Top 6 methods to convert Categorical Data | One Hot Encoder | Ordinal Encoding | Binary Encoding

Опубликовано: 25 Июль 2026
на канале: Moredatascientists
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https://www.moredatascientists.com/# In this video, you will learn

How to implement One Hot Encoder?
How to implement Ordinal Encoder?
How to implement Binary Encoder?
How to implement Backward Differencing Encoder?
How to implement Hash Encoder?
How to implement Target Encoder?
What are categorical variables?
What are the different types of categorical variables?


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#dataanalytics #deeplearning

Categorical data is a type of data that consists of categories or labels. It is one of the most important types of data used in machine learning. Categorical data can be used to make predictions and decisions, and can also be used to measure relationships between different variables. In this video, I will discuss how to convert categorical data in Scikit Learn.



Types of categorical variables:

1. Nominal data: These are variables that have two or more categories, with no intrinsic ordering to the categories. Examples include gender, zip code, and eye color.

2. Ordinal data: These are variables that have two or more categories with an intrinsic ordering. Examples include educational level (e.g. high school, college, graduate school) and satisfaction rating (e.g. very satisfied, satisfied, neutral, dissatisfied, very dissatisfied).

3. Binary data: These are variables that have only two categories. Examples include yes/no responses, male/female, and alive/deceased.


These variables can be encoded using a variety of methods, we are going to learn about the following Encoding methods in this video.

1. Ordinal Encoding
2. One- Hot Encoding
3. Binary Encoding
4. Backward Differencing Encoding
5. Hash Encoding
6. Target Encoding


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