pros and cons of knn pros it is very simple algorithm to understand and interpret it is very useful for nonlinear data because there is no assumption about data in this algorithm it is versatile algorithm as we can use it for classification as well as regression it has relatively high accuracy but there are much better supervised learning models than knn cons it is computationally bit expensive algorithm because it stores all the training data high memory storage required as compared to other supervised learning algorithms prediction is slow in case of big it is very sensitive to the scale of data as well as irrelevant features applications of knn the following are some of the areas in which knn can be applied successfullybanking system knn can be used in banking system to predict weather an individual is fit for loan approvaldoes that individual have the characteristics similar to the defaulters onecalculating credit ratings knn algorithms can be used to find an individual' credit rating by comparing with the persons having similar traits politics with the help of knn algorithmswe can classify potential voter into various classes like "will vote""will not vote""will vote to party 'congress'"will vote to party 'bjpother areas in which knn algorithm can be used are speech recognitionhandwriting detectionimage recognition and video recognition