K-means Clustering From Scratch

My implementation of K-means clustering algorithm from scratch and using it for PNG image compression. To get a deeper understanding I've decided not to use any famous libraries like Tensorflow, Pytorch, etc. But, used the NumPy library for Vectorization and Pillow to read images.
I have also implemented the cost/distortion function and logic to run K-means up to provided 'iters' and return the best values out of 'iters' iterations.

Instructions:

1. Install python3 on your systems.
2. Use below commands to install the required packages:

   python3 -m pip install --upgrade pip
   python3 -m pip install --upgrade Pillow
   python3 -m pip install --upgrade matplotlib
   python3 -m pip install --upgrade jupyterlab

3. Run jupyter-lab to open/run image_compression_kmeans.ipynb file.

References:

1. Machine Learning Specialization by Andew Ng on Coursera.
2. Main notes from Stanford CS229 course.

Output:

Note: All images are downloaded from Pixabay and are used according to the license.