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This demo shows how to apply common transformations when preparing data for machine learning The following topics are covered:

  1. One-hot encoding
  2. Binning
  3. Integerizing categories
  4. Balancing unbalanced data
  5. Splitting into training and test data

""Trifacta brought an entirely new level of productivity to the way our analyst and IT teams explore diverse data and define analytic requirements. Our users can intuitively and collaboratively prepare the growing variety of data that makes up PepsiCo’s analytic initiatives.""

""We were actually able to shave the amount of time it took to do the analysis by [a factor of] six. Rather than having to do a tremendous amount of analysis, we’re actually readily able to start getting incremental data products out quickly.""