![]() ![]() I hope you understand that the need justified the means and that you learn something along the way. Here is the real-life story on how I created fake variables with an existing dataset. This is a problem when you want to teach people how to use tools like Association Analysis or Scatter Plot. … except there aren’t many continuous variables in this data set. ![]() I wanted to use a cool animal shelter data set published by the Austin Animal Shelter, everything was going pretty well… The course was Pre-Predictive: Fundamentals of Data Investigation, something close to my heart, and a class where the training data matters. Recently, I was working on developing training content for an Inspire course. Hopefully, I can start sleeping well again once I get this out in the open (kidding). This post is 50% instructional, 50% confessional. What I am saying is that sometimes when you need to run a demo, test functionality of a tool or script, or run a training session, you might need too-good-to-be-true-fakey-fake data.Īnd that’s where this blog post comes in. I’m not encouraging using synthetic data for creating models to put into production or pitching something to your boss with fake data, because that is pointless and generally a bad idea. ![]() You may not have the time or resources to acquire the data right away but need something to ask as a placeholder, you may not be able to use data for testing or demos due to privacy concerns, or you may want data that looks a specific way to prove a point. Sometimes, you need to synthesize fake data. ![]()
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