world_countries_pop

World countries population from 1960 to 2018

datasets

The main focus is on preprocessing and data visualization of machine learning models performances.
Some functions allow to fill in gaps in time series using linear interpolation on panel data, some functions
permit to draw lift effect and lift curve in order to benchmark machine learning models or you can even find
the optimal number of clusters in agglomerative clustering algorithm.

Simon Corde

helda

Preprocess Data and Get Better Insights from Machine Learning
Models

world_countries_pop function

R Data frame with 15576 obervations and 4 variables<dl class="dl-horizontal">
<dt>country_name</dt><dd>name of the country</dd>
<dt>country_code</dt><dd>code of the country in three letters</dd>
<dt>year</dt><dd>year from 1960 to 2018</dd>
<dt>population</dt><dd>number of people</dd>
</dl>

Format

World countries population from 1960 to 2018 — world_countries_pop

R Data frame with 15576 obervations and 4 variables<dl class='dl-horizontal'>
<dt>country_name</dt><dd>name of the country</dd>
<dt>country_code</dt><dd>code of the country in three letters</dd>
<dt>year</dt><dd>year from 1960 to 2018</dd>
<dt>population</dt><dd>number of people</dd>
</dl>

world_countries_pop: World countries population from 1960 to 2018

Description

Usage

Arguments

Format

Examples