Encoding Technique in Machine Learning | Label, One-Hot, Ordinal, Target, Frequency & Custom Encoder

Struggling with categorical variables in your machine learning models? This video has you covered! 💡

In this tutorial, we’ll explore *all major encoding techniques* used in preprocessing categorical data for machine learning. From the basics to some advanced custom methods, you'll learn *when, why, and how to use* each encoder — with practical code demos and real-world examples.

🚀 What You’ll Learn:
Label Encoding: Best for ordinal relationships
One-Hot Encoding: Making categories binary
Ordinal Encoding: Handling ranked categories
Target Encoding: Using the target variable to encode
Frequency Encoding: Encode based on frequency of categories
Custom Encoders: Tailored solutions for unique data scenarios

🛠️ Hands-On Implementation in Python:
We’ll use Scikit-learn and pandas to implement each technique and discuss the *pros, cons, and ideal use cases* of every encoding method.

🎯 Perfect for:
Data Science & ML beginners
Those preparing for interviews
Anyone struggling with data preprocessing

👨‍💻 Stay ahead in your ML journey and master the art of encoding with clarity and confidence!

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Let me know in the comments which encoding technique you use the most or find most confusing — I’d love to help! 💬