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Loans (nb intro)

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Predicting the loan default risk of Czech bank customers using getML

Introduction to relational learning with getML

This notebook demonstrates the application of our relational learning algorithm to predict if a customer of a bank will default on his loan. We train the predictor on customer metadata, transaction history, as well as other successful and unsuccessful loans.

Summary:

  • Prediction type: Binary classification
  • Domain: Finance
  • Prediction target: Loan default
  • Source data: 8 tables, 78.8 MB
  • Population size: 682

Author: Dr. Johannes King, Dr. Patrick Urbanke

Background

This notebook features a textbook example of predictive analytics applied to the financial sector. A loan is the lending of money to companies or individuals. Banks grant loans in exchange for the promise of repayment. Loan default is defined as the failure to meet this legal obligation, for example, when a home buyer fails to make a mortgage payment. A bank needs to estimate the risk it carries when granting loans to potentially non-performing customers.

The analysis is based on the financial dataset from the the CTU Prague Relational Learning Repository (Motl and Schulte, 2015).

Related code example

Notebook:
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