You work for a multinational bank. There has been a significant increase in the number of customers experiencing credit card fraud over the last few months. A major news outlet even recently published a story about the credit card fraud you and other banks are experiencing.
As a response to this situation, you have been tasked to solve part of this problem by leveraging machine learning to identify fraudulent credit card transactions before they have a larger impact on your company. You have been given access to a dataset of past credit card transactions, which you can use to train a machine learning model to predict if transactions are fraudulent or not.
The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred over the course of two days and includes examples of both fraudulent and legitimate transactions.
Features The dataset contains over 30 numerical features, most of which have undergone principal component analysis (PCA) transformations because of personal privacy issues with the data. The only features that have not been transformed with PCA are 'Time' and 'Amount'. The feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction amount. 'Class' is the response or target variable, and it takes a value of '1' in cases of fraud and '0' otherwise.
Features: 'V1, V2, ... V28': Principal components obtained with PCA
Non-PCA features:
- 'Time': Seconds elapsed between each transaction and the first transaction in the dataset, Tx−t0
- 'Amount': Transaction amount; this feature can be used for example-dependent cost-sensitive learning
- 'Class': Target variable where 'Fraud = 1' and 'Not Fraud = 0' Dataset attributions Website: https://www.openml.org/d/1597
Twitter: https://twitter.com/dalpozz/status/645542397569593344
Authors: Andrea Dal Pozzolo, Olivier Caelen, and Gianluca Bontempi Source: Credit card fraud detection - June 25, 2015 Official citation: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson, and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015.
The dataset has been collected and analyzed during a research collaboration of Worldline and the Machine Learning Group (mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML.