Make Your Own Synthetic Data Project with this Step-by-Step Guide — Part 1
Tutorial, Code, Pro Tips, and more!
Synthetic data is increasingly gaining traction in the field of data science. Why? As our need to analyze more extensive and diverse datasets grows to create valuable solutions, we often encounter sensitive, private, or legally restricted data.
Take medical data, for instance. Imagine a scenario where there’s a database containing patient records, holding invaluable insights that could be utilized to extract patterns, draw conclusions, gain insights, or develop useful models. However, access to such data is often restricted to authorized medical personnel for privacy and legal reasons. So, how do we preserve the privacy of this data while still leveraging it for analysis and solution creation?
The answer lies in synthetic data.
By generating entirely fabricated datasets mirroring the correlations and relationships of real data but with different identifiers, synthetic data provides a privacy-preserving solution. These fabricated datasets represent the characteristics of real data, allowing for analysis without compromising the privacy of the original data.
