nowit will find the centroids of each cluster based on the existing cluster members we need to repeat these steps until convergence occurs random forest it is supervised classification algorithm the advantage of random forest algorithm is that it can be used for both classification and regression kind of problems basically it is the collection of decision trees ( forestor you can say ensemble of the decision trees the basic concept of random forest is that each tree gives classification and the forest chooses the best classifications from them followings are the advantages of random forest algorithmrandom forest classifier can be used for both classification and regression tasks they can handle the missing values it won' over fit the model even if we have more number of trees in the forest