it' actually very easy to do using scikit-learnas usualagainpca is dimensionality reduction technique it sounds very science-fictionyall this talk of higher dimensions butjust to make it more concrete and real againa common application is image compression you can think of an image of black and white pictureas dimensionswhere you have widthas your -axisand your -axis of heightand each individual cell has some brightness value on scale of to that is black or whiteor some value in between sothat would be datayou have spatial dimensionsand then brightness and intensity dimension on top of that if you were to distill that down to say dimensions alonethat would be compressed image andif you were to do that in technique that preserved the variance in that image as well as possibleyou could still reconstruct the imagewithout whole lot of loss in theory sothat' dimensionality reductiondistilled down to practical example nowwe're going to use different example here using the iris datasetand scikit-learn includes this all it is is dataset of various iris flower measurementsand the species classification for each iris in that dataset and it has alsolike said beforethe length and width measurement of both the petal and the sepal for each iris specimen sobetween the length and width of the petaland the length and width of the sepal we have dimensions of feature data in our dataset we want to distill that down to something we can actually look at and understandbecause your mind doesn' deal with dimensions very wellbut you can look at dimensions on piece of paper pretty easily let' go ahead and load that upgspntlmfbsoebubtfutjnqpsumpbe@jsjt gspntlmfbsoefdpnqptjujpojnqpsu $jnqpsuqzmbcbtqm gspnjufsuppmtjnqpsudzdmf jsjt mpbe@jsjt ovn bnqmftovn'fbuvsft jsjtebubtibqf qsjouovn bnqmft qsjouovn'fbuvsft qsjoumjtu jsjtubshfu@obnft