facial recognition is another example soif have dataset of facesmaybe each face represents third dimension of imagesand want to boil that downsvd and principal component analysis can be way to identify the features that really count in face soit might end up focusing more on the eyes and the mouthfor examplethose important features that are necessary for preserving the variance within that dataset soit can produce some very interesting and very useful results that just emerge naturally out of the datawhich is kind of coolto make it realwe're going to use simpler exampleusing what' called the iris dataset this is dataset that' included with scikit-learn it' used pretty commonly in examplesand here' the idea behind itsoan iris actually has different kinds of petals on its flower one' called petalwhich is the flower petals you're familiar withand it also has something called sepalwhich is kind of this supportive lower set of petals on the flower we can take bunch of different species of irisand measure the petal length and widthand the sepal length and width sotogether the length and width of the petaland the length and width of the sepal are different measurements that correspond to different dimensions in our dataset want to use that to classify what species an iris might belong to nowpca will let us visualize this in dimensions instead of while still preserving the variance in that dataset solet' see how well that works and actually write some python code to make pca happen on the iris dataset sothose were the concepts of dimensionality reductionprincipal component analysisand singular value decomposition all big fancy words and yeahit is kind of fancy thing you knowwe're dealing with reducing higher dimensional spaces down to smaller dimensional spaces in way that preserves their variance fortunatelyscikit-learn makes this extremely easy to dolike lines of code is all you need to actually apply pca so let' make that happena pca example with the iris dataset let' apply principal component analysis to the iris dataset this is dataset that we're going to reduce down to dimensions we're going to see that we can actually still preserve most of the information in that dataseteven by throwing away half of the dimensions it' pretty cool stuffand it' pretty simple too let' dive in and do some principal component analysis and cure the curse of dimensionality go ahead and open up the $"jqzoc file