want to point out that svm is supervised learning technique sowe're actually going to train it on set of training dataand we can use that to make predictions for future unseen data or test data it' little bit different than -means clustering and that -means was completely unsupervisedwith support vector machineby contrastit is training based on actual training data where you have the answer of the correct classification for some set of data that it can learn from sosvm' are useful for classification and clusteringif you will but it' supervised techniqueone example that you often see with svms is using something called support vector classification the typical example uses the iris dataset which is one of the sample datasets that comes with scikit-learn this set is classification of different flowersdifferent observations of different iris flowers and their species the idea is to classify these using information about the length and width of the petal on each flowerand the length and width of the sepal of each flower (the sepalapparentlyis little support structure underneath the petal didn' know that until now either you have four dimensions of attributes thereyou have the length and width of the petaland the length and the width of the sepal you can use that to predict the species of an iris given that information here' an example of doing that with svcbasicallywe have sepal width and sepal length projected down to two dimensions so we can actually visualize it