Feature engineering for machine learning: encoding, scaling, transformations, datetime features, text features, and feature selection. Use when preparing data for modeling or improving model performance through better representations.
Quickly install data-science-feature-engineering AI skill to your development environment via command line
Source: legout/data-platform-agent-skills.
Use this skill for creating, transforming, and selecting features that improve model performance.
| sklearn pipelines | sklearn.pipeline + ColumnTransformer | Reproducible, cross-validation safe | | Categorical encoding | categoryencoders | Beyond sklearn's limited options | | Feature selection | sklearn.featureselection | Mutual info, RFE, SelectFromModel | | Text embeddings | sentence-transformers | Pre-trained semantic embeddings |
| Auto feature engineering | Feature-engine | Comprehensive transformations |
Feature engineering for machine learning: encoding, scaling, transformations, datetime features, text features, and feature selection. Use when preparing data for modeling or improving model performance through better representations. Source: legout/data-platform-agent-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/legout/data-platform-agent-skills --skill data-science-feature-engineeringFeature engineering for machine learning: encoding, scaling, transformations, datetime features, text features, and feature selection. Use when preparing data for modeling or improving model performance through better representations. Source: legout/data-platform-agent-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/legout/data-platform-agent-skills --skill data-science-feature-engineering Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/legout/data-platform-agent-skills