The dataset is an extract from this survey. It consists of 14 demographic attributes. The dataset is a good mixture of categorical and continuos variables with a lot of missing data. This is characteristic for data mining applications.
data(marketing)A data frame with 8993 observations on the following 14 variables.
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
1. Married 2. Living together, not married 3. Divorced or separated 4. Widowed 5. Single, never married
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
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
1. House 2. Condominium 3. Apartment 4. Mobile Home 5. Other
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
The goal is to predict the Anual Income of Household from the other 13 demographics attributes.
Number of instances: 8993.
These are obtained from the original dataset with 9409 instances, by removing those observations with the response (Annual Income) missing.
# NOT RUN {
str(marketing)
summary(marketing)
# }
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