Census Income Data Set

This data set was obtained from the UC Irvine Machine Learning Repository and contains weighted census data extracted from the 1994 and 1995 Current Population Surveys conducted by the U.S. Census Bureau.

Source

This data set was obtained by downloading census-income.data (contained in census-income.data.gz) from http://archive.ics.uci.edu/ml/datasets/Census-Income+(KDD).

The original table contains 199,523 rows and 42 columns. An additional column, edu_year, has been added to aid in the analysis (see Input Variables).

The path to this data set is pub.demo.mleg.uci.censusincome.

Input Variables

There are 42 columns in the table that provide demographic and employment-related information.


Column Name	Description	Type
`age`	Age of the worker	Numeric
`class_worker`	Class of worker	Categorial: Not in universe Private Self-employed-not incorporated Local government State government Self-employed-incorporated Federal government Never worked Without pay
`det_ind_code`	Industry code	Numeric
`det_occ_code`	Occupation code	Numeric
`education`	Level of education	Categorial: Children Less than 1st grade 1st 2nd 3rd or 4th grade 5th or 6th grade 7th and 8th grade 9th grade 10th grade 11th grade 12th grade no diploma High school graduate Some college but no degree Associates degree-academic program Associates degree-occup /vocational Bachelors degree(BA AB BS) Masters degree(MA MS MEng MEd MSW MBA) Prof school degree (MD DDS DVM LLB JD) Doctorate degree(PhD EdD)
`wage_per_hour`	Wage per hour	Numeric
`hs_college`	Enrolled in educational institution last week	Categorial: Not in universe High school College or university
`marital_stat`	Marital status	Categorial: Never married Married-civilian spouse present Divorced Widowed Separated Married-spouse absent Married-A F spouse present
`major_ind_code`	Major industry code	Categorial: Not in universe or children Retail trade Manufacturing-durable goods Education Manufacturing-nondurable goods Finance insurance and real estate Construction Business and repair services Medical except hospital Public administration Other professional services Transportation Hospital services Wholesale trade Agriculture Personal services except private HH Social services Entertainment Communications Utilities and sanitary services Private household services Mining Forestry and fisheries Armed Forces
`major_occ_code`	Major occupation code	Categorial: Not in universe Adm support including clerical Professional specialty Executive admin and managerial Other service Sales Precision production craft & repair Machine operators assmblrs & inspctrs Handlers equip cleaners etc Transportation and material moving Farming forestry and fishing Technicians and related support Protective services Private household services Armed Forces
`race`	Race	Categorial: White Black Asian or Pacific Islander Other Amer Indian Aleut or Eskimo
`hisp_origin`	Hispanic origin	Categorial: All other Mexican-American Mexican (Mexicano) Central or South American Puerto Rican Other Spanish Cuban NA Do not know Chicano
`sex`	Sex	Categorial: Female Male
`union_member`	Member of a labor union	Categorial: Not in universe No Yes
`unemp_reason`	Reason for unemployment	Categorial: Not in universe Other job loser Re-entrant Job loser - on layoff Job leaver New entrant
`full_or_part_emp`	Full- or part-time employment status	Categorial: Children or Armed Forces Full-time schedules Not in labor force PT for non-econ reasons usually FT Unemployed full-time PT for econ reasons usually PT Unemployed part- time PT for econ reasons usually FT
`capital_gains`	Capital gains	Numeric
`capital_losses`	Capital losses	Numeric
`stock_dividends`	Dividends from stocks	Numeric
`tax_filer_stat`	Tax filer status	Categorial: Nonfiler Joint both under 65 Single Joint both 65+ Head of household Joint one under 65 & one 65+
`region_prev_res`	Region of previous residence	Categorial: Not in universe South West Midwest Northeast Abroad
`state_prev_res`	State of previous residence	Categorial: Not in universe California Utah Florida North Carolina ? Abroad Oklahoma Minnesota Indiana North Dakota New Mexico Michigan Alaska Kentucky Arizona New Hampshire Wyoming Colorado Oregon West Virginia Georgia Montana Alabama Ohio Texas Arkansas Mississippi Tennessee Pennsylvania New York Louisiana Vermont Iowa Illinois Nebraska Missouri Nevada Maine Massachusetts Kansas South Dakota Maryland Virginia Connecticut District of Columbia Wisconsin South Carolina New Jersey Delaware Idaho
`det_hh_fam_stat`	Detailed household and family status	Categorial: Householder Child <18 never marr not in subfamily Spouse of householder Nonfamily householder Child 18+ never marr Not in a subfamily Secondary individual Other Rel 18+ ever marr not in subfamily Grandchild <18 never marr child of subfamily RP Other Rel 18+ never marr not in subfamily Grandchild <18 never marr not in subfamily Child 18+ ever marr Not in a subfamily Child under 18 of RP of unrel subfamily RP of unrelated subfamily Child 18+ ever marr RP of subfamily Other Rel 18+ ever marr RP of subfamily Other Rel <18 never marr child of subfamily RP Other Rel 18+ spouse of subfamily RP Child 18+ never marr RP of subfamily Other Rel <18 never marr not in subfamily Grandchild 18+ never marr not in subfamily In group quarters Child 18+ spouse of subfamily RP Other Rel 18+ never marr RP of subfamily Child <18 never marr RP of subfamily Spouse of RP of unrelated subfamily Child <18 ever marr not in subfamily Grandchild 18+ ever marr not in subfamily Grandchild 18+ spouse of subfamily RP Child <18 ever marr RP of subfamily Grandchild 18+ ever marr RP of subfamily Grandchild 18+ never marr RP of subfamily Other Rel <18 ever marr RP of subfamily Other Rel <18 never married RP of subfamily Other Rel <18 spouse of subfamily RP Child <18 spouse of subfamily RP Grandchild <18 ever marr not in subfamily Grandchild <18 never marr RP of subfamily Other Rel <18 ever marr not in subfamily
`det_hh_summ`	Detailed household summary in household	Categorial: Householder Child under 18 never married Spouse of householder Child 18 or older Other relative of householder Nonrelative of householder Group Quarters- Secondary individual Child under 18 ever married
`mig_chg_msa`	Migration code - change in MSA	Categorial: ? Nonmover MSA to MSA NonMSA to nonMSA Not in universe MSA to nonMSA NonMSA to MSA Abroad to MSA Not identifiable Abroad to nonMSA
`mig_chg_reg`	Migration code - change in region	Categorial: ? Nonmover Same county Different county same state Not in universe Different region Different state same division Abroad Different division same region
`mig_move_reg`	Migration code - move within region	Categorial: ? Nonmover Same county Different county same state Not in universe Different state in South Different state in West Different state in Midwest Abroad Different state in Northeast
`mig_same`	Live in this house one year ago	Categorial: Not in universe under 1 year old Yes No
`mig_prev_sunbelt`	Migration - previous residence in sunbelt	Categorial: ? Not in universe No Yes
`num_emp`	Number of persons that worked for employer	Numeric
`fam_under_18`	Family members under 18	Categorial: Not in universe Both parents present Mother only present Father only present Neither parent present
`country_father`	Country of birth father	Categorial: United-States Mexico ? Puerto-Rico Italy Canada Germany Dominican-Republic Poland Philippines Cuba El-Salvador China England Columbia India South Korea Ireland Jamaica Vietnam Guatemala Japan Portugal Ecuador Haiti Greece Peru Nicaragua Hungary Scotland Iran Yugoslavia Taiwan Cambodia Honduras France Outlying-U S (Guam USVI etc) Laos Trinadad&Tobago Thailand Hong Kong Holand-Netherlands Panama
`country_mother`	Country of birth mother	Categorial: United-States Mexico ? Puerto-Rico Italy Canada Germany Philippines Poland Cuba El-Salvador Dominican-Republic England China Columbia South Korea Ireland India Vietnam Japan Jamaica Guatemala Ecuador Peru Haiti Portugal Nicaragua Hungary Greece Scotland Taiwan Honduras France Iran Yugoslavia Cambodia Outlying-U S (Guam USVI etc) Laos Thailand Hong Kong Trinadad&Tobago Holand-Netherlands Panama
`country_self`	Country of birth	Categorial: United-States Mexico ? Puerto-Rico Germany Philippines Cuba Canada Dominican-Republic El-Salvador China South Korea England Columbia Italy India Vietnam Poland Guatemala Japan Jamaica Peru Ecuador Haiti Nicaragua Taiwan Portugal Iran Greece Honduras Ireland France Outlying-U S (Guam USVI etc) Thailand Laos Hong Kong Cambodia Hungary Scotland Trinadad&Tobago Yugoslavia Panama Holand-Netherlands
`citizenship`	Citizenship	Categorial: Native- Born in the United States Foreign born- Not a citizen of U S Foreign born- U S citizen by naturalization Native- Born abroad of American Parent(s) Native- Born in Puerto Rico or U S Outlying
`own_or_self`	Own business or self-employed?	Numeric
`vet_question`	Fill included questionnaire for Veterans Administration	Categorial: Not in universe No Yes
`vet_benefits`	Veterans benefits	Numeric
`weeks_worked`	Weeks worked in the year	Numeric
`year`	Year of survey	Numeric
`income_50k`	Income less than or greater than $50,000	Categorial: - 50000. 50000+.
`edu_year`	Number of years of education	Numeric

Note: The edu_year column is derived from the education column according to the following mapping:


`education`	`edu_year`
Children	0
Less than 1st grade	0.5
1st 2nd 3rd or 4th grade	2.5
5th or 6th grade	5.5
7th and 8th grade	7.5
9th grade	9
10th grade	10
11th grade	11
12th grade no diploma	12
High school graduate	12
Some college but no degree	14
Associates degree-academic program	14
Associates degree-occup /vocational	14
Bachelors degree(BA AB BS)	16
Masters degree(MA MS MEng MEd MSW MBA)	18
Prof school degree (MD DDS DVM LLB JD)	20
Doctorate degree(PhD EdD)	21

Weight Variable

There is one column in the table that corresponds to the weight value.


Column Name	Description	Type
`instance_weight`	Indicates the number of people in the population that each record represents due to stratified sampling	Numeric

Output Variable

There is one column in the table that corresponds to our target value.


Column Name	Description	Type
`wage_per_hour`	Wage per hour (multiplied by 100) For example, a value of `1200` would correspond to $12.00/hr.	Numeric