Agent skill · data analytics · aj-geddes
Feature Engineering
Create and transform features using encoding, scaling, polynomial features, and domain-specific transformations for improved model performance and interpretability
Why this skill is useful
Provides domain-specific transformations and techniques for feature engineering that improve model performance, which the AI may not reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, scikit-learn, seaborn installed locally. About 3k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Feature Engineering Overview Feature engineering creates and transforms features to improve model performance, interpretability, and generalization through domain knowledge and mathematical transformations. When to Use When you need to improve model performance beyond using raw features When dealing with categorical variables that need encoding for ML algorithms When features have different scales and require normalization When creating domain-specific features based on business knowledge When handling skewed distributions or non-linear relationships When preparing data for different types of ML algorithms with specific requirements Engineering Techniques Encoding: Converting categorical to numerical Scaling: Normalizing feature ranges Polynomial Features: Higher-order terms Interactions: Combining features Domain-specific: Business-relevant transformations Temporal: Time-based features Key Principles Create features based on domain knowledge Remove redundant features Scale features appropriately Handle categorical variables Create meaningful interactions Implementation with Python Best Practices Understand your domain before engineering features Create features that are interpretable Avoid data leakage (using future information) Test feature importance after engineering Document all transformations Use appropriate scaling for different algorithms Common Transformations Log Transform: For skewed distributions Polynomial Features: For non-linear relationships Interaction Terms: For combined effects Binning: For categorical approximation Normalization: For comparison across scales Deliverables Engineered feature dataset Feature transformation documentation Correlation analysis of new features Distribution comparisons (before/after) Feature importance rankings Preprocessing pipeline code Data dictionary with feature descriptions
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/feature-engineering