data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous
columns. Continuous columns may have multiple modes whereas discrete columns are
sometimes imbalanced making the modeling difficult. Existing statistical and deep neural
network models fail to properly model this type of data. We design CTGAN, which uses a
conditional generative adversarial network to address these challenges. To aid in a fair and …