A principal component analysis step-by-step

Posted to Statistics  |  Tags:  |  Nathan Yau

Sebastian Raschka offers a step-by-step tutorial for a principal component analysis in Python.

The main purposes of a principal component analysis are the analysis of data to identify patterns and finding patterns to reduce the dimensions of the dataset with minimal loss of information.

Here, our desired outcome of the principal component analysis is to project a feature space (our dataset consisting of n x d-dimensional samples) onto a smaller subspace that represents our data “well”. A possible application would be a pattern classification task, where we want to reduce the computational costs and the error of parameter estimation by reducing the number of dimensions of our feature space by extracting a subspace that describes our data “best”.

That is, imagine you have a dataset with a lot of variables, some of them important and some of them not so much. A PCA helps you identify which is which, so the source doesn’t seem so unwieldy or to reduce overhead.

Favorites

Interactive: When Do Americans Leave For Work?

We don’t all start our work days at the same time, despite what morning rush hour might have you think.

This is an American Workday, By Occupation

I simulated a day for employed Americans to see when and where they work.

Shifting Incomes for American Jobs

For various occupations, the difference between the person who makes the most and the one who makes the least can be significant.

The Most Unisex Names in US History

Moving on from the most trendy names in US history, let’s look at the most unisex ones. Some names have …