This project explores unsupervised learning using the K-Means clustering algorithm on the Iris dataset.
The primary goal of this project was to understand the complete workflow of clustering, including:
- How K-Means forms clusters
- The role of centroid initialization
- Understanding inertia
- Evaluating clustering quality using Silhouette Score
- Analyzing the effect of PCA on clustering performance
The implementation was intentionally done step-by-step to build strong conceptual understanding of K-Means rather than using high-level abstractions.
This project uses the built-in Iris dataset provided by Scikit-learn.
- 150 samples
- 4 numerical features:
- Sepal Length
- Sepal Width
- Petal Length
- Petal Width
- 3 species (used only for reference; clustering itself is unsupervised)
from sklearn.datasets import load_iris
