  Data collection often results in records that have missing values or
variables. This investigation compares 3 different data imputation models and
identifies their merits by using accuracy measures. Autoencoder Neural
Networks, Principal components and Support Vector regression are used for
prediction and combined with a genetic algorithm to then impute missing
variables. The use of PCA improves the overall performance of the autoencoder
network while the use of support vector regression shows promising potential
for future investigation. Accuracies of up to 97.4 % on imputation of some of
the variables were achieved.
