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FPDclustering (version 2.3.1)

Country_data: Unsupervised Learning on Country Data

Description

Ten vables recorded on 167 countries. The goal is to categorize the countries using socio-economic and health indicators that determine the country's overall development. The data set has been donated by the HELP International organization, an international humanitarian NGO that needs to identify the countries that need aid and asked the analysts to categorize the countries.

Usage

data(Country_data)

Arguments

Format

A data frame with 167 observations and 10 variables.

country

country name

child_mort

Death of children under 5 years of age per 1000 live births

exports

Exports of goods and services per capita. Given as %age of the GDP per capita

health

Total health spending per capita. Given as %age of GDP per capita

imports

Imports of goods and services per capita. Given as %age of the GDP per capita

income

Net income per person

inflation

The measurement of the annual growth rate of the Total GDP

life_expec

The average number of years a new born child would live if the current mortality patterns are to remain the same

total_fer

The number of children that would be born to each woman if the current age-fertility rates remain the same.

gdpp

The GDP per capita. Calculated as the Total GDP divided by the total population.

References

R. Kokkula. Unsupervised learning on country data. kaggle, 2022. URL https://www.kaggle.com/datasets/rohan0301/unsupervised-learning-on-country-data/metadata?resource=download

Examples

Run this code
data(Country_data)
pairs(Country_data[,2:10])

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