Fraud Detection in Python

Learn how to detect fraud using Python.

Start Course for Free
4 Hours16 Videos57 Exercises11,765 Learners
4800 XP

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies


Course Description

A typical organization loses an estimated 5% of its yearly revenue to fraud. In this course, you will learn how to fight fraud by using data. For example, you'll learn how to apply supervised learning algorithms to detect fraudulent behavior similar to past ones, as well as unsupervised learning methods to discover new types of fraud activities. Moreover, in fraud analytics you often deal with highly imbalanced datasets when classifying fraud versus non-fraud, and during this course you will pick up some techniques on how to deal with that. The course provides a mix of technical and theoretical insights and shows you hands-on how to practically implement fraud detection models. In addition, you will get tips and advice from real-life experience to help you prevent making common mistakes in fraud analytics.

  1. 1

    Introduction and preparing your data

    Free

    In this chapter, you'll learn about the typical challenges associated with fraud detection, and will learn how to resample your data in a smart way, to tackle problems with imbalanced data.

    Play Chapter Now
    Introduction to fraud detection
    50 xp
    Checking the fraud to non-fraud ratio
    100 xp
    Plotting your data
    100 xp
    Increasing successful detections using data resampling
    50 xp
    Resampling methods for imbalanced data
    50 xp
    Applying SMOTE
    100 xp
    Compare SMOTE to original data
    100 xp
    Fraud detection algorithms in action
    50 xp
    Exploring the traditional way to catch fraud
    100 xp
    Using ML classification to catch fraud
    100 xp
    Logistic regression combined with SMOTE
    100 xp
    Using a pipeline
    100 xp

Datasets

Chapter 1 datasetsChapter 2 datasetsChapter 3 datasetsChapter 4 datasets

Collaborators

Hadrien LacroixMari Nazary
Charlotte Werger Headshot

Charlotte Werger

Director of Advanced Analytics at Nike

Dr. Charlotte Werger currently works at Nike as a Director of Advanced Analytics. Charlotte is a data scientist with a background in econometrics and finance. She loves applying Machine Learning to a broad variety of problems, ranging from image recognition to fraud detection, to customer recommender systems. Charlotte has previously worked in finance as Head of Data Science at Van Lanschot Kempen, and as a quantitative researcher and portfolio manager for BlackRock and Man AHL. In those roles, she specialized in using data science to predict movements in stock markets. As the former Head of Education at Faculty, she loves teaching data science on- and off-line. Charlotte is also active as a Data Science mentor for the Springboard program. Charlotte holds a P.h.D from the European University Institute.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA