Overview
Feature engineering is the process of using domain knowledge to extract features from raw data that make machine learning algorithms work better.
Good feature engineering can significantly improve model performance, sometimes more than algorithm optimization.
Key Concepts
- Feature creation and transformation
- Categorical encoding techniques
- Binning and discretization
- Feature scaling methods
- Dimensionality reduction
Practice Exercise
Exercise: Feature Engineering Challenge
Using the Titanic dataset:
- Create a new feature 'FamilySize' by combining SibSp and Parch
- Extract title from passenger names (Mr, Mrs, Miss, etc.)
- Create age groups by binning the Age feature
- Encode categorical variables appropriately
- Create a correlation matrix to see relationships between features
Resources
Kaggle Feature Engineering
Main resource for today
Feature Engineering for Machine Learning
Comprehensive guide to feature engineering
Categorical Encoding
Different techniques for encoding categorical variables
Feature Scaling
When and how to scale features
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