Data preparation for machine learning requires business domain expertise, bias awareness and an experimental thought process. Before preparing your data, you’ll first define a business problem solve. During that exercise, you’ll select an outcome metric and brainstorm potential input variables that influence it from many varied perspectives. From there you will begin identifying, collecting, cleaning, shaping and sampling data to run through automated machine learning model processes.
Note that it is also not unusual for relevant machine learning input data to occur outside of existing transactional processes. If that is the case, you can still start creating a first-generation machine learning model with existing data and continue to build new model versions over time as supplementary data is acquired.
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