DS - Kmeans clustering workshop

Data Science with Python

Welcome to the Kmeans clustering Workshop as part of the Data Science with Python Series. In this workshop we will cover the basics of implementing a kmeans clustring algorithm, how to choose the optimal number of clusters and how to evaluat the clusters that you end up with.

Author: Philip Wilkinson, Head of Science (21/22) UCL Data Science Society (philip.wilkinson.19@ucl.ac.uk)

Requirements

Prior to this lecture please install

• Anaconda
• Git
• DS01, DS02, DS03

Proudly presented by the UCL Data Science Society

Structure

├── DS - Data Science with Python - Kmeans clustering
│   ├── README.md
│   ├── Data
│   │   ├── Convenience_stores_dataset.gpkg
│   │   ├── London_outline.gpkg
│   ├── problem.ipynb
│   ├── solution.ipynb
    └── workshop.ipynb