One-Hot vs Label Encoding Explained: Choose the Right Method for Your ML Model 🤖

One-Hot vs Label Encoding — which one is right for your machine learning model?
In this short, beginner-friendly video from DesignUrThought, we break down the core differences between these two popular encoding methods and explain when to use each — with easy, real-life examples.

💡 If you're struggling with categorical data in your ML pipeline, this is a must-watch. One wrong move in encoding can lead your model to learn false patterns — and destroy your model's accuracy.

🔍 In this video, you'll learn:
What is Label Encoding and where it goes wrong

What is One-Hot Encoding and why it's safer for most models

How encoding impacts different algorithms (Linear Models vs Decision Trees)

Real-world use case: Cities like New York vs London

A clear answer to the question: When should I use One-Hot vs Label Encoding?

🎓 Whether you're preparing for a machine learning interview, building your first ML model, or just brushing up on feature engineering, @designUrThought here to make ML concepts clear, simple, and practical.

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