One-Hot vs Label Encoding | ML Interview Question Explained | #designurthought #shorts

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. 👍 Like, comment, and share if this helped! 💬 Got a topic you want simplified? Drop it below. #OneHotEncoding #LabelEncoding #MachineLearning #DataPreprocessing #FeatureEngineering #MLShorts #DesignUrThought #MLForBeginners #MLInterview #DataScienceTips #AIExplained #MLSimplified