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
Jun 27, 2018
Jun 27, 2018
M27SingleProfessionalTRUECamping Equipment144.78
F39MarriedOtherFALSEOutdoor Protection144.83
F39MarriedOtherFALSEOutdoor Protection137.37
F56UnspecifiedHospitalityFALSEPersonal Accessories92.61
M45MarriedRetiredFALSEGolf Equipment119.04
M45MarriedRetiredFALSEGolf Equipment123.76
F39MarriedOtherFALSEOutdoor Protection142.23
F49MarriedOtherFALSEGolf Equipment105.96
F49MarriedOtherFALSEGolf Equipment109.21
M47MarriedRetiredFALSEGolf Equipment117.58
M47MarriedRetiredFALSEGolf Equipment115.03
M21SingleRetailFALSEPersonal Accessories112.03
F66MarriedOtherFALSEGolf Equipment108.11
F35MarriedProfessionalFALSEGolf Equipment152.95
M20SingleSalesTRUEMountaineering Equipment124.66
Drop file to add data source.