课程

Fraud Detection in Python

中级技能水平

更新时间 2024年8月

Learn how to detect fraud using Python.

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PythonMachine Learning4 小时16 视频57 练习4,800 经验值21,763成就声明

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课程描述

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.

先决条件

Unsupervised Learning in Python Supervised Learning with scikit-learn

1

Introduction and preparing your data

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.

Introduction to fraud detection

50 经验值

Checking the fraud to non-fraud ratio

100 经验值

Plotting your data

100 经验值

Increasing successful detections using data resampling

50 经验值

Resampling methods for imbalanced data

50 经验值

Applying SMOTE

100 经验值

Compare SMOTE to original data

100 经验值

Fraud detection algorithms in action

50 经验值

Exploring the traditional way to catch fraud

100 经验值

Using ML classification to catch fraud

100 经验值

Logistic regression combined with SMOTE

100 经验值

Using a pipeline

100 经验值

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.

Review of classification methods

50 经验值

Natural hit rate

100 经验值

Random Forest Classifier - part 1

100 经验值

Random Forest Classifier - part 2

100 经验值

Performance evaluation

50 经验值

Performance metrics for the RF model

100 经验值

Plotting the Precision Recall Curve

100 经验值

Adjusting your algorithm weights

50 经验值

Model adjustments

100 经验值

Adjusting your Random Forest to fraud detection

100 经验值

GridSearchCV to find optimal parameters

100 经验值

Model results using GridSearchCV

100 经验值

Ensemble methods

50 经验值

Logistic Regression

100 经验值

Voting Classifier

100 经验值

Adjust weights within the Voting Classifier

100 经验值

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.

Normal versus abnormal behavior

50 经验值

Exploring your data

100 经验值

Customer segmentation

100 经验值

Using statistics to define normal behavior

100 经验值

Clustering methods to detect fraud

50 经验值

Scaling the data

100 经验值

K-means clustering

100 经验值

Elbow method

100 经验值

Assigning fraud versus non-fraud

50 经验值

Detecting outliers

100 经验值

Checking model results

100 经验值

Other clustering fraud detection methods

50 经验值

100 经验值

Assessing smallest clusters

100 经验值

Checking results

100 经验值

4

Fraud detection using text

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

Using text data

50 经验值

Word search with dataframes

100 经验值

Using list of terms

100 经验值

Creating a flag

100 经验值

Text mining to detect fraud

50 经验值

Removing stopwords

100 经验值

Cleaning text data

100 经验值

Topic modeling on fraud

50 经验值

Create dictionary and corpus

100 经验值

100 经验值

Flagging fraud based on topics

50 经验值

Interpreting the topic model

50 经验值

Finding fraudsters based on topic

100 经验值

50 经验值

Fraud Detection in Python

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