Data Pre-Processing | Encoding Categorical Data in Machine Learning
Welcome to this video on Encoding Categorical Data in Machine Learning, part of our ML series using the Titanic dataset!
In this session, you will learn:
Why encoding is necessary for ML models
Label Encoding: Ideal for binary or ordinal features
One-Hot Encoding: Best for nominal features
Hands-on demo with the Titanic dataset in Python
Differences between LabelEncoder and get_dummies()
When and why to use drop_first=True
📌 What we cover:
Sex column → Label Encoding
Embarked column → One-Hot Encoding
Final encoded dataset walkthrough
✅ Perfect for beginners in data preprocessing and machine learning!
👉 Don’t forget to like, share & subscribe for the full ML course series!
📂 Titanic dataset source: https://www.kaggle.com/c/titanic/data
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