Introducing an XGBoost use case
In this section, we will be discussing a use case where we want to predict whether credit card transactions are fraudulent. We will be going through the following steps:
- Defining the business problem
- Uploading, analyzing, and preparing data for training
- Splitting data into training and testing datasets
- Preprocessing the input variables
Defining the business problem
In this section, we will use a credit card payment transaction dataset to build a binary classification model using XGBoost in Redshift ML. This dataset contains customer and terminal information along with the date and amount related to the transaction. This dataset also has some derived fields based on recency, frequency, and monetary numeric features, along with a few categorical variables, such as whether a transaction occurred during the weekend or at night. Our goal is to identify whether a transaction is fraudulent or non-fraudulent. This use case is taken...
