Dataset: Census Income Data Set

About this Dataset

Census Income Data Set

Context

This data set come from UCI Machine Learning Repository at https://archive.ics.uci.edu/ml/datasets/census+income

Data Set Information:

Prediction task is to determine whether a person makes over 50K a year from the analysis of 13 predictors.

Content

age: continuous.
workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
fnlwgt: continuous.
education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
education-num: continuous.
marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
sex: Female, Male.
capital-gain: continuous.
capital-loss: continuous.
hours-per-week: continuous.
native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.

Acknowledgements

Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))

Description of fnlwgt (final weight)

The weights on the CPS files are controlled to independent estimates of the civilian non-institutional population of the US. These are prepared monthly for us by Population Division here at the Census Bureau. We use 3 sets of controls.

These are:

A single cell estimate of the population 16+ for each state.
Controls for Hispanic Origin by age and sex.
Controls by Race, age and sex.

We use all three sets of controls in our weighting program and "rake" through them 6 times so that by the end we come back to all the controls we used.

The term estimate refers to population totals derived from CPS by creating "weighted tallies" of any specified socio-economic characteristics of the population.

People with similar demographic characteristics should have similar weights. There is one important caveat to remember
about this statement. That is that since the CPS sample is actually a collection of 51 state samples, each with its own
probability of selection, the statement only applies within state.

Summary

Data Set Characteristics: Multivariate
Area: Social
Attribute Characteristics: Categorical, Integer
Number of Attributes: 14
Date Donated: 1996-05-01
Associated Tasks: Classification
Missing Values? Yes

Tables

Census

@kaggle.vivamoto_us_adult_income_update.census

506 KB
48842 rows
15 columns


CREATE TABLE census (
  "age" BIGINT,
  "workclass" VARCHAR,
  "fnlwgt" BIGINT,
  "education" VARCHAR,
  "education_num" BIGINT,
  "marital_status" VARCHAR,
  "occupation" VARCHAR,
  "relationship" VARCHAR,
  "race" VARCHAR,
  "sex" VARCHAR,
  "capital_gain" BIGINT,
  "capital_loss" BIGINT,
  "hours_per_week" BIGINT,
  "native_country" VARCHAR,
  "income" VARCHAR
);

Test

@kaggle.vivamoto_us_adult_income_update.test

191.69 KB
16281 rows
15 columns


CREATE TABLE test (
  "age" BIGINT,
  "workclass" VARCHAR,
  "fnlwgt" BIGINT,
  "education" VARCHAR,
  "education_num" BIGINT,
  "marital_status" VARCHAR,
  "occupation" VARCHAR,
  "relationship" VARCHAR,
  "race" VARCHAR,
  "sex" VARCHAR,
  "capital_gain" BIGINT,
  "capital_loss" BIGINT,
  "hours_per_week" BIGINT,
  "native_country" VARCHAR,
  "income" VARCHAR
);

Train

@kaggle.vivamoto_us_adult_income_update.train

355.28 KB
32561 rows
15 columns


CREATE TABLE train (
  "age" BIGINT,
  "workclass" VARCHAR,
  "fnlwgt" BIGINT,
  "education" VARCHAR,
  "education_num" BIGINT,
  "marital_status" VARCHAR,
  "occupation" VARCHAR,
  "relationship" VARCHAR,
  "race" VARCHAR,
  "sex" VARCHAR,
  "capital_gain" BIGINT,
  "capital_loss" BIGINT,
  "hours_per_week" BIGINT,
  "native_country" VARCHAR,
  "income" VARCHAR
);