Iris Flower Classification with k-NN

Introduction

This project demonstrates the implementation of the k-Nearest Neighbors (k-NN) algorithm, a popular machine learning algorithm, using the famous Iris Flower dataset. The goal is to classify different types of iris flowers based on their features such as sepal length, sepal width, petal length, and petal width.

Dataset

The Iris Flower dataset contains 150 records, each representing an iris flower belonging to one of three species:

1. Iris Setosa (Class 0)
2. Iris Versicolor (Class 1)
3. Iris Virginica (Class 2)

Each flower has four features:

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

The dataset is loaded from two .npy files:

• irises.npy: Contains the feature data of the flowers.
• types.npy: Contains the class labels for the flowers.

Features

• Algorithm: k-Nearest Neighbors (k-NN)
• Data: Processed from the Iris Flower dataset
• Language: Python
• Libraries: Numpy, SciPy

Running the Notebook

To run the code, follow these steps:

1. Make sure you have installed the required dependencies by running:

   pip install numpy scipy

2. Open the Jupyter notebook iris_flowers.ipynb.
3. Run all the cells to load the dataset, process the data, and classify the iris flowers using the k-NN algorithm.

Output

The notebook prints out the predicted classes of the iris flowers, compares them with the actual class labels, and calculates the accuracy at the end.

Contribution

If you'd like to contribute to this project, feel free to open a pull request or report issues in the issue tracker