oqbssbz oqbssbz sfuvso  please note that because we're using supervised learning herewe not only need the feature data againbut we also need the actual answers for our training dataset what the dsfbuf$mvtufsfe%bub function does hereis to create bunch of random data for people that are clustered around pointsbased on age and incomeand it returns two arrays the first array is the feature arraythat we're calling and then we have the array of the thing we're trying to predict forwhich we're calling lot of times in scikitlearn when you're creating model that you can predict fromthose are the two inputs that it will takea list of feature vectorsand the thing that you're trying to predictthat it can learn from sowe'll go ahead and run that so now we're going to use the dsfbuf$mvtufsfe%bub function to create random people with different clusters we will just create scatter plot to illustrate thoseand see where they land upnbuqmpumjcjomjof gspnqzmbcjnqpsu  dsfbuf$mvtufsfe%bub qmugjhvsf gjhtj[ qmutdbuufs   btuzqf oqgmpbu qmutipx the following graph shows our data that we're playing with every time you run this you're going to get different set of clusters soyou knowi didn' actually use random seed to make life interesting couple of new things here-- ' using the gjhtj[ parameter on qmugjhvsf to actually make larger plot soif you ever need to adjust the size in nbuqmpumjcthat' how you do it ' using that same trick of using the color as the classification number that end up with so the number of the cluster that started with is being plotted as the color of these data points you can seeit' pretty challenging problemthere' definitely some intermingling of clusters here