One of the main aims of EDA is to prepare your dataset to develop a useful model capable of characterizing sensed data. To create such models, we first need to understand the dataset. If our data set is labeled, we will be performing supervised learning tasks, and if our data is unlabeled, then we will be performing unsupervised learning tasks. Moreover, once we create these models, we need to quantify how effective our model is. We can do this by performing several evaluations on these models. In this section, We are going to discuss in-depth how to use EDA for model development and evaluation. The main objective of this section is to allow you to use EDA techniques on real datasets, prepare different types of models, and evaluate them.

This section contains the following chapters:

• Chapter 9, Hypothesis Testing and Regression
• Chapter 10, Model Development and Evaluation
• Chapter 11, EDA on Wine Quality Data