GeeseTools 0.1.6

Modular and extensible data preprocessing library

🪿 Goose

Modular and Extensible Data Preprocessing Library for Machine Learning

Goose is a plug-and-play, mixin-based Python library that streamlines the preprocessing of tabular datasets for machine learning tasks. Whether you’re cleaning messy data, encoding categories, transforming skewed distributions, or scaling features — this package has you covered.

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🚀 Features

• 🧼 Handle missing data
• 🔢 Convert object columns to numeric
• 🔍 Identify feature types (categorical, ordinal, nominal, etc.)
• ⚙️ Encode nominal and ordinal features
• 🔄 Transform skewed and heavy-tailed features
• 📏 Scale features with standard or power transformations
• 🧪 Train-test split with optional oversampling
• 📊 Transformation logs for transparency and reproducibility
• 🔌 Built using Mixins for modular extension

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📂 Installation

You can install the package directly from Test PyPI:

pip install --index-url https://test.pypi.org/simple/ Goose

Or, after building your wheel file (.whl) from the source:

pip install dist/Goose-0.1.3-py3-none-any.whl

Or install directly in editable mode (for development):

pip install -e .

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🧪 Usage

import Goose as Goose

# Instantiate with a dataset
obj = Goose(
    dataframe=df,
    target_variable='target',
    ordinal_features=['education_level'],
    ordinal_categories=[['Low', 'Medium', 'High']],
    use_one_hot_encoding=True
)

# Apply full preprocessing pipeline
X_train, X_test, y_train, y_test = obj.pre_process()

# Access logs
print(obj.transformation_log_df)

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🗂 Default Sample Dataset

If no DataFrame is provided, the processor loads a built-in heart.csv dataset:

obj = Goose()  # Uses sample heart dataset

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📁 Project Structure

src/
│
├── Goose/
│   ├── Goose.py                  # Main class
│   ├── mixins/                 # Modular preprocessing logic
│   ├── data/heart.csv          # Default dataset
│   ├── datasets.py             # Heart dataset loader
│   └── __init__.py

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⚙️ Requirements

• Python 3.9–3.11
• pandas
• scikit-learn
• imbalanced-learn
• scipy
• ipython
• openpyxl

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📜 License

MIT © Abhijeet
You're free to use, modify, and distribute this project with proper attribution.

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✨ Contributions Welcome

Want to add new mixins or support more file types? Fork it, branch it, push it, and let’s build together!