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Data Science Dojo <br/>
Copyright (c) 2016 - 2019
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**<span style="color:#E57932">Level:</span>** Intermediate <br/>
**<span style="color:#E57932">Recommended Use:</span>** Classification Models<br/>
**<span style="color:#E57932">Domain:</span>** Social<br/>
This *intermediate* level data set was extracted from the census bureau database. There are 48842 instances of data set, mix of continuous and discrete (train=32561, test=16281).
The data set has 15 attribute which include age, sex, education level and other relevant details of a person. The data set will help to improve your skills in **Exploratory Data Analysis**, **Data Wrangling**, **Data Visualization** and **Classification Models**.
Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
| **Column Position** | **Atrribute Name** | **Definition** | **Data Type** | **Example** | **% Null Ratios** |
|------------------- |---------------- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |-------------- |----------------------------------------- |--------------- |
| 1 | age | Age (years) | Quantitative | 38, 42, 71 | 0 |
| 2 | workclass | Workclass 8 different categories: (Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked) | Qualitative | "Private", Local-gov", "Never-worked" | 6 |
| 3 | fnlwgt | Final Weight* | Quantitative | 83311, 338409 | 0 |
| 4 | education | 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) | Qualitative | "Bachelors", "9th", "Preschool" | 0 |
| 5 | education-num | Years of education | Quantitative | 13, 9, 7 | 0 |
| 6 | marital-status | Marital Status: (Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse) | Qualitative | "Divorced", Separated", "Widowed" | 0 |
| 7 | occupation | 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) | Qualitative | "Tech-support", "Armed Forces", "Sales" | 6 |
| 8 | relationship | Relationship:(Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried) | Qualitative | "Wife", "Unmarried", "Own-child" | 0 |
| 9 | race | Race: (White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black) | Qualitative | "White", "Asian-Pac-Islander", "Other" | 0 |
| 10 | sex | Sex: (Male, Female) | Qualitative | Male, Female | 0 |
| 11 | capital-gain | Amount of capital gained | Quantitative | 14084, 0, 5178 | 0 |
| 12 | capital-loss | Amount of capital lost | Quantitative | 0, 2042, 1902 | 0 |
| 13 | hours-per-week | Number of hours worked per week | Quantitative | 40, 50, 70 | 0 |
| 14 | native-country | 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) | Qualitative | "China", "Italy", "Vietnam" | 2 |
| 15 | income | Either the income is greater than $50,000 or lesser than and equal to $50,000: (>50K, <=50K) | Qualitative | ">50K", "<=50K" | 0 |
*Description of fnlwgt (final weight):
The weights on the CPS files are controlled to independent estimates of the civilian noninstitutional 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:
1. A single cell estimate of the population 16+ for each state.
2. Controls for Hispanic Origin by age and sex.
3. 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.
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Census Income Data Set (UC Irvine)](http://mlr.cs.umass.edu/ml/datasets/Census+Income). The UCI page mentions [US Census Bureau](http://www.census.gov/ftp/pub/DES/www/welcome.html) as the original source of the data set.