Problem

Life expectancy varies significantly across different countries, influenced by a complex interplay of socio-economic, health, and environmental factors. Understanding these factors is crucial for policymakers to develop strategies that improve population health and reduce disparities in life expectancy. However, the relationships between these variables are not always clear, and there is a need for a comprehensive analysis that can guide effective policy interventions.

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Solution

This project analyzed the life expectancy data of 183 countries from 2000 to 2015 using various statistical and machine learning techniques:

Exploratory Data Analysis: Identified trends and variations in life expectancy over time.
Correlation Analysis: Examined the relationships between life expectancy and key variables such as adult mortality, schooling, and income composition.
ANOVA and Multiple Regression: Determined the statistical significance of these variables and developed a predictive model for life expectancy.
K-Means Clustering: Grouped countries into clusters based on their life expectancy and related factors to identify patterns among different regions.

Impact

Our analysis revealed several key insights:

Global Trends: Life expectancy generally increased over the study period, with a decrease in data dispersion, indicating narrowing disparities.
Key Predictors: Adult mortality, schooling, and income composition were identified as significant predictors of life expectancy. These findings suggest that improvements in education and economic resources, along with reductions in adult mortality, could substantially increase life expectancy.
Cluster Analysis: The clustering revealed distinct groups of countries, with developed nations exhibiting higher life expectancy and better socio-economic indicators, while underdeveloped regions, particularly in Asia and Africa, lagged behind. These insights provide a clear direction for targeted policy interventions, particularly in underdeveloped regions.

"This study has shed light on critical issues related to global health trends that will inform policy decisions for years to come."

Team

Team Member #1 - Data Analyst
- Collected and organized data from multiple sources
- Conducted statistical analysis to identify trends and patterns in the data
Team Member #2 - Visualization Specialist
- Created visualizations such as graphs and charts to help present our findings in an easy-to-understand format
- Ensured that all visualizations were accurate and consistent with the data analysis

Analyzing Life Expectancy Trends in 183 Countries