The wrong way to start your machine learning project is to “chuck everything into a model to see what happens”. The better way is to visualise your data to expose the relationships that you expect, to confirm that your data looks correct and to identify problems that are likely to make your life difficult.
We’ll review ways to quickly and visually diagnose your data, to check it meets your assumptions and to prepare it for discussion with your colleagues. We’ll look at tools including Pandas, Seaborn and Pandas Profiling. At the end you’ll have new tools to help you confidently investigate new data with your associates.