Skip to content

Overview

FeatureByte's SDK Tutorials

Welcome to the Credit Default SDK Tutorials.

What You'll Learn

This tutorial series walks you through creating a catalog, registering its data model, formulating your use case, crafting features, computing training data, training model and deploying and managing those features. All using FeatureByte SDK.

For a holistic view of FeatureByte's open-source platform, along with insights into the overall workflow and the intricacies of the SDK, please head over to our documentation main page and explore the workflow and SDK overview sections.

Dataset Overview

In this tutorial, we'll use the Credit Default Dataset, which contains transactional data from related to credit default prediction. It consists of six tables:

  • NEW_APPLICATION: Records new loan applications.
  • PRIOR_APPLICATION: Contains data on prior loan applications.
  • CONSUMER_LOAN_STATUS: Tracks consumer loans status.
  • CONSUMER_INSTALLMENTS: Logs monthly installments for consumer loans at the time of payment.
  • CASH_LAON_STATUS: Tracks cash loans status.
  • CASH_INSTALLMENTS: Logs monthly installments for cash loans at the time of payment.

The tutorials cover the first four tables, while the last two (Cash_loan_status and Cash_installments) are left for you to explore.

Credit default dataset

Note

If you are interested in a use case that exploits item table, checkout out the Grocery SDK Tutorials.

Getting Started

  • For Practitioners: If you aim to run the notebooks and immerse yourself in the end-to-end workflow, please follow the instructions for the tutorials installation and execute each notebook in sequence.

  • For Readers: If you're here just to read and understand, feel free to jump to any section of your interest.

End-to-End Workflow

1. Create catalog

Define the Data Model of the catalog

2. Register tables

3. Register entities

4. Update descriptions to tables (optional)

5. Set Default Cleaning Operations

Formulate your use case

6. Formulate Use Case

7. Create Observation Tables

Create features

8. Create Lookup Features

9. Create Window Aggregates from Event Table

10. Create Features from SCD

11. Create Calendar Window Aggregates from Time Series

Compute training data and train model for your use case

12. Create Feature List

13. Compute Historical Feature Values

14. Train LGBM

Deploy your features

15. Deploy and serve a feature list

Download the tutorials here

Download all the Credit Default Tutorial notebooks here