Building and testing machine learning models requires access to
large and diverse data. But where can you find usable datasets
without running into privacy issues? This practical book introduces
techniques for generating synthetic data-fake data generated from
real data-so you can perform secondary analysis to do research,
understand customer behaviors, develop new products, or generate
new revenue Data scientists will learn how synthetic data
generation provides a way to make such data broadly available for
secondary purposes while addressing many privacy concerns. Analysts
will learn the principles and steps for generating synthetic data
from real datasets. And business leaders will see how synthetic
data can help accelerate time to a product or solution. This book
describes: Steps for generating synthetic data using multivariate
normal distributions Methods for distribution fitting covering
different goodness-of-fit metrics How to replicate the simple
structure of original data An approach for modeling data structure
to consider complex relationships Multiple approaches and metrics
you can use to assess data utility How analysis performed on real
data can be replicated with synthetic data Privacy implications of
synthetic data and methods to assess identity disclosure
General
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