Order the following steps for performing classification on the Iris dataset using a nearest-neighbor approach.

First, load the Iris dataset and split it into a training set and a test set. Then, for each test sample, compute the distance to every training sample using a chosen metric such as Euclidean distance. Next, identify the nearest training sample (or a set of nearest neighbors if using k‑NN) and assign the test sample the label of that nearest neighbor. Finally, evaluate the model by comparing the predicted labels to the true labels of the test set to measure accuracy. This sequence of loading, splitting, distance calculation, neighbor selection, and evaluation completes the nearest‑neighbor classification pipeline.