#TABULAR DATA EXAMPLES HOW TO#
#TABULAR DATA EXAMPLES GENERATOR#

of MIT, and it is an open source project⁵. To produce highly realistic tabular data, we will use conditional generative adversarial networks - CTGAN⁴. Different distributions: multimodal, long tail, non-gaussian.Mixed data types: numerical, categorical, time, text.Tabular GAN:ĭeveloping a general-purpose GAN that would reliably work for a tabular dataset is not a straightforward task. al¹ to see the mathematical concept behind the GAN.

#TABULAR DATA EXAMPLES GENERATOR#

But as training progresses, the generator learns to get progressively better at generating fake data, and fooling the discriminator, until the discriminator is unable to tell if the input is real or not. At the start of the training, the generator is not very good at generating fake data, and the discriminator is able to catch the fake data easily. The two models compete against each other in a zero-sum game that drives the whole system towards optimization.

A discriminator that learns to distinguish the generator’s fake data from the real data.

A generator that learns to produce fake data.

As a result, the generated records contain none of the original data itself, but retains the real dataset’s original underlying probability distribution. After the probability distribution has been learned, the model can then generate synthetic records by randomly sampling from the distribution¹. Wondering why generative models could be an ideal method to employ for creating synthetic data? Well, in generative models, a neural network (NN) is used to approximate the underlying probability distribution of an input data in a high-dimensional latent space. The ability to use synthetic datasets where sensitive attributes and Personally Identifiable Information (PII) are not disclosed, is crucial for staying compliant with privacy regulations, and convenient for data analysis, sharing, and experimenting. Enormous amounts of data, such as clinical trial records, financial data, census results, are all represented in tabular format. Tabular data is one of the most common and important data modalities.

#TABULAR DATA EXAMPLES HOW TO#

In this article, we will show you how to generate synthetic tabular data using a generative adversarial network (GAN). In the previous article, we introduced the concept of synthetic data and its applications in data privacy and machine learning. Photo by Hayden Dunsel on Unsplash Introduction

Tabular data examples