Skip to main content

Tutorial 4A: Building a regression model for loan prediction with H2O Driverless AI

Overview​

In this tutorial, you will learn how to use H2O Driverless AI to build a regression model to predict loan amounts. We will walk through the steps to load data, set up an experiment, and evaluate results using interpretability tools.

Objectives​

Determining the appropriate loan amount to approve for applicants is a critical challenge for the banking sector. Banks must consider a wide range of factors, including the applicant's income, credit history, employment details, and other financial indicators, when predicting loan amounts. An inaccurate prediction can lead to either overestimating the loan amount or underestimating it, resulting in customer dissatisfaction.

Consider a bank that processes thousands of loan applications every day. The goal is to approve loans quickly while ensuring that the approved amount aligns with the applicant's repayment capacity. Manually processing these applications is slow and error-prone.

This tutorial addresses the challenge of predicting loan amounts by leveraging H2O Driverless AI. You will learn how to build a robust regression model to predict loan amounts. By following this tutorial, you will gain hands-on experience in preparing data, configuring a machine learning experiment, interpreting model results, and optimizing predictive performance using H2O Driverless AI.

This tutorial focuses on solving the problem of accurately predicting loan amounts using an automated machine learning approach. Specifically, it aims to:

  • Simplify the process of data preparation, feature engineering, and model selection using H2O Driverless AI.
  • Build a reliable regression model that can predict loan amounts with high precision, catering to the needs of financial decision-makers.
  • Identify and interpret the variables that significantly impact loan predictions.
  • Equip you with the knowledge and skills to use H2O Driverless AI effectively for solving real-world regression problems.

By the end of the tutorial, you will have a working model for loan prediction and a deeper understanding of the tools and techniques required to tackle similar predictive tasks in different domains.

Prerequisites​

  • Access to the Lab 4 in Aquarium containing H2O Driverless AI v1.10.7 (LTS).
  • Familiarity with H2O Driverless AI is essential. Alternatively, completion of the following tutorial is also acceptable: Tutorial 1A: Introduction to H2O Driverless AI.
Feedback