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feature-engineering-kit

feature-engineering-kit

github.com/dkyazzentwatwaData & databases12458 installsUpdated 29w ago
Repository

About feature-engineering-kit

Feature Engineering Kit is a data-preparation skill for transforming datasets with encoding, scaling, and feature generation. It covers tabular, date, and text features, as well as missing-value handling.

Key features

  1. 01One-hot, label, and target encoding
  2. 02Standard, min-max, and robust scaling
  3. 03Generate polynomial interactions
  4. 04Discretize continuous values
  5. 05Extract date features
  6. 06Create text features and impute missing values

Use cases

  1. 01Prepare categorical data for modeling
  2. 02Scale numeric columns
  3. 03Generate date and text features
  4. 04Handle missing values

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill feature-engineering-kit

After installing, type/feature-engineering-kitin Claude Code to confirm it loaded.

Bundled filesscripts/feature_engineering.pyscripts/requirements.txt

Details

From the project README

Feature Engineering Kit

Automated feature engineering with encodings, scaling, and transformations.

Features

  • Encodings: One-hot, label, target encoding
  • Scaling: Standard, min-max, robust scaling
  • Polynomial Features: Generate interactions
  • Binning: Discretize continuous features
  • Date Features: Extract time-based features
  • Text Features: TF-IDF, word counts
  • Missing Value Handling: Imputation strategies

CLI Usage

python feature_engineering.py --data train.csv --output engineered.csv --config config.json

Dependencies

  • scikit-learn>=1.3.0
  • pandas>=2.0.0
  • numpy>=1.24.0

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