Introduction

Welcome to Eskapade! This is a short introduction of the package and why we built it. In the next sections we will go over the installation, a tutorial on how to run Eskapade properly, some examples use-cases, and how to develop analysis code and run it in Jupyter and PyCharm.

What is Eskapade?

Eskapade is an abbreviation for: 'Enterprise Solution KPMG Advanced Predictive Analytics Decision Engine'. It is a framework to make your data analytics modular. This results in faster roll-out of analytics solutions in your business and less overhead when taking multiple analyses in production. In particular, it is intended for building Machine Learning models that are retrained when a certain trigger is reached.

Why did we build this?

We found that the implementation phase of a data analytics solution at clients - a careful process - is often completely different from building the solution itself - which proceeds through explorative iterations. Therefore we made this analysis framework that makes it easier to set up a data analysis, while simultaneously making it easier to put it into production.

Next to that, it makes analyses modular, which has a lot of advantages. It is easier to work with multiple people on the same project, because the contributions are divided in clear blocks. Re-use of code becomes more straightforward, as old code is already put in a block with a clear purpose. Lastly, it gives you a universal basis for all your analyses, which can both be used across a company, or for different clients.

More about the purpose can be read at the general readme.

Naming convention

Before settling on the name Eskapade, this project had internal naming conventions including Decision Engine and Analytics Engine. We are working on removing all old names and streamlining this all into future versions, but one might still find these in certain parts of the repository.