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Fraud Detection Case Study

For the next two days we will work with the entire end to end pipeline of data science through a case study. We have touched on aspects of this throughout the course but have not yet put all the pieces together.

Topics included in this case study include:

  • Classification modeling.
  • Programming Practice: Handing off models.
  • Teamwork.
  • Web applications.
  • Website hosting with AWS
  • Deploying a DS application.
  • Data visualization.
  • Results presentation.

Rough timeline

  • Day 1: Project scoping, Team direction, Model building
  • Day 2: Web app and deployment

Deliverables

We will want two deliverables from you for this project:

  • A dashboard for investigators to use which helps them identify new events that are worthy of investigation for fraud. This will pull in new data regularly, and update a useful display for the investigation team. How you wish to lay this out is up to you.
  • A ten-minute presentation on your process and results.

Notes

  • Overview: gives a detailed overview of the project. Included are suggestions for how you can organize your team, though this is not binding, and you are free to deviate.
  • Building your model: notes on how to get started with the dataset and how to save your model once you've trained it.

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