes a trained Support Vector Machine (SVM) model for prediction.

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📂 Project Structure

flower-classification/
├── Iris Classification - DataFlair.ipynb   # Reference notebook
├── Iris Classification - DataFlair.py      # Python script
├── main.ipynb                              # Main project notebook
├── iris.data                               # Iris dataset (features)
├── iris.names                              # Dataset description
├── SVM.pickle                              # Pre-trained SVM model
├── main.pdf                                # Project report
├── requirements.txt                        # Dependencies 
└── README.md                               # Project documentation

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📊 Dataset

• Dataset: Iris Dataset
• Classes:
  • Iris Setosa
  • Iris Versicolour
  • Iris Virginica
• Features:
  • Sepal length
  • Sepal width
  • Petal length
  • Petal width

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⚙️ Installation

Clone the repository and install dependencies:

git clone https://github.com/your-username/flower-classification.git
cd flower-classification
pip install -r requirements.txt

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🚀 Usage

1. Run the Notebook

Open the main Jupyter notebook:

jupyter notebook main.ipynb

2. Run the Python Script

You can also execute the script directly:

python "Iris Classification - DataFlair.py"

3. Load Pre-trained Model

The SVM.pickle file contains a trained model:

import pickle

with open("SVM.pickle", "rb") as f:
    model = pickle.load(f)

sample = [[5.1, 3.5, 1.4, 0.2]]  # Example input
prediction = model.predict(sample)
print("Predicted class:", prediction)

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📈 Results

• Model: Support Vector Machine (SVM)
• Accuracy: ~97% on test data (Iris dataset)

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📝 References

• Iris Dataset - UCI ML Repository
• DataFlair tutorial on Iris Classification

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🤝 Contributing

Feel free to open issues or submit pull requests if you’d like to improve this project.

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📄 License

This project is licensed under the MIT License.