Detect Fraud Using Isolation Forest

Contributed by: Brindha Priyadarshini Jeyaraman

Difficulty level: Advanced

In this Shortcut, you’ll learn how to detect credit card fraud using the Isolation Forest algorithm, an effective technique for unsupervised anomaly detection. By the end, you’ll understand how machine learning models can detect fraudulent transactions by identifying anomalies in transaction data. Isolation Forest is particularly useful for fraud detection tasks due to its ability to isolate anomalies with fewer splits.

A split in Isolation Forest is a decision point that partitions the data into two subsets. It’s based on a randomly selected feature and a randomly split value. Anomalies tend to be isolated with fewer splits, while normal data points require more splits. This property is used to identify outliers in the data.

Introducing Isolation Forest

Isolation Forest is an unsupervised learning algorithm that excels at detecting outliers or anomalies in data. Unlike other algorithms that rely on distance or density measures to detect anomalies, Isolation Forest works by isolating data points that are significantly different from the rest. It builds random decision trees by splitting the dataset along random feature values. Anomalies require fewer splits to isolate, making them easier to detect.

Steps to Detect Fraud Using Isolation Forest

There are seven main steps to detect fraud using Isolation Forest:

1. Data collection and preprocessing

2. Feature scaling ...