adult#

The data set contains features for binary prediction of the income of an adult (the adult dataset).

The data has 48842 records with 14 features and one binary target column, referring to whether the person’s income is greater than 50K.

This is a copy of UCI ML Adult dataset. https://archive.ics.uci.edu/ml/datasets/adult

References:

Ron Kohavi, “Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid”, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996

The typical ML task in this dataset is to build a model that determines whether a person makes over 50K a year.

Dataset Shape:

Description:

Dataset Description#
Column name	Column Role	Description
Age	Feature	The age of the person.
workclass	Feature	[Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked]
fnlwgt	Feature	Final weight.
education	Feature	[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	Feature	Number of years of education
marital-status	Feature	[Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse]
occupation	Feature	[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]
relationship	Feature	[Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried]
race	Feature	[White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black]
sex	Feature	[Male, Female]
capital-gain	Feature	The capital gain of the person
capital-loss	Feature	The capital loss of the person
hours-per-week	Feature	The number of hours worked per week
native-country	Feature	[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]
target	Target	The target variable, whether the person makes over 50K a year.

Functions

`load_data`([data_format, as_train_test])	Load and returns the Adult dataset (classification).
`load_fitted_model`([pretrained])	Load and return a fitted classification model.

load_fitted_model

load_data