Develop a k-nearest neighbor (kNN)

 Q2. The attached Excel file Iris.xlsx lists a dataset that consists of samples from each of three flower species of Iris (Iris setosa, Iris virginica and Iris versicolor) and 4 attributes – Petal Length, Petal Width, Sepal Length, Sepal width. The dataset has been partitioned to training and testing set. Develop a k-nearest neighbor (kNN) model that predicts the class of the test sets given the attribute information. Assume that the model uses Euclidean distance to find the nearest neighbor using k=1. Show all the calculation steps in Excel for the prediction the testing set classes. Develop a confusion matrix of the predicted and actual class. Calculate overall classifier accuracy from the confusion matrix. Do not use Weka or rattle for this exercise, use only Excel. Upload the Excel file showing all the calculation details. 

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