Summary

In this chapter, we started by talking about why data exploration is an important part of the modeling process and how it can help in not only preprocessing the dataset for the modeling process, but also help us engineer informative features and improve model accuracy. This chapter focused on not only gaining a basic overview of the dataset and its features, but also gaining insights by creating visualizations that combine several features.

We looked at how to find the summary statistics of a dataset using core functionality from pandas. We looked at how to find missing values and talked about why they're important, while learning how to use the Missingno library to analyze them and the pandas and scikit-learn libraries to impute the missing values.

Then, we looked at how to study the univariate distributions of variables in the dataset and visualize them for both categorical and continuous variables using bar charts, pie charts, and histograms. Lastly, we learned how to explore relationships...