orders
(3.4m rows, 206k users):
order_id
: order identifieruser_id
: customer identifiereval_set
: which evaluation set this order belongs in (seeSET
described below)order_number
: the order sequence number for this user (1 = first, n = nth)order_dow
: the day of the week the order was placed onorder_hour_of_day
: the hour of the day the order was placed ondays_since_prior
: days since the last order, capped at 30 (with NAs fororder_number
= 1)
products
(50k rows):
product_id
: product identifierproduct_name
: name of the productaisle_id
: foreign keydepartment_id
: foreign key
aisles
(134 rows):
aisle_id
: aisle identifieraisle
: the name of the aisle
deptartments
(21 rows):
department_id
: department identifierdepartment
: the name of the department
order_products__SET
(30m+ rows):
order_id
: foreign keyproduct_id
: foreign keyadd_to_cart_order
: order in which each product was added to cartreordered
: 1 if this product has been ordered by this user in the past, 0 otherwise
where SET
is one of the four following evaluation sets (eval_set
in orders
):
"prior"
: orders prior to that users most recent order (~3.2m orders)"train"
: training data supplied to participants (~131k orders)"test"
: test data reserved for machine learning competitions (~75k orders)
There is no pricing details available for the product .
I would like to do prediction for the product for which has been bough by customers. It would be great if we can have customer spending amount or salary of the customer.