Set of notebooks going deeper on understanding some classical approaches on exploratory data analysis. All notebooks can executed on a virtual environment in https://mybinder.org/v2/gh/caiomiyashiro/exploratory_data_analysis/master
Principal Component Analysis is a unsupervised machine learning technique used for multivariable exploratory data analysis and dimensionality reduction.
Even though is a well established technique, its foundations are still explained in broken parts in the internet. This tutorial intends to join all the theoritical side and show how it connects with the practical applications.
The notebook depends on a few python packages. If you want to rerun it, please install the required packages on "requirements.txt"