12 Frequency encoding -Categorical variable encoding - Python Machine Learning AI Data preprocessing
In this video, we delve into the world of categorical data encoding, focusing on the Frequency Encoding technique. Whether you're a beginner in data science or looking to expand your understanding of encoding methods, this video will provide you with a comprehensive guide to frequency encoding. We'll break down the key concepts and walk you through step-by-step Python implementations. Plus, you'll discover how to apply frequency encoding to your datasets and understand when it's the best method for your machine learning workflows.
Check out other videos in our series, covering 37 different encoding methods, grouped into 8 categories, and learn how to choose the right encoding technique with a comparative analysis using standard datasets.
What you'll learn in this video:
• Detailed explanation of frequency encoding
• Python code implementations using:
o Custom logic with Pandas
o category_encoders library
o feature-engine library
• When and why to use frequency encoding for your machine learning models
This video is part of a larger series on advanced categorical encoding methods. Subscribe and turn on notifications to stay updated as we release more tutorials on all 38 encoding techniques!
Methods covered in this series:
1. Basic Encoding Techniques:
o One-hot encoding
o Label encoding
o Ordinal encoding
2. Target-Based Encoding:
o Target Encoding (Mean Encoding)
o Leave-One-Out Encoding
o M-estimator Encoding
o Probability Ratio Encoding
3. Frequency or Count-Based Encoding:
o Frequency Encoding
o Count Encoding
4. Binary and Hash Encoding Methods:
o Binary Encoding
o Hash Encoding
5. Mathematical or Statistical Encoding:
o Effect Encoding (Deviation Encoding)
o Polynomial Encoding
o Weight of Evidence (WOE) Encoding
o CatBoost Encoding
6. Decision Tree-Based Encoding:
o Decision Tree Encoding
7. Special Scenarios Encoding:
o Rare Category Encoding
o Time-based Encoding
8. Advanced Encoding:
o Facet Encoding
o Helmert Encoding
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Keywords: frequency encoding, categorical encoding, data preprocessing, machine learning, data science, encoding techniques, feature engineering, python coding, frequency-based encoding, data handling, feature encoding in python, real-world examples, AI, machine learning tutorial, python tutorial, supervised learning, frequency encoding comparison, machine learning models, encoding methods, categorical variables, encoding in machine learning, data transformation, encoding guide
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