Building upon the univariate and bivariate analysis techniques covered previously, this chapter expands our analytical toolkit. We will first examine visualizations designed to reveal patterns among multiple variables at once, like pair plots. You will also learn how to refine these plots for better communication.

Following visualization, we transition to an introduction to feature engineering. This involves using the understanding gained during EDA to modify existing features or create new ones. We will cover practical techniques such as data scaling, normalization, and encoding categorical data, which are often necessary steps before applying machine learning algorithms. Finally, we address how to structure and present the insights gathered throughout the EDA process.