Update README.md

README.md

@@ -37,7 +37,60 @@ dataset_info:
     num_examples: 32561
   download_size: 553790
   dataset_size: 5316802
+license: cc
+language:
+- en
+pretty_name: adult-census-income
+size_categories:
+- 10K<n<100K
 ---
-# Dataset Card for "adult_income.csv"
+# adult-census-income
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+## 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.