Categorical Encoding Sklearn | Label Encoder | Ordinal Encoder | OneHot Encoder | Sklearn tutorials
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In this fourth tutorial of our Sklearn series, we delve into the world of categorical encoding. Categorical data is commonly encountered in various machine learning projects, and it requires special treatment to be effectively used in models. In this video, we explore two popular categorical encoding techniques - Label Encoder and Ordinal Encoder.
We start by understanding what categorical data is and why it's important in machine learning.
Next, we introduce the Label Encoder, a straightforward technique to convert categorical labels into numeric representations.
We demonstrate how to use the Label Encoder with real-world examples and datasets.
Moving on, we dive into the Ordinal Encoder, which allows us to handle ordinal categorical data with meaningful orderings.
We discuss the differences between nominal and ordinal data and how the Ordinal Encoder preserves the ordinality of the categories.
Practical examples illustrate the usage of the Ordinal Encoder and its effectiveness in handling ordered categorical data.
Whether you're new to machine learning or looking to enhance your data preprocessing skills, this tutorial will equip you with essential knowledge on categorical encoding techniques. Join us in this insightful exploration of Label Encoder and Ordinal Encoder!
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