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  3. Fraud Detection in Python
  • 1

    Introduction and preparing your data

    Gratuit

    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.

  • 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.

  • 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.

  • 4

    Fraud detection using text

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

Initializing

Exercice

K-means clustering

A very commonly used clustering algorithm is K-means clustering. For fraud detection, K-means clustering is straightforward to implement and relatively powerful in predicting suspicious cases. It is a good algorithm to start with when working on fraud detection problems. However, fraud data is oftentimes very large, especially when you are working with transaction data. MiniBatch K-means is an efficient way to implement K-means on a large dataset, which you will use in this exercise.

The scaled data from the previous exercise, X_scaled is available. Let's give it a try.

Instructions

100 XP
  • Import MiniBatchKMeans from sklearn.
  • Initialize the minibatch kmeans model with 8 clusters.
  • Fit the model to your scaled data.