Understanding Categorical Encoding Techniques: Ordinal, One-Hot, and Label Encoding

Introduction: Categorical variables are an essential part of data analysis, but they cannot be directly processed by machine learning models. To address this, we use various encoding techniques to convert categorical data into numerical form. In this blog post, we will explore three popular encoding methods: Ordinal Encoding, One-Hot Encoding, and Label Encoding.

What is Categorical Data?

Categorical data refers to a type of data that represents specific categories or groups. It is a type of data that is non-numerical and consists of labels or qualitative values rather than numerical values. Categorical data is often represented by text or symbols and can be divided into different distinct groups or categories. In machine learning, categorical data is typically represented using the “object” or “string” data type.

Here are a few examples of categorical data:

Gender: Categorical variable with categories such as “Male” and “Female.”

Marital Status: Categorical variable with categories such as “Married” and “Single.”

Education Level: Categorical variable with categories such as “High School,” “Bachelor’s Degree,” “Master’s Degree,” etc.