Credits

This dataset was from tekkum and the original file was in xlsx format.

Context

While numerous studies have explored the factors influencing life expectancy, most have focused on demographic variables, economic indicators, and mortality rates. However, there has been limited examination of the impact of immunization coverage, health expenditures, and educational attainment on life expectancy. This study seeks to address these gaps by developing a comprehensive dataset with no missing values analyses, utilizing data from many years across 193 different countries. Key immunizations such as Hepatitis B, Polio, and Diphtheria, along with factors like GDP, schooling, and health expenditure, are included in this dataset. This approach aims to identify the most significant predictors of life expectancy, allowing countries to prioritize interventions that could most effectively improve the health and longevity of their populations.

Dataset

The success of this analysis relies heavily on the accuracy and completeness of the data. The dataset used in this project has been sourced from the Global Health Observatory (GHO) data repository of the World Health Organization (WHO), which tracks health metrics and related factors for countries worldwide. The corresponding economic data was obtained from the United Nations. From the broad range of health-related variables available, this study focuses on those that are most representative and critical to understanding life expectancy. The dataset includes data for 193 countries and has been meticulously merged into a single file containing 22 columns and 2,938 rows, representing 20 predictive variables. The variables were categorized into four main groups: Immunization-related factors, Mortality factors, Economic factors, and Social factors. Countries with a lot of missing values were excluded, and some values were generated by Bayesian Ridge.

Question

This dataset aims to answer the following key questions:

1. Do the selected predictive factors significantly impact life expectancy, and which variables are the most influential?

2. Should countries with a lower life expectancy (below 65 years) increase healthcare expenditure to improve their population's lifespan?

3. How do infant and adult mortality rates influence life expectancy across different regions?

4. What is the relationship between life expectancy and lifestyle factors such as alcohol consumption?

5. How does educational attainment, as measured by years of schooling, affect human lifespan?

6. Is there a positive or negative correlation between alcohol consumption and life expectancy?

7. What is the impact of immunization coverage on life expectancy, particularly regarding diseases like Hepatitis B, Polio, and Diphtheria?