This example uses the data file pm_customer_train1.csv, which contains historical data that tracks the offers made to specific customers in past campaigns, as indicated by the value of the campaign field. The largest number of records fall under the Premium account campaign.

The values of the campaign field are actually coded as integers in the data (for example 2 = Premium account). Later, you'll define labels for these values that you can use to give more meaningful output.

The file also includes a response field that indicates whether the offer was accepted (0 = no, and 1 = yes). This will be the target field, or value, that you want to predict. A number of fields containing demographic and financial information about each customer are also included. These can be used to build or "train" a model that predicts response rates for individuals or groups based on characteristics such as income, age, or number of transactions per month.