Credit Card Fraud

In this example, we use VerticaPy to detect fraudulent credit card transactions. You can download the Jupyter notebook here.

The Credit Card Fraud Detection dataset contains credit card transactions from September 2013 by European cardholders. It contains numerical input variables from a principal component analysis (PCA) transformation.

To preserve the cardholders' confidentiality, we cannot access the original features and background information about the data.

"Time" and "Amount" are the only features that have not been transformed with PCA.

• V1, V2,..., V28 : principal components from PCA
• Time : Number of seconds elapsed between this transaction and the first transaction in the dataset
• Amount : Transaction amount
• Class : Response variable, where a value of 1 indicates fraudulent activity

"Amount" will be useful for example-dependent cost-sensitive learning.

We will follow the entire Data Science cycle (Data Exploration, Data Preparation, Data Modeling, Model Evaluation, Model Deployment) to solve this problem.

Initialization¶

This example uses the following version of VerticaPy:

Connect to Vertica. This example uses an existing connection called "VerticaDSN." For details on how to create a connection, use see the connection tutorial.

Let's create a Virtual DataFrame of the dataset.

Data Exploration

Let's explore the data by displaying descriptive statistics of all the columns.

It'll be difficult to work on the principal components (V1 through V28) without knowing what they mean. The only features we can work on are Time and Amount.

Let's convert the number of seconds elapsed to the correct date and time. We know that the records were ingested in September 2013, so we'll use that to create the new feature.

Let's take the data over two days and split them into a training set (first day) and test set (second day).

Fraudulent activity probably isn't uniform across all hours of the day, so we'll extract the hour from the time and see how that influences the prediction.