In this group project done with classmate Sid Shapiro, we analyzed the Iris data using a k-nearest neighbor classifier. We scaled the data using the mean and standard deviation, then visualized it using Principal Components Analysis. Next we determined an optimal value for the number of nearest neighbors using a 5-fold cross validation study. Finally, we built our classifier and reported the results. We used 8 nearest neighbors, and found an accuracy of 100% using our technique.