DS - Dimensionality reduction

Data Science with Python

Welcome to the Dimensionality Reduction Workshop as part of the Data Science with Python Series. In this workshop we will cover the basics of dimensionality reduction and introduce you to Random Forests, Principle Component Analysis and t-distributed Stochastic Neighbor Embedding. This will cover the basics of dimensinoality reduction and allow you to implement it in your own workflow.

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 - Dimensionality Reduction
│   ├── README.md
│   ├── Data
│   │   ├── NBA_tot.txt
│   ├── problem.ipynb
│   ├── solution.ipynb
    └── workshop.ipynb