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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.
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
Netflix's catalog titles created using their OData catalog API at http://developer.netflix.com/docs/oData_Catalog. Great to see that October is the month when everybody gets ready for the Holiday season.
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)
- 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 ( | )
Contiene datos sobre los productos encontrados en diversas cestas de la compra. Traducción del fichero original publicado en GitHub.
Food and alcohol expenses for countries. Data taken from the USDA
Contiene datos sobre precios y metros cuadrados de inmuebles publicados para su venta. Muestreo y traducción del fichero original publicado en http://www.louisdorard.com/guest/everyone-can-do-data-science-bigml.
Amazon Redshift example database