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Fraud Detection in Python

Learn how to detect fraud using Python.

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4 Stunden16 Videos57 Übungen18.744 LernendeTrophyLeistungsnachweis

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Kursbeschreibung

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.
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  1. 1

    Introduction and preparing your data

    Kostenlos

    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.

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    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
  2. 2

    Fraud detection using labeled data

    Now that you're familiar with the main challenges of fraud detection, you're about to learn how to flag fraudulent transactions with supervised learning. You will use classifiers, adjust them, and compare them to find the most efficient fraud detection model.

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    Review of classification methods
    50 xp
    Natural hit rate
    100 xp
    Random Forest Classifier - part 1
    100 xp
    Random Forest Classifier - part 2
    100 xp
    Performance evaluation
    50 xp
    Performance metrics for the RF model
    100 xp
    Plotting the Precision Recall Curve
    100 xp
    Adjusting your algorithm weights
    50 xp
    Model adjustments
    100 xp
    Adjusting your Random Forest to fraud detection
    100 xp
    GridSearchCV to find optimal parameters
    100 xp
    Model results using GridSearchCV
    100 xp
    Ensemble methods
    50 xp
    Logistic Regression
    100 xp
    Voting Classifier
    100 xp
    Adjust weights within the Voting Classifier
    100 xp
  3. 3

    Fraud detection using unlabeled data

    This chapter focuses on using unsupervised learning techniques to detect fraud. You will segment customers, use K-means clustering and other clustering algorithms to find suspicious occurrences in your data.

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    Normal versus abnormal behavior
    50 xp
    Exploring your data
    100 xp
    Customer segmentation
    100 xp
    Using statistics to define normal behavior
    100 xp
    Clustering methods to detect fraud
    50 xp
    Scaling the data
    100 xp
    K-means clustering
    100 xp
    Elbow method
    100 xp
    Assigning fraud versus non-fraud
    50 xp
    Detecting outliers
    100 xp
    Checking model results
    100 xp
    Other clustering fraud detection methods
    50 xp
    DBSCAN
    100 xp
    Assessing smallest clusters
    100 xp
    Checking results
    100 xp
  4. 4

    Fraud detection using text

    In this final chapter, you will use text data, text mining, and topic modeling to detect fraudulent behavior.

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    Using text data
    50 xp
    Word search with dataframes
    100 xp
    Using list of terms
    100 xp
    Creating a flag
    100 xp
    Text mining to detect fraud
    50 xp
    Removing stopwords
    100 xp
    Cleaning text data
    100 xp
    Topic modeling on fraud
    50 xp
    Create dictionary and corpus
    100 xp
    LDA model
    100 xp
    Flagging fraud based on topics
    50 xp
    Interpreting the topic model
    50 xp
    Finding fraudsters based on topic
    100 xp
    Recap
    50 xp
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Trainierst du 2 oder mehr?

Verschaffen Sie Ihrem Team Zugriff auf die vollständige DataCamp-Plattform, einschließlich aller Funktionen.

Datensätze

Chapter 1 datasetsChapter 2 datasetsChapter 3 datasetsChapter 4 datasets

Mitwirkende

Collaborator's avatar
Hadrien Lacroix
Collaborator's avatar
Mari Nazary

Voraussetzungen

Unsupervised Learning in PythonSupervised Learning with scikit-learn
Charlotte Werger HeadshotCharlotte Werger

Director of Advanced Analytics at Nike

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