Summary
In this chapter, we saw how multivariate analysis allows you to visualize how two or more variables interact with each other. We explored this through scatter plots using both simple x and y dimensions but later adding in color and an optional z dimension for 3D scatter plots.
We finished the chapter by talking about the Pearson correlation and showing how you can calculate a correlation score between two columns of numeric data. Finally, we saw how many such correlations can be visualized in a correlation matrix to help you spot unusual associations in your data.
This concludes the first part of this book, where we’ve explored the basics of Polyglot Notebooks and applied them to data wrangling and data analysis workloads.
In the next part of the book, we’ll move on to new territories as we discuss machine learning and the process of training, evaluating, and deploying machine learning models.
