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Curso

Fraud Detection in Python

IntermediárioNível de habilidade

Actualizado 05/2025

Learn how to detect fraud using Python.

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PythonMachine Learning4 horas16 vídeos57 Exercícios4,800 XP19,842Certificado de conclusão

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Descrição do curso

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.

Pré-requisitos

Unsupervised Learning in Python Supervised Learning with scikit-learn

1

Introduction and preparing your data

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Introduction to fraud detection

Checking the fraud to non-fraud ratio

Plotting your data

Increasing successful detections using data resampling

Resampling methods for imbalanced data

Applying SMOTE

Compare SMOTE to original data

Fraud detection algorithms in action

Exploring the traditional way to catch fraud

Using ML classification to catch fraud

Logistic regression combined with SMOTE

Using a pipeline

2

Fraud detection using labeled data

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Review of classification methods

Natural hit rate

Random Forest Classifier - part 1

Random Forest Classifier - part 2

Performance evaluation

Performance metrics for the RF model

Plotting the Precision Recall Curve

Adjusting your algorithm weights

Model adjustments

Adjusting your Random Forest to fraud detection

GridSearchCV to find optimal parameters

Model results using GridSearchCV

Ensemble methods

Logistic Regression

Voting Classifier

Adjust weights within the Voting Classifier

3

Fraud detection using unlabeled data

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Normal versus abnormal behavior

Exploring your data

Customer segmentation

Using statistics to define normal behavior

Clustering methods to detect fraud

Scaling the data

K-means clustering

Elbow method

Assigning fraud versus non-fraud

Detecting outliers

Checking model results

Other clustering fraud detection methods

Assessing smallest clusters

Checking results

4

Fraud detection using text

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Using text data

Word search with dataframes

Using list of terms

Creating a flag

Text mining to detect fraud

Removing stopwords

Cleaning text data

Topic modeling on fraud

Create dictionary and corpus

Flagging fraud based on topics

Interpreting the topic model

Finding fraudsters based on topic

Fraud Detection in Python

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