In this chapter, we discussed how to use explanatory analysis to draw insight on customer behavior. We discussed how regression analysis can be used to dive deeper into understanding customer behavior. More specifically, you learned how to use logistic regression to understand what attributes of customers drive higher engagement rates. In Python and R exercises, we employed the descriptive analysis that we covered in Chapter 2, Key Performance Indicators and Visualizations, as well as regression analysis for explanatory analysis. We started the exercises by analyzing the data in order to better understand and identify noticeable patterns in the data. While analyzing the data, you learned one additional way to visualize the data, through box plots, using the matplotlib and pandas packages in Python and the ggplot2 library in R.
While fitting regression models, we discussed...
