Summary
In this chapter, you added R and Python visuals to your Power BI reports to discover new features in the FAA Wildlife Strike data. Using an R correlation plot, you were able to interactively slice and dice several incident flag values for positive and negative correlations. With Python histograms you took a look at the impact of speed and height on the outcomes for your planned Power BI ML models. Finally, you added new features to your Predict Damage, Predict Size, and Predict Height ML queries that will be used for ML in Power BI.
In the next chapter, you will begin migrating content to the Power BI cloud service. After migrating the Power BI dataset and report, you will then migrate the Power Query scripts to dataflows for use with Power BI ML.
