with different kernels you might get different results svc with linear kernel will produce something very much as you see in the preceding image you can use polynomial kernels or fancier kernels that might project down to curves in two dimensions as shown in the image you can do some pretty fancy classification this way these have increasing computational costsand they can produce more complex relationships but againit' case where too much complexity can yield misleading resultsso you need to be careful and actually use train/test when appropriate since we are doing supervised learningyou can actually do train/test and find the right model that worksor maybe use an ensemble approach you need to arrive at the right kernel for the task at hand for things like polynomial svcwhat' the right degree polynomial to useeven things like linear svc will have different parameters associated with them that you might need to optimize for this will make more sense with real exampleso let' dive into some actual python code and see how it worksusing svm to cluster people by using scikitlearn let' try out some support vector machines here fortunatelyit' lot easier to use than it is to understand we're going to go back to the same example used for -means clusteringwhere ' going to create some fabricated cluster data about ages and incomes of hundred random people if you want to go back to the -means clustering sectionyou can learn more about kind of the idea behind this code that generates the fake data and if you're readyplease consider the following codejnqpsuovnqzbtoq $sfbufgblfjodpnfbhfdmvtufstgps/qfpqmfjoldmvtufst efgdsfbuf$mvtufsfe%bub / qpjout fs$mvtufs gmpbu  gpsjjosbohf jodpnf$fouspje oqsboepnvojgpsn bhf$fouspje oqsboepnvojgpsn gpskjosbohf jou qpjout fs$mvtufs bqqfoe <oqsboepnopsnbm jodpnf$fouspjeoqsboepnopsnbm bhf$fouspje bqqfoe