gingado 0.0.2.dev0

A machine learning library for economics and finance

Welcome to gingado!

A machine learning library for economics and finance

gingado seeks to facilitate the use of machine learning in economic and finance use cases, while promoting good practices. gingado aims to be suitable for beginners and advanced users alike.

Overview

gingado is a free, open source library built different functionalities:

• data augmentation, to add more data from official sources, improving the machine models being trained by the user;
• automatic benchmark model, to enable the user to assess their models against a reasonably well-performant model;
• (new!) relevant datasets, both real and simulamed, to allow for easier model development and comparison;
• support for model documentation, to embed documentation and ethical considerations in the model development phase; and
• utils, including tools to allow for lagging variables in a straightforward way.

Each of these functionalities builds on top of the previous one. They can be used on a stand-alone basis, together, or even as part of a larger pipeline from data input to model training to documentation! {% include tip.html content='New functionalities are planned over time, so consider checking frequently on gingado for the latest toolsets.' %}

Design principles

The choices made during development of gingado derive from the following principles, in no particular order:

• flexibility: users can use gingado out of the box or build custom processes on top of it
• compatibility: gingado works well with other widely used libraries in machine learning, such as scikit-learn and pandas
• responsibility: gingado facilitates and promotes model documentation, including ethical considerations, as part of the machine learning development workflow

Acknowledgements

gingado's API is inspired on the following libraries:

• scikit-learn (API description)
• keras (website here and also, this essay)
• fastai (description here)

In addition, gingado is developed and maintained using nbdev.

Presentations, talks, papers

The most current version of the paper describing gingado is here. The paper and other material about gingado (ie, slide decks, papers) in this dedicated repository. Interested users are welcome to visit the repository and comment on the drafts or slide decks, preferably by opening an issue. I also store in this repository suggestions I receive as issues, so users can see what others commented (anonymously unless requested) and comment along as well!

Install

To install gingado, simply run the following code on the terminal:

$ pip install gingado