Imbalanced Classification with the Fraudulent Credit Card Transactions Dataset

Last Updated on August 21, 2020

Fraud is a major problem for credit card companies, both because of the large volume of transactions that are completed each day and because many fraudulent transactions look a lot like normal transactions.

Identifying fraudulent credit card transactions is a common type of imbalanced binary classification where the focus is on the positive class (is fraud) class.

As such, metrics like precision and recall can be used to summarize model performance in terms of class labels and precision-recall curves can be used to summarize model performance across a range of probability thresholds when mapping predicted probabilities to class labels.

This gives the operator of the model control over how predictions are made in terms of biasing toward false positive or false negative type errors made by the model.

In this tutorial, you will discover how to develop and evaluate a model for the imbalanced credit card fraud dataset.

After completing this tutorial, you will know:

• How to load and explore the dataset and generate ideas for data preparation and model selection.
• How to systematically evaluate a suite of machine learning models with a robust test harness.
• How to fit a final model
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