Data Preparation and Transformation

You have probably heard that data scientists spend most of their time working on data-preparation-related activities. It is now time to explain why that happens and what types of activities they work on.

In this chapter, you will learn how to deal with categorical and numerical features, as well as how to apply different techniques to transform your data, such as one-hot encoding, binary encoders, ordinal encoding, binning, and text transformations. You will also learn how to handle missing values and outliers in your data, which are two important tasks you can implement to build good machine learning (ML) models.

In this chapter, you will cover the following topics:

• Identifying types of features
• Dealing with categorical features
• Dealing with numerical features
• Understanding data distributions
• Handling missing values
• Dealing with outliers
• Dealing with unbalanced datasets
• Dealing with text data

This...