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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.
This is a random sample of the approximately 6 million patient records from GE Medical Quality Improvement Consortium (MQIC) database.
This dataset consists of 14 demographic attributes of shopping mall customers in the San Francisco Bay area.
The goal is to predict the Anual Income of Household from the other 13 demographics attributes.
ANNUAL INCOME OF HOUSEHOLD (PERSONAL INCOME IF SINGLE) - 1 = Less than $10,000 - 2 = $10,000 to $14,999 - 3 = $15,000 to $19,999 - 4 = $20,000 to $24,999 - 5 = $25,000 to $29,999 - 6 = $30,000 to $39,999 - 7 = $40,000 to $49,999 - 8 = $50,000 to $74,999 - 9 = $75,000 or more
- 1 = Male
- 2 = Female
MARITAL STATUS - 1 = Married - 2 = Living together, not married - 3 = Divorced or separated - 4 = Widowed - 5 = Single, never married
AGE - 1 = 14 thru 17 - 2 = 18 thru 24 - 3 = 25 thru 34 - 4 = 35 thru 44 - 5 = 45 thru 54 - 6 = 55 thru 64 7. 65 and Over
EDUCATION - 1 = Grade 8 or less - 2 = Grades 9 to 11 - 3 = Graduated high school - 4 = 1 to 3 years of college - 5 = College graduate - 6 = Grad Study
- 1 = Professional/Managerial
- 2 = Sales Worker
- 3 = Factory Worker/Laborer/Driver
- 4 = Clerical/Service Worker
- 5 = Homemaker
- 6 = Student, HS or College
- 7 = Military
- 8 = Retired
- 9 = Unemployed
HOW LONG HAVE YOU LIVED IN THE SAN FRAN./OAKLAND/SAN JOSE AREA? - 1 = Less than one year - 2 = One to three years - 3 = Four to six years - 4 = Seven to ten years - 5 = More than ten years
DUAL INCOMES (IF MARRIED) - 1 = Not Married - 2 = Yes - 3 = No
PERSONS IN YOUR HOUSEHOLD - 1 = One - 2 = Two - 3 = Three - 4 = Four - 5 = Five - 6 = Six - 7 = Seven - 8 = Eight - 9 = Nine or more
PERSONS IN HOUSEHOLD UNDER 18 - 0 = None - 1 = One - 2 = Two - 3 = Three - 4 = Four - 5 = Five - 6 = Six - 7 = Seven - 8 = Eight - 9 = Nine or more
HOUSEHOLDER STATUS - 1 = Own - 2 = Rent - 3 = Live with Parents/Family
TYPE OF HOME - 1 = House - 2 = Condominium - 3 = Apartment - 4 = Mobile Home - 5 = Other
- ETHNIC CLASSIFICATION
- 1 = American Indian
- 2 = Asian
- 3 = Black
- 4 = East Indian
- 5 = Hispanic
- 6 = Pacific Islander
- 7 = White
- 8 = Other
WHAT LANGUAGE IS SPOKEN MOST OFTEN IN YOUR HOME? - 1 = English - 2 = Spanish - 3 = Other
This dataset is an extract from a survey performed by Impact Resources, Inc., Columbus, OH (1987).
Predict the auction sale price for a piece of heavy equipment to create a "blue book" for bulldozers.
The goal of the contest is to predict the sale price of a particular piece of heavy equiment at auction based on it's usage, equipment type, and configuaration. The data is sourced from auction result postings and includes information on usage and equipment configurations.
This is an example for CleverTask
Data based on administrative records (individual income tax returns) from the Internal Revenue Service's Individual Master File (IMF) system, which includes a record for every Form 1040, 1040A, and 1040EZ filed with the IRS. The records included in this study were returns that were filed between January 1, 2009 and December 31, 2009. Generally, these are Tax Year 2008 returns although a limited number of late-filed returns for tax years before 2008 were also filed during this period. If a taxpayer filed returns for multiple years during this period, only the most recent return was included.
Relative spinal bone mineral density measurements on 261 North American adolescents. Each value is the difference in spnbmd taken on two consecutive visits, divided by the average. The age is the average age over the two visits.
- idnum: identifies the child, and hence the repeat measurements
age: average age of child when measurements were taken - gender: male or female
spnbmd: Relative Spinal bone mineral density measurement
If you use these data in a publication, please acknowledge the original source:
Bachrach LK, Hastie T, Wang M-C, Narasimhan B, Marcus R. Bone Mineral Acquisition in Healthy Asian, Hispanic, Black and Caucasian Youth. A Longitudinal Study. J Clin Endocrinol Metab (1999) 84, 4702-12.
This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.
Originally from the UCI repository