PCA_2_components

Jupyter notebook for Principal component analysis (PCA).

PCA is used in exploratory data analysis and for making predictive models (https://en.wikipedia.org/wiki/Predictive_modeling "Predictive modeling"). It is commonly used for dimensionality reduction (https://en.wikipedia.org/wiki/Dimensionality_reduction "Dimensionality reduction") by projecting each data point onto only the first few principal components to obtain lower-dimensional data while preserving as much of the data's variation as possible.

- Wikipedia

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Using this notbook:

• Create the environment from the environment.yml file: conda env create -f environment.yml

• This will create an envitroment called: PCA2D

• Activate the newly created enviroment with: conda activate PCA2D

• Run jupyter-lab jupyter lab

Things most likely will break due to Jupyter widgets if that is the case follow the installation guide here: https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Events.html