/KNN-For-Iris-Flower-Classification

Primary LanguageJupyter Notebook

Iris Flower Classification using K-Nearest Neighbors (KNN)

Iris Flowers

Overview

This project implements the K-Nearest Neighbors (KNN) algorithm to classify Iris flowers based on their sepal and petal measurements. The Iris dataset, a standard in machine learning, provides data for three species: Setosa, Versicolor, and Virginica.

Key Features

  • Methodology: Implemented in Python, utilizing Pandas and NumPy for data handling, and Scikit-learn for machine learning tasks.
  • Model Training: Trains a KNN classifier with n_neighbors=3 to predict Iris species.
  • Performance Evaluation: Includes a confusion matrix and classification report to assess model accuracy.

Usage

Clone the repository and install dependencies:

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

Run the main script to classify Iris flowers:

python iris_classification.py

Contributing

Contributions are welcome! Fork the repository and submit pull requests for enhancements.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

Feel free to reach out via LinkedIn for any inquiries or collaborations.