Handle Categorical Data in Pandas | Label Encoding & One Hot Encoding for Categorical Data Devduniya
Welcome to Devduniya! In this video, we dive into the world of handling categorical data in Pandas, one of the most popular data manipulation libraries in Python. Categorical data is a common type of data encountered in various domains, and it requires special treatment to be effectively utilized in machine learning models.
In this tutorial, we focus on two widely used techniques for encoding categorical data: Label Encoding and One Hot Encoding. You'll learn the concepts behind each method and understand when to use them based on your specific dataset and problem.
Label Encoding is a process that assigns unique numeric labels to each category in a categorical feature. We'll explore how to implement Label Encoding using Pandas, and we'll discuss its advantages and limitations.
Next, we'll dive into One Hot Encoding, a technique that creates binary columns for each category in a categorical feature. We'll demonstrate how to apply One Hot Encoding using Pandas, and we'll discuss how it handles scenarios where multiple categories can be present simultaneously.
Handle Categorical Data in Pandas | Label Encoding & One Hot Encoding for Categorical Data Devduniya
The below topics are explained in this Machine Learning basics video:
1. Intro ( 00:00 )
2. Handle Categorical Data ( 01:00 )
3. One Hot Encoding ( 06:56 )
4. Label Encoding ( 10:10 )
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QUERIES SOLVED:
▪ Handle Categorical Data in Pandas
▪ Label Encoding with Code
▪ One Hot Encoding with Code
▪ Pandas tutorial
▪ Pandas Playlist in HINDI
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