Skip to content

Latest commit

 

History

History
10 lines (9 loc) · 474 Bytes

README.md

File metadata and controls

10 lines (9 loc) · 474 Bytes

Principal Component Analysis

This repository looks at dimensionality reduction techniques. It contains the code to a principal component analysis and to an autoencoder, both using the wine dataset.

Getting started

Start by cloning the Git repository:

git clone https://github.com/lena-will/pca.git

Data

I use the wine dataset from UCI's machine learning repository. The data and description can be found here: https://archive.ics.uci.edu/ml/datasets/wine