Now let’s start designing the ML.NET data transformation pipeline. This is the sequence of feature engineering steps that will transform the dataset into something suitable for a machine learning algorithm to train on.
After completing the previous lessons, you should have a pretty good idea which feature engineering steps are needed to get this dataset ready for machine learning training.
You’re already performing these transformations:
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