Feature Engineering for Existing Features.png

Feature engineering is a crucial stage in any machine learning project. It allows you to use data to define features that enable machine learning algorithms to work properly.

In this course, you will learn the techniques that will help you create new features from existing features. You’ll start by diving into label encoding which is crucial for converting categorical features into numerical. You’ll also learn about other various types of encoding such as: one-hot, count, and mean, all of which are important for feature engineering. 

In the remaining chapters, you’ll learn about feature interaction and datetime features. In all, this course will show you the many different ways you can create features from existing ones.

Feature Engineering for Machine Learning

## What is count encoding?

**Count encoding** uses the number of categories in the dataset as the new feature. For example, in the column `Type`, the category value `Sun` appears 3 times, then the new feature for the `Sun` is 3.

# What is count encoding?

**Count encoding** uses the number of categories in the dataset as the new feature. For example, in the column `Type`, the category value `Sun` appears 3 times, then the new feature for the `Sun` is 3.

Learn how to use count encoding to encode categorical features.

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Count Encoding

What is count encoding?