Historical customer data for a fictional outdoor equipment store used in Watson Studio tutorials train the machine learning models. The sample data is structured in rows and columns, and saved in a .csv file.

Feature columns
Feature columns are columns that contain the attributes on which the machine learning model will base predictions. In this historical data, there are four feature columns:

• GENDER: Customer gender
• AGE: Customer age
• MARITAL_STATUS: "Married", "Single", or "Unspecified"
• PROFESSION: General category of the customer's profession, such "Hospitality" or "Sales", or simply "Other"

Label columns
Label columns are columns that contain historical outcomes that the models will be trained predict. In this historical data, there are three label columns:

• IS_TENT: Whether or not the customer bought a tent
• PRODUCT_LINE: The product category in which the customer has been most interested
• PURCHASE_AMOUNT: The average amount of money the customer has spent on each visit to the store