Introduction

The Country Health Trends Dataset sourced from Kaggle provides key indicators such as life expectancy, fertility rate, population, and regional classifications for countries worldwide. This data allows researchers to explore global development trends, perform comparative analysis across regions, and investigate correlations between health and socioeconomic factors. This report provides a statistical overview and analysis of these indicators, uncovering insights into global health disparities and patterns across regions.

Objectives

The main objectives of this analysis are as follows:

1. Perform Exploratory Data Analysis (EDA) to understand the distribution and range of key indicators.
2. Conduct comparative analysis across regions for life expectancy and fertility rate.
3. Explore correlations between life expectancy and fertility rate.
4. Visualize global life expectancy using an interactive map.
5. Highlight population distribution by region.

Data Overview

The dataset contains the following columns:

• Country: Name of the country or territory.
• Life Expectancy: Average years a newborn is expected to live if current mortality rates continue.
• Fertility Rate: Average number of children a woman would have over her lifetime.
• Population: Total population of the country.
• Region: Geographical region or political classification of each country.

Data Summary:

• Total Entries: 191
• Regions Represented: Six main regions, including America, Europe & Central Asia, East Asia & Pacific, Middle East & North Africa, South Asia, and Sub-Saharan Africa.

Methodology

1. Data Cleaning and Preparation:
   • Checked and handled missing values.
   • Prepared the data for region-wise analysis by grouping and summarizing statistics for each region.
2. Summary Statistics by Region:
   • Calculated the mean, median, minimum, and maximum for life expectancy and fertility rate across each region.
   • Aggregated population data to determine total and average population for each region.
3. Exploratory Data Analysis (EDA):
   • Visualized the distribution of life expectancy and fertility rates to understand skewness and distribution patterns.
   • Created box plots to compare life expectancy and fertility rate across regions.
   • Constructed scatter plots to assess relationships between life expectancy, fertility rate, and population size.
4. Correlation Analysis:
   • Used Pearson correlation to evaluate the relationship between life expectancy and fertility rate.
5. Geospatial and Population Visualizations:
   • Developed an interactive world map of life expectancy by country.
   • Plotted a bar chart showing population distribution by region.

Key Findings

Regional Summary of Life Expectancy and Fertility Rate

• Life Expectancy:
  • The highest average life expectancies are observed in America (73.2 years) and Middle East & North Africa (72.8 years), while the lowest is found in Sub-Saharan Africa (56.5 years).
• Fertility Rate:
  • Regions such as Sub-Saharan Africa exhibit significantly higher fertility rates (average 5.4), while Europe & Central Asia show the lowest rates (average 1.7).
• Population:
  • South Asia leads in total population, suggesting higher resource needs, followed by Sub-Saharan Africa and Europe & Central Asia.

Distribution Analysis

• Life Expectancy Distribution:

• The life expectancy distribution displays a negative skew, indicating that most countries have higher life expectancies, with a few outliers on the lower side.
• Fertility Rate Distribution:

• Fertility rate has a positive skew, with several countries displaying high fertility rates, especially in Sub-Saharan Africa.

Comparative Analysis of Life Expectancy and Fertility Rate by Region

• Life Expectancy:

• A box plot reveals that America and Middle East & North Africa have relatively higher and more consistent life expectancy values.
• Sub-Saharan Africa shows both the lowest median and broader range, indicating variability in healthcare and socioeconomic conditions.
• Fertility Rate:

• Fertility rates are notably high in Sub-Saharan Africa, while Europe & Central Asia consistently exhibit low fertility, aligning with trends in more economically developed regions.

Correlation Analysis: Life Expectancy vs. Fertility Rate

• A negative correlation (Pearson’s r = -0.85) was observed between life expectancy and fertility rate, suggesting that higher fertility rates tend to coincide with lower life expectancy. This pattern aligns with demographic transition models, where lower fertility rates are often seen in countries with higher life expectancy due to improved healthcare and economic conditions.

Geospatial Analysis of Life Expectancy

• A map highlights countries with high life expectancies, including Iceland, Sweden, Canada, and Spain. This visualization reveals stark contrasts across regions, particularly between developed nations and regions such as Sub-Saharan Africa.

Population Distribution by Region

• A bar chart indicates that South Asia holds the largest population, followed by Sub-Saharan Africa and Europe & Central Asia. This regional breakdown emphasizes the need for tailored health policies in densely populated areas to address specific regional health challenges.

Conclusion

This analysis of global health and socioeconomic data reveals significant disparities in life expectancy and fertility rates across regions. A clear inverse relationship exists between life expectancy and fertility rate, aligning with global health and economic development patterns. The regional comparisons underscore the influence of socioeconomic factors, with Sub-Saharan Africa facing unique challenges related to high fertility rates and lower life expectancies, while regions like America and Europe & Central Asia exhibit higher life expectancy and lower fertility rates.

Recommendations for Further Analysis

1. Time-Series Analysis: Examining trends over time would allow for tracking progress in life expectancy and fertility rates.
2. Income and Education Analysis: Adding these variables could clarify the socioeconomic factors influencing life expectancy and fertility rates.
3. Policy Impact Study: Assessing the effect of health policies on life expectancy and fertility rate changes across regions could provide actionable insights for policymakers.