there' handy dandy mpbe@jsjt function built into scikit-learn that will just load that up for you with no additional workso you can just focus on the interesting part let' take look at what that dataset looks likethe output of the preceding code is as followsyou can see that we are extracting the shape of that datasetwhich means how many data points we have in itthat is and how many featuresor how many dimensions that dataset hasand that is sowe have iris specimens in our datasetwith dimensions of information againthat is the length and width of the sepaland the length and width of the petalfor total of featureswhich we can think of as dimensions we can also print out the list of target names in this datasetwhich are the classificationsand we can see that each iris belongs to one of three different speciessetosaversicoloror virginica that' the data that we're working with iris specimensclassified into one of speciesand we have features associated with each iris let' look at how easy pca is even though it' very complicated technique under the hooddoing it is just few lines of code we're going to assign the entire iris dataset and we're going to call it we will then create pca modeland we're going to keep @dpnqpofout because we want dimensionsthat iswe're going to go from to we're going to use xijufo svfthat means that we're going to normalize all the dataand make sure that everything is nice and comparable normally you will want to do that to get good results thenwe will fit the pca model to our iris dataset we can use that model thento transform that dataset down to dimensions let' go ahead and run that it happened pretty quickly jsjtebub qdb $ @dpnqpofout xijufo svf gju @qdb qdbusbotgpsn please think about what just happened there we actually created pca model to reduce dimensions down to and it did that by choosing vectorsto create hyperplanes aroundto project that data down to dimensions you can actually see what those vectors arethose eigenvectorsby printing out the actual components of pca sopca stands for principal component analysisthose principal components are the eigenvectors that we chose to define our planes aboutqsjouqdbdpnqpofout