Does it support feature scaling?

Absolutely, it provides automated tools for normalization and standardization to prevent features with larger ranges from dominating the model.

How does this skill help improve model accuracy?

It creates new predictive features from existing data, such as interaction terms, which help the model capture complex relationships more effectively.

What feature selection methods does it use?

It generates code for popular methods like Random Forest importance and SelectKBest to identify and retain only the most relevant data attributes.

Can it handle categorical data?

Yes, it automates encoding processes like one-hot encoding and label encoding to prepare non-numeric data for machine learning algorithms.

Is the generated code ready for production?

Yes, Claude generates Python code using the feature-engineering-toolkit with built-in data validation and error handling for robust implementation.

Machine Learning Feature Engineering Toolkit

Name: Machine Learning Feature Engineering Toolkit
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Automates the creation, selection, and transformation of data features to optimize machine learning model performance and interpretability.

About

This skill empowers Claude to act as an automated data scientist, streamlining the feature engineering lifecycle by generating robust Python code for feature extraction, selection, and transformation. It identifies relevant patterns in raw data, reduces model complexity through importance-based selection, and ensures data is properly scaled and encoded for various algorithms. Whether you're dealing with churn prediction or fraud detection, this tool integrates seamlessly with your ML workflow to improve model accuracy and efficiency while providing actionable insights into feature importance.

Key Features

Automated encoding for categorical variables (one-hot, label encoding)
Intelligent feature selection using importance scoring and SelectKBest
Built-in data validation and cleaning for ML-ready datasets
Automated feature creation and interaction term generation
Comprehensive data transformation including scaling and normalization
3 GitHub stars

Use Cases

Reducing fraud detection model latency by selecting the top 10 most impactful features
Standardizing raw datasets for neural network training through scaling and encoding
Improving churn prediction accuracy by creating interaction terms from customer demographics

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add bbgnsurftech/claude-skills-collection feature-engineering-toolkit

For use in Claude.ai and ChatGPT

Download Skill

GitHub