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Global Life Expectancy Predictors
Data Science and Analytics
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About
Explores the key predictors that influence life expectancy across the globe. This dataset contains health status, mortality rates, immunisation factors, and economic indicators for 193 countries. It is designed to facilitate the understanding of factors contributing to life expectancy and can be used to build predictive models. Analysis using Linear Regression, Decision Tree, and Random Forest models has shown that these variables can explain a significant amount of the variance in life expectancy, with Random Forest being the most accurate predictive model.
Columns
- Country: The name of the country.
- Year: The year of the recorded data, ranging from 2000 to 2015.
- Status: The development status of the country, either 'Developing' or 'Developed'.
- Life expectancy: The life expectancy in years.
- Adult Mortality: The probability of dying between 15 and 60 years of age, per 1000 people.
- infant deaths: The number of infant deaths per 1000 people.
- Alcohol: Per capita consumption of alcohol (for those 15+) in litres of pure alcohol.
- percentage expenditure: Health expenditure as a percentage of Gross Domestic Product (GDP) per capita.
- Hepatitis B: The percentage of 1-year-olds with Hepatitis B immunisation coverage.
- Measles: The number of reported measles cases per 1000 people.
- BMI: The average Body Mass Index of the population.
- under-five deaths: The number of deaths of children under five years old per 1000 people.
- Polio: The percentage of 1-year-olds with Polio immunisation coverage.
- Total expenditure: General government expenditure on health as a percentage of total government expenditure.
- Diphtheria: The percentage of 1-year-olds with Diphtheria immunisation coverage.
- HIV/AIDS: Deaths per 1000 live births due to HIV/AIDS for children under 5.
- GDP: Gross Domestic Product per capita in US dollars.
- Population: The population of the country.
- thinness 1-19 years: The prevalence of thinness among individuals aged 10-19 years.
- thinness 5-9 years: The prevalence of thinness among children aged 5-9 years.
- Income composition of resources: A human development index metric based on income resources, from 0 to 1.
- Schooling: The average number of years of schooling.
Distribution
- Format: CSV
- File Size: 333.44 kB
- Structure: The dataset contains 2938 records and 22 columns.
Usage
- Predictive Modelling: Ideal for building and comparing machine learning models (like Linear Regression, Decision Tree, and Random Forest) to predict life expectancy.
- Public Health Analysis: Researchers can investigate the relationship between health indicators (e.g., immunisation, mortality) and life expectancy.
- Socioeconomic Studies: Analyse the impact of economic factors like GDP, schooling, and income on population health outcomes.
- Global Health Benchmarking: Compare health and development metrics across different countries and regions over time.
Coverage
- Geographic: Covers 193 countries worldwide.
- Time Range: Data spans from the year 2000 to 2015.
- Demographic: Includes various health and economic indicators across populations, with specific metrics for different age groups (e.g., infant mortality, adult mortality).
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For developing and testing predictive health models and practicing data cleaning techniques.
- Public Health Researchers: To study determinants of health and inform policy decisions.
- Economists: To explore the links between economic development and health outcomes.
- Students and Academics: As a practical dataset for projects in statistics, machine learning, and social sciences.
Dataset Name Suggestions
- Global Life Expectancy Predictors
- WHO Health and Economic Data for Life Expectancy
- Worldwide Mortality and Health Indicators (2000-2015)
- Predicting Life Expectancy: A Global Dataset
Attributes
Original Data Source: Global Life Expectancy Predictors
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