please go ahead and open up the ,'pme$sptt bmjebujpojqzoc and follow along if you will we're going to look at the iris dataset againremember we introduced this when we talk about dimensionality reductionjust to refresh your memorythe iris dataset contains set of iris flower measurementswhere each flower has length and width of its petaland length and width of its sepal we also know which one of different species of iris each flower belongs to the challenge here is to create model that can successfully predict the species of an iris flowerjust given the length and width of its petal and sepal solet' go ahead and do that we're going to use the svc model if you remember back againthat' just way of classifying data that' pretty robust there' section on that if you need to go and refresh your memoryjnqpsuovnqzbtoq gspntlmfbsojnqpsudsptt@wbmjebujpo gspntlmfbsojnqpsuebubtfut gspntlmfbsojnqpsutwn jsjt ebubtfutmpbe@jsjt  qmjuuifjsjtebubjoupusbjouftuebubtfutxjui sftfswfegpsuftujoh @usbjo @uftu @usbjo @uftu dsptt@wbmjebujpousbjo@uftu@tqmju jsjtebubjsjtubshfuuftu@tj[ sboepn@tubuf #vjmebo $npefmgpsqsfejdujohjsjtdmbttjgjdbujpot vtjohusbjojohebub dmg twn lfsofm mjofbs gju @usbjo @usbjo /pxnfbtvsfjutqfsgpsnbodfxjuiuifuftuebub dmgtdpsf @uftu @uftu what we do is use the dsptt@wbmjebujpo library from scikit-learnand we start by just doing conventional train test splitjust single train/test splitand see how that will work to do that we have usbjo@uftu@tqmju function that makes it pretty easy sothe way this works is we feed into usbjo@uftu@tqmju set of feature data jsjtebub just contains all the actual measurements of each flower jsjtubshfu is basically the thing we're trying to predict