Iris Flower Classification

Project Overview

This project is part of my Data Science internship at CodeAlpha. The goal was to create a machine learning model that classifies Iris flowers into three species: Setosa, Versicolor, and Virginica, based on their sepal and petal measurements.

Tools & Technologies

• Language: Python
• Environment: Kaggle Notebooks
• Libraries: Pandas, Seaborn, Matplotlib, Scikit-learn

Data Visualization

I used a Pairplot to visualize the relationship between the features. You can see the Iris_pairplot.png file in this repository to view the clear clusters formed by the different species.

Model & Results

• Algorithm: K-Nearest Neighbors (KNN)
• Accuracy: [1.0]
• Summary: The model performs exceptionally well, especially in identifying the Setosa species, which is clearly separated from the others in the feature space.

Files in this Repository

• iris-flower-dataset.ipynb: The full Python code and analysis.
• Iris_pairplot.png: Visualization of the dataset clusters.
• README.md: Project documentation.