Instacart Data Analysis

The Task

The dataset is an open-source dataset provided by Instacart (source)

This anonymized dataset contains a sample of over 3 million grocery orders from more than 200,000 Instacart users. For each user, we provide between 4 and 100 of their orders, with the sequence of products purchased in each order. We also provide the week and hour of day the order was placed, and a relative measure of time between orders.

Below is the full data schema

orders (3.4m rows, 206k users):

• order_id: order identifier
• user_id: customer identifier
• eval_set: which evaluation set this order belongs in (see SET 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 on
• order_hour_of_day: the hour of the day the order was placed on
• days_since_prior: days since the last order, capped at 30 (with NAs for order_number = 1)

products (50k rows):

• product_id: product identifier
• product_name: name of the product
• aisle_id: foreign key
• department_id: foreign key

aisles (134 rows):

• aisle_id: aisle identifier
• aisle: the name of the aisle

deptartments (21 rows):

• department_id: department identifier
• department: the name of the department

order_products__SET (30m+ rows):

• order_id: foreign key
• product_id: foreign key
• add_to_cart_order: order in which each product was added to cart
• reordered: 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)