International Funding for Development Country

Sanbercode

Python

Data Science

HELP International is an international non-governmental organization committed to fighting poverty and providing basic facilities and assistance to communities in underdeveloped countries during times of disaster and natural disasters. HELP International has successfully raised around $10 million. Now, the CEO of HELP International needs to decide how to use the money strategically and effectively. Therefore, the CEO must make a decision on which country most needs aid.

Objective - Categorizing countries based on social, economic, and health factors that determine overall country development as a basis for recommending which countries have the highest urgency to receive aid from HELP International.

  • All Parts
  • Analysis
  • Visualization
  • Insights

The level of welfare of a country is influenced by socio-economic, health, physical & environmental, legal, and the potential of its citizens. Generally, socio-economic and health factors are often the main considerations in determining development decisions because these two factors have a large impact and can be felt directly by all layers of citizens.

Features Selection - features that will be used as the basis for further analysis and clustering are GDP per capita and Life Expectancy. GDP per capita can reveal the level of prosperity of a country and its people, and the life expectancy rate is a key indicator in assessing the health status of a country. No missing values were found, so the dataframe can be directly processed for further analysis. Also, handling outliers for preparing analysis.

Before proceeding with clustering, we will continue by finding the number of clusters. The elbow method can be used to find the value of n. Based on the analysis within the range of 1-5, it was found that the variation of the n value is 3.

The dots (.) represented for outliers in the data. Picture before handling outliers.

The analysis uses two variables or features. The image shows that based on the data of GDP per capita and life expectancy, the data has a concentration of tendency in the low range for GDP per capita but quite high for life expectancy.

Image shows elbow method result.

The figure explains the clustering results. Based on the value of n, three groups (clusters) are generated. (1-Red) data with low GDP per capita and life expectancy. (2-Olive) group of data with low GDP per capita but high life expectancy. (3-Green) group of data with high GDP per capita and high life expectancy.

The majority of the countries in the dataframe have a life expectancy rate above average. This means that most of the countries can be categorized as moderately to very prosperous/well-off.

Most countries have relatively low GDP per capita, indicating poor economic and social conditions.

Based on the clustering results, five countries with the lowest GDP per capita and life expectancy were found. These countries are Haiti, Lesotho, the Central African Republic, Zambia, and Malawi.

This figure illustrates the locations of countries that are most in need of assistance based on socio-economic and health factors on the world map.

© fahrunisays, All Right Reserved.
Template By HTML Codex
Distributed By: ThemeWagon