---
dataset_info:
  features:
  - name: age
    dtype: int64
  - name: workclass
    dtype: string
  - name: fnlwgt
    dtype: int64
  - name: education
    dtype: string
  - name: education.num
    dtype: int64
  - name: marital.status
    dtype: string
  - name: occupation
    dtype: string
  - name: relationship
    dtype: string
  - name: race
    dtype: string
  - name: sex
    dtype: string
  - name: capital.gain
    dtype: int64
  - name: capital.loss
    dtype: int64
  - name: hours.per.week
    dtype: int64
  - name: native.country
    dtype: string
  - name: income
    dtype: string
  splits:
  - name: train
    num_bytes: 5316802
    num_examples: 32561
  download_size: 553790
  dataset_size: 5316802
license: cc
language:
- en
pretty_name: adult-census-income
size_categories:
- 10K<n<100K
---
# adult-census-income

## Overview 

The adult-census-income dataset is used for prediction tasks to determine whether a person makes over $50K a year.

## Dataset Details

The original dataset, the [Adult Census Income](https://www.kaggle.com/datasets/uciml/adult-census-income), was created by Barry Becker from the 1994 Census database (USA), 
to explore biases in ML algorithms. The prediction task of this dataset is to determine whether a person makes over 50K a year.
This data was extracted from the 1994 [Census Bureau database](https://www.census.gov/en.html) by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics). A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1) && (HRSWK>0)).

- Dataset Name: adult-census-income
- Language:  English
- Total Size: 32,561  demonstrations

## Contents

The features and values that can be found in the adult census dataset are the following:

- **Income:** '>50K' (24,1%), '<=50K'(75,9%).
- **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-inspect, 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.

## How to use

```python
from datasets import load_dataset

dataset = load_dataset("AiresPucrs/adult-census-income", split='train')

```

## License

The dataset is licensed under the [Creative Commons(CC)](https://creativecommons.org/publicdomain/zero/1.0/) License CC0 1.0.