Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data (Paperback)

Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data (Paperback)margin: 0px 0px 14px; padding: 0px; font family: "Amazon Ember", Arial, sans serif; font size: 14px;'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 datafake data generated from real dataso you can perform secondary analysis to do research, understand customer behaviors,

margin: 0px 0px 14px; padding: 0px; font-family: "Amazon Ember", Arial, sans-serif; font-size: 14px;'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 datafake data generated from real dataso you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue.'margin: -4px 0px 14px; padding: 0px; font-family: "Amazon Ember", Arial, sans-serif; font-size: 14px;'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.'margin: -4px 0px 14px; padding: 0px; font-family: "Amazon Ember", Arial, sans-serif; font-size: 14px;'This book describes: