This project aims to classify different species of flowers based on the length of their petals and sepals. By leveraging machine learning algorithms and the well-known Iris dataset, we develop a model capable of accurately identifying the species of new flowers.
The Iris Flower Classification project provides an excellent opportunity to dive into the world of machine learning and explore classification techniques. With the Iris dataset, which contains measurements of petal and sepal lengths for three different iris flower species (Setosa, Versicolor, and Virginica), we aim to build a robust model for accurate species prediction.
- Clone the repository:
git clone https://github.com/veeramanikandanr48/iris-flower-classification.git - Install the required dependencies by running:
pip install -r requirements.txt
- Navigate to the project directory:
cd iris-flower-classification - Launch the Jupyter notebook:
jupyter notebook iris_classification.ipynb - Follow the instructions provided in the notebook to explore the dataset, perform data preprocessing, train the model, and make predictions.
We welcome contributions from the community to enhance the project. To contribute, please follow these steps:
- Fork the repository.
- Create a new branch:
git checkout -b feature/your-feature - Implement your changes and commit them:
git commit -m 'Add your feature' - Push the changes to the branch:
git push origin feature/your-feature - Submit a pull request.
We appreciate your contributions and value the collaborative spirit of the open-source community.
Let's work together to create a powerful and accurate flower classification model! 🌸🔬