As of 2023, the countries with the highest life expectancy included Switzerland, Japan, and Spain. As of that time, a new-born child in Switzerland could expect to live an average of **** years. Around the world, females consistently have a higher average life expectancy than males, with females in Europe expected to live an average of *** years longer than males on this continent. Increases in life expectancy The overall average life expectancy in OECD countries increased by **** years from 1970 to 2019. The countries that saw the largest increases included Turkey, India, and South Korea. The life expectancy at birth in Turkey increased an astonishing 24.4 years over this period. The countries with the lowest life expectancy worldwide as of 2022 were Chad, Lesotho, and Nigeria, where a newborn could be expected to live an average of ** years. Life expectancy in the U.S. The life expectancy in the United States was ***** years as of 2023. Shockingly, the life expectancy in the United States has decreased in recent years, while it continues to increase in other similarly developed countries. The COVID-19 pandemic and increasing rates of suicide and drug overdose deaths from the opioid epidemic have been cited as reasons for this decrease.

In 2024, the average life expectancy in the world was 71 years for men and 76 years for women. The lowest life expectancies were found in Africa, while Oceania and Europe had the highest. What is life expectancy?Life expectancy is defined as a statistical measure of how long a person may live, based on demographic factors such as gender, current age, and most importantly the year of their birth. The most commonly used measure of life expectancy is life expectancy at birth or at age zero. The calculation is based on the assumption that mortality rates at each age were to remain constant in the future. Life expectancy has changed drastically over time, especially during the past 200 years. In the early 20th century, the average life expectancy at birth in the developed world stood at 31 years. It has grown to an average of 70 and 75 years for males and females respectively, and is expected to keep on growing with advances in medical treatment and living standards continuing. Highest and lowest life expectancy worldwide Life expectancy still varies greatly between different regions and countries of the world. The biggest impact on life expectancy is the quality of public health, medical care, and diet. As of 2022, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and Australia, all at 84–83 years. Most of the countries with the lowest life expectancy are mostly African countries. The ranking was led by the Chad, Nigeria, and Lesotho with 53–54 years.

Men born in Chad have the lowest life expectancy in the world as of 2024, reaching only ** years. The lowest life expectancy for women in the world in 2024 was for girls born in Nigeria, with only ** years. Except for Afghanistan, all the countries with the lowest life expectancy in the world are in Africa.

The research on life expectancy in countries takes the spotlight in the notebook's machine learning model. Substantial data analysis and predictive algorithms are used to uncover the reasons causing differences in longevity among countries. With the aid of strong statistical tools, valuable insights into the complex link between healthcare, socioeconomic factors, and life expectancy are sought |Description|Column| |:------:|:--------:| |Country under study|Country| |year|Year| |Status of the country's development|Status| |Population of country|Population| |Percentage of people finally one year old who were immunized against hepatitis B|Hepatitis B| |The number of reported measles cases per 1000 people|Measles| |Percentage of 1-year-olds immunized against polio|Polio| |Percentage of people finally one year old who were immunized against diphtheria|Diphtheria| |The number of deaths caused by AIDS of the last 4-year-olds who were born alive per 1000 people|HIV/AIDS| |The number of infant deaths per 1000 people|infant deaths| |he number of deaths of people under 5 years old per 1000 people|under-five deaths| |The ratio of government medical-health expenses to total government expenses in percentage|Total expenditure| |Gross domestic product|GDP| |The average body mass index of the entire population of the country|BMI| |Prevalence of thinness among people 19 years old in percentage|thinness 1-19 years| |Liters of alcohol consumption among people over 15 years old|Alcohol| |The number of years that people study|Schooling| |Country life expectancy|Life expectancy [target variable]|

File Description: "Life Expectancy Data.csv" This dataset contains 2,938 entries and 22 columns, covering life expectancy and related health indicators for multiple nations from 2000 to 2015. It includes country-wise data and other economic, social, and health metrics. Column Description: 1. Country – Name of the country. 2. Year – Data year (ranging from 2000 to 2015). 3. Status – Economic classification (Developing/Developed). 4. Life expectancy – Average lifespan in years. 5. Adult Mortality – Probability of death between ages 15-60 per 1,000 individuals. 6. Infant Deaths – Number of infant deaths per 1,000 live births. 7. Alcohol – Per capita alcohol consumption. 8. Percentage Expenditure – Government health expenditure as a percentage of GDP. 9. Hepatitis B – Immunization coverage percentage. 10. Measles – Number of reported measles cases. 11. BMI – Average Body Mass Index. 12. Under-Five Deaths – Mortality rate for children under five. 13. Polio & Diphtheria – Immunization rates. 14. HIV/AIDS – Deaths due to HIV/AIDS per 1,000 individuals. 15. GDP – Gross Domestic Product per capita. 16. Population – Total population of the country. 17. Thinness (1-19 years, 5-9 years) – Percentage of underweight children. 18. Income Composition of Resources– Human development index proxy. 19. Schooling– Average number of years of schooling. Missing Data: Some columns (like Hepatitis B, GDP, Population, Total Expenditure) contain missing values. Further File Information: Total Countries: 193 Years Covered: 2000–2015 Total Entries: 2,938 Missing Data Overview: Some columns have missing values, notably: Hepatitis B (553 missing) GDP (448 missing) Population (652 missing) Total expenditure (226 missing) Income Composition of Resources (167 missing) Schooling (163 missing) Summary Statistics: Life Expectancy:

Range: 36.3 to 89 years Mean: 69.2 years Adult Mortality:

Mean: 165 per 1,000 Max: 723 per 1,000 GDP per Capita:

Mean: $7,483 Max: $119,172 Population:

Mean: ~12.75 million Max: 1.29 billion Education:

Schooling Average: 12 years Max: 20.7 years

Futuristic Scope of this data: For comparative analysis of the 2000–2015 life expectancy dataset with new datasets on the same parametres , you can perform several statistical tests and analytical methods based on different research questions. Below are some key tests and approaches:

1. Trend Analysis (Time-Series) Objective: Identify trends in life expectancy and related indicators over time. Methods: Moving Averages: Smooth fluctuations to detect trends. Linear/Polynomial Regression: Check whether life expectancy follows an increasing or decreasing trend. Time-Series Decomposition: Separate data into trend, seasonality, and residuals.
2. Descriptive Statistics & Comparative Summary Objective: Compare summary statistics between years or groups. Tests/Methods: Mean, Median, Standard Deviation: Compare distributions of life expectancy, GDP, or schooling. Boxplots & Histograms: Show variations over different years or between developing vs. developed countries. Coefficient of Variation (CV): Compare variability in life expectancy across regions.
3. Correlation & Regression Analysis Objective: Examine relationships between variables. Methods: Pearson/Spearman Correlation: Check relationships between life expectancy and GDP, health expenditure, etc. Multiple Linear Regression: Predict life expectancy based on GDP, immunization, and schooling. Multicollinearity (VIF Test): Ensure independent variables are not highly correlated.
4. Hypothesis Testing (Comparative Analysis) Test Objective When to Use? t-Test (Independent Samples) Compare life expectancy between developed & developing nations Two groups (e.g., 2000 vs. 2015, or developed vs. developing) Paired t-Test Compare life expectancy in the same country over two time periods Before/after comparison (e.g., 2000 vs. 2015 for the same country) ANOVA (One-Way) Compare life expectancy across multiple groups More than two groups (e.g., continents or income groups) Chi-Square Test Test if categorical distributions (e.g., immunization coverage) differ over time Categorical variables (e.g., immunization rates vs. year)
5. Clustering & Classification (Machine Learning) Objective: Group countries based on life expectancy patterns. Methods: K-Means Clustering: Identify groups with similar life expectancy trends. Hierarchical Clustering: Create a country similarity tree. Decision Trees/Random Forest: Classify countries based on development status using life expectancy factors.
6. Forecasting Future Trends Objective: Predict life expectancy in future years using historical data. Methods: ARIMA (AutoRegressive Integrated Moving Average): Time-series forecasting. Exponential Smoothing: Forecast gradual trends. Machine Learning (LSTM, XGBoost): Predict based on multiple indicators.
7. Comparative Regional Analysis O...

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information.

The COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury".

The data are derived from the medical certificate of death, which is obligatory in the Member States. The information recorded in the death certificate is according to the rules specified by the WHO.

Data published in Eurostat's dissemination database are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdowns might include age of mother and parity.

Data are available for Member States, Iceland, Norway, Liechtenstein, Switzerland, United Kingdom, Serbia, Turkey, North Macedonia and Albania. Regional data (NUTS level 2) are available for all of the countries having NUTS2 regions except Albania.

Annual national data are available in Eurostat's dissemination database in absolute number, crude death rates and standardised death rates. At regional level the same is provided in form of 3-years averages (the average of year, year -1 and year -2). Annual crude and standardised death rates are also available at NUTS2 level. Monthly national data are available for 21 EU Member States from reference year 2019 and in 24 Member States from reference year 2022 in absolute numbers and standardised death rates.

International estimates of mean life expectancy at age 40, by country for men and women

A global phenomenon, known as the demographic transition, has seen life expectancy from birth increase rapidly over the past two centuries. In pre-industrial societies, the average life expectancy was around 24 years, and it is believed that this was the case throughout most of history, and in all regions. The demographic transition then began in the industrial societies of Europe, North America, and the West Pacific around the turn of the 19th century, and life expectancy rose accordingly. Latin America was the next region to follow, before Africa and most Asian populations saw their life expectancy rise throughout the 20th century.

This scatter chart displays median age (year) against death rate (per 1,000 people). The data is about countries.

Monaco had the highest life expectancy among both men and women worldwide as of 2024. That year, life expectancy for men and women was ** and ** years, respectively. The East Asian countries and regions, Hong Kong, Japan, South Korea, and Macao, followed. Many of the countries on the list are struggling with aging populations and a declining workforce as more people enter retirement age compared to people entering employment.

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information.

The COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury".

The data are derived from the medical certificate of death, which is obligatory in the Member States. The information recorded in the death certificate is according to the rules specified by the WHO.

Data published in Eurostat's dissemination database are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdowns might include age of mother and parity.

Data are available for Member States, Iceland, Norway, Liechtenstein, Switzerland, United Kingdom, Serbia, Turkey, North Macedonia and Albania. Regional data (NUTS level 2) are available for all of the countries having NUTS2 regions except Albania.

Annual national data are available in Eurostat's dissemination database in absolute number, crude death rates and standardised death rates. At regional level the same is provided in form of 3-years averages (the average of year, year -1 and year -2). Annual crude and standardised death rates are also available at NUTS2 level. Monthly national data are available for 21 EU Member States from reference year 2019 and in 24 Member States from reference year 2022 in absolute numbers and standardised death rates.

This dataset is about countries per year in Micronesia. It has 64 rows. It features 4 columns: country, death rate, and median age.

Mean, median and modal ages at death in the UK and its constituent countries, 2001 to 2003 and 2016 to 2018.

The average for 2023 based on 12 countries was 75.35 years. The highest value was in Chile: 81.17 years and the lowest value was in Bolivia: 68.58 years. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

Average age of deaths of the Basque Country by province, large groups cause of death (CIE-9) and sex (from 1996 - to 1998)

Data on causes of death (COD) provide information on mortality patterns and form a major element of public health information.

The COD data refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury".

The data are derived from the medical certificate of death, which is obligatory in the Member States. The information recorded in the death certificate is according to the rules specified by the WHO.

Data published in Eurostat's dissemination database are broken down by sex, 5-year age groups, cause of death and by residency and country of occurrence. For stillbirths and neonatal deaths additional breakdowns might include age of mother and parity.

Data are available for Member States, Iceland, Norway, Liechtenstein, Switzerland, United Kingdom, Serbia, Turkey, North Macedonia and Albania. Regional data (NUTS level 2) are available for all of the countries having NUTS2 regions except Albania.

Annual national data are available in Eurostat's dissemination database in absolute number, crude death rates and standardised death rates. At regional level the same is provided in form of 3-years averages (the average of year, year -1 and year -2). Annual crude and standardised death rates are also available at NUTS2 level. Monthly national data are available for 21 EU Member States from reference year 2019 and in 24 Member States from reference year 2022 in absolute numbers and standardised death rates.

This dataset is about countries per year in Norway. It has 64 rows. It features 4 columns: country, death rate, and median age.

Context

I am developing my data science skills in areas outside of my previous work. An interesting problem for me was to identify which factors influence life expectancy on a national level. There is an existing Kaggle data set that explored this, but that information was corrupted. Part of the problem solving process is to step back periodically and ask "does this make sense?" Without reasonable data, it is harder to notice mistakes in my analysis code (as opposed to unusual behavior due to the data itself). I wanted to make a similar data set, but with reliable information.

This is my first time exploring life expectancy, so I had to guess which features might be of interest when making the data set. Some were included for comparison with the other Kaggle data set. A number of potentially interesting features (like air pollution) were left off due to limited year or country coverage. Since the data was collected from more than one server, some features are present more than once, to explore the differences.

Content

A goal of the World Health Organization (WHO) is to ensure that a billion more people are protected from health emergencies, and provided better health and well-being. They provide public data collected from many sources to identify and monitor factors that are important to reach this goal. This set was primarily made using GHO (Global Health Observatory) and UNESCO (United Nations Educational Scientific and Culture Organization) information. The set covers the years 2000-2016 for 183 countries, in a single CSV file. Missing data is left in place, for the user to decide how to deal with it.

Three notebooks are provided for my cursory analysis, a comparison with the other Kaggle set, and a template for creating this data set.

Inspiration

There is a lot to explore, if the user is interested. The GHO server alone has over 2000 "indicators". - How are the GHO and UNESCO life expectancies calculated, and what is causing the difference? That could also be asked for Gross National Income (GNI) and mortality features. - How does the life expectancy after age 60 compare to the life expectancy at birth? Is the relationship with the features in this data set different for those two targets? - What other indicators on the servers might be interesting to use? Some of the GHO indicators are different studies with different coverage. Can they be combined to make a more useful and robust data feature? - Unraveling the correlations between the features would take significant work.

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National Records of Scotland Guidance;What is ‘period’ life expectancyAll of the estimates presented in this report are ‘period’ life expectancy. They are calculated assuming that mortality rates for each age group in the time period (here 2021-2023) are constant throughout a person’s life. Period life expectancy is often described as how long a baby born now could expect to live if they experienced today’s mortality rates throughout their lifetime. It is very unlikely that this would be the case as it means that future changes in things such as medicine and legislation are not taken into consideration.Period life expectancy is not an accurate prediction of how long a person born today will actually live, but it is a useful measure of population health at a point in time and is most useful for comparing trends over time, between areas of a country and with other countries.How national life expectancy is calculatedThe latest life expectancy figures are calculated from the mid-year population estimates for Scotland and the number of deaths registered in Scotland during 2021, 2022, and 2023. Life expectancy for Scotland is calculated for each year of age and represents the average number of years that someone of that age could expect to live if death rates for each age group remained constant over their lifetime. Life expectancy in Scotland is calculated as a three-year average, produced by combining deaths and population data for the three-year period. Three years of data are needed to provide large enough numbers to make these figures accurate and lessen the effect of very ‘good’ or ‘bad’ years. Throughout this publication, the latest life expectancy figures refer to 2021-2023 period. How sub-national life expectancy is calculatedWe calculate life expectancy for areas within Scotland using a very similar method to the national figures but with a few key differences. Firstly, we use age groups rather than single year of age. This is to increase the population size of each age group to reduce fluctuations and ensure accurate calculation of mortality rates. Secondly, we use a maximum age group of 90+ whereas the national figures are calculated up to age 100. These are known as ‘abridged life tables.’ Because these methods produce slightly different figures, we also calculate a Scotland figure using the abridged method to allow for accurate comparisons between local areas for example. This Scotland figure is only for comparison and does not replace the headline national figure. You can read more information about the methods in this publication in our methodology guide on the NRS website. Uses of life expectancyLife expectancy at birth is a very useful indicator of mortality conditions across a population at a particular point in time. It also provides an objective means of comparing trends in mortality over time, between areas of a country and with other countries. This is used to monitor and investigate health inequalities and to set public health targets. Life expectancy is also used to inform pensions policy, research and teaching.