This repository contains Python code for visualizing Principal Component Analysis using Manim. Principal Component Analysis is a powerful statistical technique used for dimensionality reduction and feature extraction in data analysis. The visualization provided offer detailed insights into the workings of PCA, illustrating how correlated variables are transformed into uncorrelated principal components, capturing the maximum variance in the data.
Make sure your version of Python >= 3.10 and you have installed all the necessary packages which can be found in the Manim installation guide.
To install the packages run:
pip install -r requirements.txtTo start rendering videos please check out the Manim quickstart guide.
- André-Anan Gilbert (3465546)
- Jan-Henrik Bertrand (8556462)
- Felix Noll (9467152)
- Marc Grün (9603221)