Скачать или смотреть Encoding Categorical Variables in Python | Chapter 7 Machine Learning Tutorial

Welcome to Chapter 7 of our Machine Learning tutorial series using Scikit-Learn. In this video, we focus on **encoding categorical variables**, a critical preprocessing step that converts non-numerical data into a format machine learning algorithms can understand. Many real-world datasets contain text-based or category-based features, and proper encoding is essential for building accurate and efficient models.

Topics covered in this chapter include:

1. Understanding Categorical Variables
What categorical data is and why machine learning models cannot work with raw text labels.
The difference between nominal and ordinal categorical data.

2. LabelEncoder
Learn how LabelEncoder converts categories into numerical labels.
Understand when LabelEncoder is appropriate and when it can cause unintended ordering issues.
Practical examples using simple datasets.

3. OneHotEncoder
Learn how OneHotEncoder creates binary columns for each category.
Understand why One-Hot Encoding is commonly used for nominal data.
Explore how OneHotEncoder avoids false relationships between categories.

4. OrdinalEncoder
Learn how OrdinalEncoder preserves meaningful order in categorical variables.
Understand real-world use cases such as ratings, levels, and rankings.

5. Practical Examples
Apply each encoding method to sample datasets.
Compare results and understand how encoding affects model training and performance.

6. Best Practices
How to choose the correct encoder based on data type and problem context.
Common mistakes to avoid when encoding categorical variables.

By the end of this chapter, you will clearly understand how to encode categorical data using Scikit-Learn, choose the right encoding technique, and prepare your dataset correctly for machine learning workflows. Proper encoding improves model accuracy and prevents misleading predictions.

Useful Links:
GitHub: https://github.com/Ezee-Kits/
YouTube:    / @ezee_kits  
Email: [email protected]

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