Overview

Loan Applications Dataset User Interface Tutorials

Welcome to the FeatureByte Enterprise User Interface!

Our UI platform offers a user-friendly, no-code interface that builds on the strong foundation of our SDK.

Beyond core SDK functionalities, FeatureByte UI introduces powerful tools that enhance your feature engineering workflow, including:

• Feature Ideation for guided features discovery,
• a Self-Organized Catalog for seamless feature reuse,
• and a structured, Governed Approval Flow for effective feature management.

What You'll Learn

This tutorial series walks you through the entire process, step by step. You’ll learn how to:

• Create a catalog.
• Define your data model.
• Structure your use cases.
• Use Feature Ideation to generate relevant features.
• Build feature lists
• Compute training data
• Manage feature deployment

Note

For a deeper introduction to FeatureByte’s Approval Flow and version control, checkout out the Grocery UI Tutorial "Manage Feature Life Cycle".

Dataset Overview

In this tutorial, we'll work with the Loan Applications Dataset, which provides transactional and historical data for predicting loan defaults. The dataset is composof of seven interconnected tables:

• NEW_APPLICATION: Contains information about new loan applications submitted by clients.
• CLIENT_PROFILE: Describes the profile for each client.
• BUREAU: Lists all previous credits taken by clients from other financial institutions, as reported to the credit bureau.
• PREVIOUS_APPLICATION: Details prior loan applications made by the client.
• INSTALLMENTS_PAYMENTS: Logs monthly installments for loans at the time of payment.
• LOAN_STATUS: Captures the current status of each loan over time.
• CREDIT_CARD_MONTHLY_BALANCE: Provides monthly balance summaries for credit cards previously held by the client with the institution.

Note

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

Tutorial Structure

This tutorial follows a structured, end-to-end workflow:

1. Create Catalog

Define the Data Model of the catalog

2. Register Tables

3. Register Entities

4. Set Default Cleaning Operations

5. Update Descriptions and Tag Semantics

Formulate your use case

6. Formulate Use Case

7. Create Observation Tables

Ideate features

8. Ideate Features

8b. Refine Ideation

Compute training data for your use case

9. Create New Feature List

10. Compute Feature Table

Deploy features

11. Deploy and Serve a Feature List

Note

For a deeper introduction to FeatureByte’s Approval Flow and version control, checkout out the Grocery UI Tutorials.