Supermarket Data aggregated by Customer and info from shops pivoted to new columns.

Pennacchioli, D., Coscia, M., Rinzivillo, S., Pedreschi, D. and Giannotti, F., Explaining the Product Range Effect in Purchase Data. In BigData, 2013.

Source

Supermarket data

Sample dataset of one year hourly basis machine monitor, with the recorded info about failures.

Stats from etsy.com shops: items, followers, admirers, feedback, open date, sales,...

Source

Data from 58,000 shops extracted directly from etsy.com using import.io crawler

The Sales Jan 2009 file contains some “sanitized” sales transactions during the month of January. Below is a sample of a report built in just a couple of minutes using the Blank Canvas app. These 998 transactions are easily summarized and filtered by transaction date, payment type, country, city, and geography

Source

spatialkey datasets

A dataset of steel plates’ faults, classified into 7 different types. The goal was to train machine learning for automatic pattern recognition.

Source

Steel Plates Faults Data Set at UCI Machine Learning Repository

Data from a semi-conductor manufacturing process

Source SECOM Data Set from UCI

BBVA Innova challenge Big Data https://www.centrodeinnovacionbbva.com

BBVA contest. Credit card transactions stats in Barcelona and Madrid between Nov-2012 and Apr-2013

Zipcodes: Give a zone identifier and a commercial category, it returns the top postal codes where the clients with the most payments, unique cards and total spent originate.

Predicted field "Incomes" is the total income in a location (Seller Zipcode) and month, grouped by Commercial Category and Buyer Zipcode.

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (or not) subscribed.

The classification goal is to predict if the client will subscribe a term deposit.

Source

Bank Marketing Data Set at UCI Machine Learning Repository

Processed dataset of orders, with several products bought in each order. Dataset prepared for Association Discovery between items (products)

• 3,346,083 orders
• from 206,209 different users
• 33,819,106 products bought (49,685 different products)

Dataset structure:

• order_id: Order ID
• user_id: User ID
• order_number: Order number for a user set of orders
• order_dow: Order day of week (0 to 6)
• order_hour_of_day: Order hour of day (0 to 23)
• days_since_prior_order: Number of days since the previous order of the same user
• products: List of products bought in the order, separated by pipe ( | )

Source:

Instacart Market Basket Analysis at Kaggle based on 3 Million Instacart Orders, Open Sourced blog post