README.md

metadata

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. It can also be used to explore biases in ML algorithms.

Dataset Details

The original dataset, the 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 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

from datasets import load_dataset

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

License

This dataset is licensed under the Creative Commons(CC) License CC0 1.0.