  This paper proposes a new method to generate synthetic data sets based on
copula models. Our goal is to produce surrogate data resembling real data in
terms of marginal and joint distributions. We present a complete and reliable
algorithm for generating a synthetic data set comprising numeric or categorical
variables. Applying our methodology to two datasets shows better performance
compared to other methods such as SMOTE and autoencoders.
