you can see here that it did its best given that it had to draw straight linesand polygonal shapesit did decent job of fitting to the data that we had soyou knowit did miss few but by and largethe results are pretty good svc is actually very powerful techniqueit' real strength is in higher dimensional feature data go ahead and play with it by the way if you want to not just visualize the resultsyou can use the qsfejdu function on the svc modeljust like on pretty much any model in scikit-learnto pass in feature array that you're interested in if want to predict the classification for someone making $ , year who was years oldi would use the following codetwdqsfejdu this would put that person inin our casecluster number if had someone making $ , here who was would use the following codetwdqsfejdu this is what your output should now look likethat person would end up in cluster number whatever that represents in this example sogo ahead and play with it activity nowlinear is just one of many kernels that you can uselike said there are many different kernels you can use one of them is polynomial modelso you might want to play with that please do go ahead and look up the documentation it' good practice for you to looking at the docs if you're going to be using scikit-learn in any sort of depththere' lot of different capabilities and options that you have available to you sogo look up scikitlearn onlinefind out what the other kernels are for the svc methodand try them outsee if you actually get better results or not