kaggle-feature-engineering

Generate groupby, interaction, and target encoding features for Kaggle competitions.

1|Updated Jun 6, 2026

Search Tags:#data science #feature engineering #model optimization #kaggle #gpu acceleration

Ready to run

npx skills add https://github.com/OlixIgnacious/agent-skills --skill kaggle-feature-engineering

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: kaggle-feature-engineering
Source: https://github.com/OlixIgnacious/agent-skills/tree/main/.claude/skills/kaggle-feature-engineering
Command: npx skills add https://github.com/OlixIgnacious/agent-skills --skill kaggle-feature-engineering

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, cupy, cudf, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps address the complex task of feature engineering in Kaggle competitions, allowing users to efficiently generate and optimize features that improve model performance.

Core Features & Use Cases

Groupby Aggregations: Automates the creation of group-based features.
Interaction Features: Generates interaction terms for feature engineering.
Target Encoding: Implements CV-safe target encoding for categorical features.
Time Series Features: Generates lag and rolling features for time series data.
Feature Selection: Facilitates feature selection after each batch to enhance model performance.
GPU Acceleration: Utilizes GPU acceleration for efficient feature generation.
Use Case: This Skill is ideal for Kaggle competitors looking to build models for tabular data problems. After setting up baseline models, it helps iteratively refine features for better CV performance.

Quick Start

Run the skill by executing /kaggle-feature-engineering command.

Frequently Asked Questions about kaggle-feature-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate groupby aggregations and target encoding for Kaggle tabular data?▼

Automate groupby aggregations and target encoding for Kaggle tabular data by running /kaggle-feature-engineering to generate group-based, interaction, and CV-safe categorical features.

Can I use GPU acceleration for feature engineering with pandas and cuDF?▼

Yes, you can use GPU acceleration for feature engineering with pandas and cuDF. The Skill utilizes pandas, cupy, and cuDF dependencies to accelerate feature generation for large datasets.

What's the best way to generate time series lag and rolling features for machine learning competitions?▼

Generate time series lag and rolling features for machine learning competitions by executing the Skill, which builds temporal features specifically designed to improve model cross-validation performance.

Do I need a GPU to run kaggle-feature-engineering for interaction feature generation?▼

You do not strictly need a GPU to run kaggle-feature-engineering for interaction feature generation, but having GPU support via cupy and cuDF significantly accelerates the feature creation process.

How does CV-safe target encoding work for categorical features in Kaggle competitions?▼

CV-safe target encoding for categorical features in Kaggle competitions works by transforming categories using out-of-fold target means, preventing data leakage and improving model generalization.

Why does feature selection matter after batch feature generation in model optimization?▼

Feature selection matters after batch feature generation in model optimization because the Skill facilitates selecting relevant features post-batch, reducing dimensionality and enhancing model performance.