Kmeans

Kmeans is a compact clustering repo that combines a from-scratch Python implementation with notebook-based experiments in unsupervised learning and image segmentation.

Best Ways To Explore It

• Open kmeans.ipynb in Colab
• Open image_segmentation.ipynb in Colab

Those notebook links are the fastest way to review or run the project from any computer without local setup.

What Is In The Repo

• kmeans.py from-scratch K-means implementation
• kmeans.ipynb notebook exploration of clustering behavior
• image_segmentation.ipynb image segmentation experiment using clustering

What This Repo Demonstrates

• unsupervised learning fundamentals
• practical clustering workflows
• image segmentation with K-means
• translating algorithm concepts into working Python code

Run Locally

pip install numpy matplotlib scikit-learn jupyterlab
jupyter notebook

Then open either notebook, or inspect kmeans.py directly.

Status

This is a lightweight learning and experimentation repo rather than a production application. It is best experienced through the notebooks above.