Notebook: Fraud Detection, Exercise

Next, you'll approach the task of payment fraud detection! This is a real-world problem, with fraud accounting for billions of dollars worth of loss, worldwide. As you follow along with this lesson, you should work in the referenced SageMaker notebooks. We will present a solution to you, but please try to work on a solution of your own, when prompted. Much of the value in this experience will come from experimenting with the code, in your own way .

To open this notebook:

• Navigate to your SageMaker notebook instance, in the SageMaker console , which has been linked to the main Github exercise repository
• Activate the notebook instance (if it is in a "Stopped" state), and open it via Jupyter
• Click on the exercise notebook in the Payment_Fraud_Detection directory.

You may also directly view the exercise and solution notebooks via the repository at the following links:

• Exercise notebook
• Solution notebook

The solution notebook is meant to be consulted if you are stuck or want to check your work.

Notebook Outline

We'll go over the following steps to complete the notebook.

• Load in and explore payment transaction data
• Train a LinearLearner to classify the data
• Improve a basic model by accounting for class imbalance in the dataset and different metrics for model "success"

Later: Delete Resources

At the end of this exercise, and intermittently, you will be reminded to delete your endpoints and resources so that you do not incur any extra processing or storage fees!