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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 label encoding?

We all know that the machine learning algorithm can only deal with numerical features. However, in most scenarios, categorical features are prevalent. For example, a person's gender, address, product type, weather, and so on are categorical features. To ensure the machine learning algorithm can use this information (features or fields), we need to transform these categorical features into numerical features. **Label encoding** is one of the methods used for this transformation. 

In short, **label encoding** is simply converting each value of a column to a number like the image shown below.

>**Notice**: The encoding operation must be performed simultaneously on both the train and test datasets. Otherwise, it may cause inconsistencies in the encoding of the same data in different datasets.

Learn how to encode categorical features with the label encoding method.

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Label Encoding

What is label encoding?