Exploring Global Socioeconomic Clusters

Welcome to the analysis of global socioeconomic clusters, where I embarked on a data-driven journey to understand the diverse economic landscapes that shape our world. In this comprehensive study, I delved into a wealth of socioeconomic data and employ advanced analytics to uncover meaningful insights that not only highlight disparities but also offer a foundation for informed decision-making and humanitarian action.

Objectives

• Understand Global Disparities: To gain insights into the diverse economic landscapes and disparities that exist among countries worldwide.
• Identify Socioeconomic Clusters: Utilize machine learning techniques to segment countries into clusters based on shared socioeconomic characteristics.
• Inform Decision-Making: To rovide data-driven information to aid policymakers, humanitarian organizations, and stakeholders in prioritizing aid efforts and crafting targeted development strategies.
• Foster Sustainable Development: To contribute to the global effort to reduce inequalities and promote sustainable socioeconomic development across nations.

Data Source

The data for this analysis was sourced from Kaggle, a reputable platform for data science and analysis. This dataset contains critical socioeconomic indicators for countries across the globe, forming the bedrock of my exploration into global socioeconomic clusters.

Navigating the Analysis

I began by exploring the essential socioeconomic variables that shape the trajectory of nations. Through visually compelling choropleth maps, I depicted crucial metrics such as GDP per capita, child mortality, income levels, exports, imports, health spending, and inflation on a global canvas.

Then I embarked on a fascinating journey of segmentation, applying the K-Means clustering algorithm to categorize countries into distinct clusters based on their socioeconomic characteristics. This segmentation strategy allowed me to move beyond broad classifications and examine the nuanced challenges and opportunities faced by different groups of nations.

Unveiling Cluster Insights

This analysis goes further than mere clustering; it provides deep insights into each cluster's unique profile. By examining key socioeconomic indicators, it offers a comprehensive understanding of the challenges and potentials inherent in each cluster. From countries in dire need of aid to those enjoying prosperity, and those occupying intermediate positions, each cluster tells a compelling story about the state of our world.

Importing Libraries and Datasets

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import Image
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
import plotly.express as px
import warnings
warnings.filterwarnings("ignore")

# Import dataset
df = pd.read_csv('Country-data.csv')

df.head()

	country	child_mort	exports	health	imports	income	inflation	life_expec	total_fer	gdpp
0	Afghanistan	90.2	10.0	7.58	44.9	1610	9.44	56.2	5.82	553
1	Albania	16.6	28.0	6.55	48.6	9930	4.49	76.3	1.65	4090
2	Algeria	27.3	38.4	4.17	31.4	12900	16.10	76.5	2.89	4460
3	Angola	119.0	62.3	2.85	42.9	5900	22.40	60.1	6.16	3530
4	Antigua and Barbuda	10.3	45.5	6.03	58.9	19100	1.44	76.8	2.13	12200

Exploring data

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 167 entries, 0 to 166
Data columns (total 10 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   country     167 non-null    object 
 1   child_mort  167 non-null    float64
 2   exports     167 non-null    float64
 3   health      167 non-null    float64
 4   imports     167 non-null    float64
 5   income      167 non-null    int64  
 6   inflation   167 non-null    float64
 7   life_expec  167 non-null    float64
 8   total_fer   167 non-null    float64
 9   gdpp        167 non-null    int64  
dtypes: float64(7), int64(2), object(1)
memory usage: 13.2+ KB

df.describe()

	child_mort	exports	health	imports	income	inflation	life_expec	total_fer	gdpp
count	167.000000	167.000000	167.000000	167.000000	167.000000	167.000000	167.000000	167.000000	167.000000
mean	38.270060	41.108976	6.815689	46.890215	17144.688623	7.781832	70.555689	2.947964	12964.155689
std	40.328931	27.412010	2.746837	24.209589	19278.067698	10.570704	8.893172	1.513848	18328.704809
min	2.600000	0.109000	1.810000	0.065900	609.000000	-4.210000	32.100000	1.150000	231.000000
25%	8.250000	23.800000	4.920000	30.200000	3355.000000	1.810000	65.300000	1.795000	1330.000000
50%	19.300000	35.000000	6.320000	43.300000	9960.000000	5.390000	73.100000	2.410000	4660.000000
75%	62.100000	51.350000	8.600000	58.750000	22800.000000	10.750000	76.800000	3.880000	14050.000000
max	208.000000	200.000000	17.900000	174.000000	125000.000000	104.000000	82.800000	7.490000	105000.000000

GDP per country

fig = px.choropleth(df,
                    locationmode='country names',
                    locations='country',
                    color='gdpp',
                    title='Coutries by gdpp'
                   )
fig.show()

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

fig = px.choropleth(df,
                    locationmode='country names',
                    locations='country',
                    color='child_mort',
                    title='Coutries by child_mort'
                   )
fig.show()

Net income per person

fig = px.choropleth(df,
                    locationmode='country names',
                    locations='country',
                    color='income',
                    title='Coutries by income'
                   )
fig.show()

Exports of goods and services per capita

fig = px.choropleth(df,
                    locationmode='country names',
                    locations='country',
                    color='exports',
                    title='Coutries by exports'
                   )
fig.show()

Imports of goods and services per capita

fig = px.choropleth(df,
                    locationmode='country names',
                    locations='country',
                    color='imports',
                    title='Coutries by imports'
                   )
fig.show()

Total health spending per capita

fig = px.choropleth(df,
                    locationmode='country names',
                    locations='country',
                    color='health',
                    title='Coutries by health'
                   )
fig.show()

inflation

fig = px.choropleth(df,
                    locationmode='country names',
                    locations='country',
                    color='inflation',
                    title='Coutries by inflation'
                   )
fig.show()

Applying KMeans

# remove country colomm
df_without_countries = df.drop(['country'],axis=1)

df_without_countries.head()

	child_mort	exports	health	imports	income	inflation	life_expec	total_fer	gdpp
0	90.2	10.0	7.58	44.9	1610	9.44	56.2	5.82	553
1	16.6	28.0	6.55	48.6	9930	4.49	76.3	1.65	4090
2	27.3	38.4	4.17	31.4	12900	16.10	76.5	2.89	4460
3	119.0	62.3	2.85	42.9	5900	22.40	60.1	6.16	3530
4	10.3	45.5	6.03	58.9	19100	1.44	76.8	2.13	12200

Clustring Quality

ks = range(1,6)
inertias = []

for i in ks:
    model = KMeans(i)
    model.fit(df_without_countries)
    inertias.append(model.inertia_)

plt.plot(ks,inertias)
plt.xlabel('Number of Clusters')
plt.ylabel('Inertias')
plt.show()

Depending on elbow method, we will use 3 clusters

Creating a pipeline

scaler = StandardScaler()
kmeans = KMeans(n_clusters=3,random_state=42)
pipeline = make_pipeline(scaler,kmeans)

pipeline.fit(df_without_countries)

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('kmeans', KMeans(n_clusters=3, random_state=42))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

clusters = pipeline.predict(df_without_countries)

clusters

array([1, 2, 2, 1, 2, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 0, 2, 1, 2, 2, 2, 1,
       2, 0, 2, 1, 1, 2, 1, 0, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 2, 0, 0,
       0, 2, 2, 2, 2, 1, 1, 2, 2, 0, 0, 1, 1, 2, 0, 1, 0, 2, 2, 1, 1, 2,
       1, 2, 0, 2, 2, 2, 1, 0, 0, 0, 2, 0, 2, 2, 1, 1, 0, 2, 1, 2, 2, 1,
       1, 2, 2, 0, 2, 1, 1, 2, 2, 1, 0, 1, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2,
       0, 0, 1, 1, 0, 2, 1, 2, 2, 2, 2, 2, 0, 0, 2, 2, 1, 2, 2, 1, 2, 2,
       1, 0, 0, 0, 2, 1, 0, 0, 2, 2, 1, 2, 0, 0, 2, 1, 2, 1, 1, 2, 2, 2,
       2, 1, 2, 0, 0, 0, 2, 2, 2, 2, 2, 1, 1])

country = df.iloc[:,0]

final = pd.DataFrame({'clusters':clusters,'country':country})
final.head()

	clusters	country
0	1	Afghanistan
1	2	Albania
2	2	Algeria
3	1	Angola
4	2	Antigua and Barbuda

fig = px.choropleth(final,
                    locationmode='country names',
                    locations='country',
                    color='clusters',
                    title='Coutries by clusters'
                   )
fig.show()

final_df = pd.merge(final, df, on='country')

Examining the different clusters

final_df[final_df['clusters']==1]. head()

	clusters	country	child_mort	exports	health	imports	income	inflation	life_expec	total_fer	gdpp
0	1	Afghanistan	90.2	10.0	7.58	44.9	1610	9.440	56.2	5.82	553
3	1	Angola	119.0	62.3	2.85	42.9	5900	22.400	60.1	6.16	3530
17	1	Benin	111.0	23.8	4.10	37.2	1820	0.885	61.8	5.36	758
21	1	Botswana	52.5	43.6	8.30	51.3	13300	8.920	57.1	2.88	6350
25	1	Burkina Faso	116.0	19.2	6.74	29.6	1430	6.810	57.9	5.87	575

final_df[final_df['clusters']==0].head()

	clusters	country	child_mort	exports	health	imports	income	inflation	life_expec	total_fer	gdpp
7	0	Australia	4.8	19.8	8.73	20.9	41400	1.160	82.0	1.93	51900
8	0	Austria	4.3	51.3	11.00	47.8	43200	0.873	80.5	1.44	46900
11	0	Bahrain	8.6	69.5	4.97	50.9	41100	7.440	76.0	2.16	20700
15	0	Belgium	4.5	76.4	10.70	74.7	41100	1.880	80.0	1.86	44400
23	0	Brunei	10.5	67.4	2.84	28.0	80600	16.700	77.1	1.84	35300

final_df[final_df['clusters']==2].head()

	clusters	country	child_mort	exports	health	imports	income	inflation	life_expec	total_fer	gdpp
1	2	Albania	16.6	28.0	6.55	48.6	9930	4.49	76.3	1.65	4090
2	2	Algeria	27.3	38.4	4.17	31.4	12900	16.10	76.5	2.89	4460
4	2	Antigua and Barbuda	10.3	45.5	6.03	58.9	19100	1.44	76.8	2.13	12200
5	2	Argentina	14.5	18.9	8.10	16.0	18700	20.90	75.8	2.37	10300
6	2	Armenia	18.1	20.8	4.40	45.3	6700	7.77	73.3	1.69	3220

Insight

Cluster 1: Countries in Dire Need of Aid

Cluster 1 comprises countries that are in dire need of aid. Most of these nations are located in the middle and south of Africa, with a few countries in Asia, such as Yemen and Afghanistan, sharing similar characteristics. These countries exhibit the following key characteristics:

High Child Mortality: Cluster 1 countries experience a high child mortality rate, indicating inadequate healthcare and health infrastructure.
Low GDP per Capita: They have a significantly low GDP per capita, reflecting limited economic prosperity and development.
Low Income Levels: The income per capita in Cluster 1 countries is among the lowest globally, indicating widespread poverty.
High Inflation: These nations also face high inflation rates, further exacerbating economic challenges.
Lower Life Expectancy: Life expectancy is notably lower in Cluster 1, underscoring health-related issues.
Minimal Health Spending: Total health spending per capita is minimal, leading to inadequate healthcare services.

Cluster 0: Developed and Prosperous Countries

Cluster 0 encompasses countries that are relatively developed and enjoy higher standards of living. These nations exhibit the following common traits:

Low Child Mortality: Cluster 0 countries boast low child mortality rates, indicating access to quality healthcare and improved living conditions.
High GDP per Capita: They possess a high GDP per capita, reflecting strong economic performance and prosperity.
High Income Levels: Cluster 0 nations have high income levels, resulting in a higher standard of living.
Low Inflation: Inflation rates are low in these countries, contributing to economic stability.
Higher Life Expectancy: Life expectancy is notably higher in Cluster 0, indicating better healthcare and living conditions.

Cluster 2: Intermediate Position Countries

Cluster 2 includes countries that occupy an intermediate position between the other two clusters. These nations exhibit the following characteristics:

Moderate Child Mortality: Cluster 2 countries have moderate child mortality rates, suggesting a mixed healthcare and living conditions scenario.
Moderate GDP per Capita: They possess moderate GDP per capita, indicating moderate economic development.
Moderate Income Levels: Income levels in Cluster 2 countries are at an intermediate range.
Moderate Inflation: Inflation rates are moderate, contributing to economic stability.
Moderate Life Expectancy: Life expectancy in Cluster 2 falls within a moderate range, indicating a mixed healthcare situation.