Use Logistic Regression in Credit Scoring

Contributed by: Brindha Priyadarshini Jeyaraman

Difficulty level: Intermediate

In this Shortcut, you’ll learn how to use logistic regression, a foundational machine learning technique, to predict whether a customer will default on their credit. We’ll walk through the steps of loading, preprocessing, and training the model using a dataset that contains financial information, such as income, loan amount, and age. By the end, you’ll understand how logistic regression can be applied to credit scoring, providing insights into potential customer risk.

Introducing Logistic Regression

Logistic regression is one of the most straightforward and interpretable algorithms used for binary classification tasks. Unlike linear regression, which predicts continuous values, logistic regression predicts the probability that a given input belongs to a particular class (for example, default or no default).

In credit scoring, logistic regression is widely used to estimate the probability of a customer defaulting on a loan. This probability helps financial institutions assess risk and make more informed lending decisions.

Steps to Predicting Credit Defaults with Logistic Regression

There are several essential steps to predicting credit defaults using logistic regression:

1. Data collection and preprocessing

2. Data cleaning

3. Feature scaling

4. Creating training and test sets

5. Building the logistic regression model

6. Making predictions and evaluating the model ...