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--- |
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dataset_info: |
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features: |
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- name: age |
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dtype: int64 |
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- name: workclass |
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dtype: string |
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- name: fnlwgt |
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dtype: int64 |
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- name: education |
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dtype: string |
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- name: education.num |
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dtype: int64 |
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- name: marital.status |
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dtype: string |
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- name: occupation |
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dtype: string |
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- name: relationship |
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dtype: string |
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- name: race |
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dtype: string |
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- name: sex |
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dtype: string |
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- name: capital.gain |
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dtype: int64 |
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- name: capital.loss |
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dtype: int64 |
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- name: hours.per.week |
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dtype: int64 |
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- name: native.country |
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dtype: string |
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- name: income |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 5316802 |
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num_examples: 32561 |
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download_size: 553790 |
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dataset_size: 5316802 |
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license: cc |
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language: |
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- en |
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pretty_name: adult-census-income |
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size_categories: |
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- 10K<n<100K |
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--- |
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# adult-census-income |
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## Overview |
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The adult census income dataset is used for prediction tasks to determine whether a person makes over $50K a year. |
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It can also be used to explore biases in ML algorithms. |
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## Dataset Details |
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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), |
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to explore biases in ML algorithms. The prediction task of this dataset is to determine whether a person makes over 50K a year. |
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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)). |
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- Dataset Name: adult-census-income |
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- Language: English |
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- Total Size: 32,561 demonstrations |
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## Contents |
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The features and values that can be found in the adult census dataset are the following: |
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- **Income:** '>50K' (24,1%), '<=50K'(75,9%). |
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- **Age:** continuous. |
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- **Workclass:** Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. |
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- **fnlwgt:** continuous. |
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- **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. |
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- **Education.num:** continuous. |
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- **Marital.status:** Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. |
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- **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. |
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- **Relationship:** Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. |
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- **Race:** White, Asian-Pac-Islander, Amer-Indian--Eskimo, Other, Black. |
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- **Sex:** Female, Male. |
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- **Capital.gain:** continuous. |
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- **Capital.loss:** continuous. |
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- **Hours.per.week:** continuous. |
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- **Native.country:** United States, Cambodia, England, Puerto Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, |
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Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, |
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Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands. |
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## How to use |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("AiresPucrs/adult-census-income", split='train') |
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``` |
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## License |
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This dataset is licensed under the [Creative Commons(CC)](https://creativecommons.org/publicdomain/zero/1.0/) License CC0 1.0. |
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