feature-engine

Funding Links: https://github.com/sponsors/feature-engine

Name: Feature-engine
Kind: organization
Followers: 56
Following: 0
Total stars: 1940
Repositories count: 4
Created at: 2022-11-15T19:57:53.399Z
Updated at: 2025-03-06T15:51:51.750Z
Last synced at: 2025-03-06T15:51:51.750Z

GitHub Sponsors Profile

Call for Sponsors

We are calling for sponsors to support the active development and maintenance of Feature-engine.
Our development goals

Expand the library’s functionality to include support for time series, text and datetime variables.
Expand the library's functionality with further alternatives for feature creation, transformation and selection.
Continue maintaining a high-quality, well-documented collection of canonical tools for data processing.
Expand the documentation with more examples about Feature-engine’s functionality.
Expand the documentation with more detail on how to contribute to the package.

More details about the direction of the project and coming functionality can be found in our roadmap.
Why sponsor Feature-engine
Feature-engine is growing in popularity, and the number of contributions is increasing steadily. In addition to the work to expand the library's functionality as per our roadmap, code contributions are regularly reviewed by core developers. As the code base grows, we also carry out regular maintenance and refactoring of our code base, to ensure code performance and maintain and improve readability.
Maintaining and enhancing an open-source project like Feature-engine, requires the committed work of 1-2 developers that dedicate a minimum of 8 hours per week to the project.
As a first step, we would like to raise funds to support 1 core developer to commit 8 weekly hours of work to support and expand Feature-engine's functionality.
About Feature-engine
Feature-engine is an open source Python library to engineer and select features for use in machine learning models. Feature-engine preserves Scikit-learn functionality, with methods fit() and transform() for learning parameters from and then transforming the data.
Feature-engine includes transformers for:

Missing data imputation
Categorical encoding
Discretisation
Variable transformation
Outlier handling
Variable creation
Variable selection
Features for time series forecasting

and much more...
With extensive API and user guide documentation, Feature-engine provides both ready-to-use code to transform the data for use in machine learning and guidelines on when, why and how to use each transformation.
Thank you very much, and we look forward to your support!
Please note
We do not accept sponsorship from fossil fuel companies.