This document compares the performance of K-means clustering and K-nearest neighbor (K-NN) classification for imputing missing values. It finds that K-NN performs better than K-means clustering in terms of accuracy when imputing missing values at rates from 2% to 20%. The document simulates datasets with various missing value rates, uses each method to group the data and impute missing values via mean substitution, and compares the results to the original complete dataset to calculate accuracy. K-NN achieved an average accuracy of 67% compared to 62% for K-means clustering across the different missing value rates tested.