About the Dataset

This dataset contains Quality of Life indices for various countries around the globe, extracted from the Numbeo website. The data provides valuable metrics for comparing countries based on several aspects of living standards, which can assist in decisions such as choosing a place to live or analyzing global trends in quality of life.

OBS: The code to generate this dataset is presented on: https://www.kaggle.com/code/marcelobatalhah/web-scrapping-quality-of-life-index

Columns in the Dataset

1. Rank:
   The global rank of the country based on its Quality of Life Index according to Year (1 = highest quality of life).

2. Country:
   The name of the country.

3. Quality of Life Index:
   A composite index that evaluates the overall quality of life in a country by combining other indices, such as Safety, Purchasing Power, and Health Care.

4. Purchasing Power Index:
   Measures the relative purchasing power of the average consumer in a country compared to New York City (baseline = 100).

5. Safety Index:
   Indicates the safety level of a country. A higher score suggests a safer environment.

6. Health Care Index:
   Evaluates the quality and accessibility of healthcare in the country.

7. Cost of Living Index:
   Measures the relative cost of living in a country compared to New York City (baseline = 100).

8. Property Price to Income Ratio:
   Compares the affordability of real estate by dividing the average property price by the average income.

9. Traffic Commute Time Index:
   Reflects the average time spent commuting due to traffic.

10. Pollution Index:
    Rates the level of pollution in the country (air, water, etc.).

11. Climate Index:
    Rates the favorability of the climate in the country (higher = more favorable).

12. Year:
    Year when the metrics were extracted.

Key Insights from the Dataset

• The Quality of Life Index aggregates multiple indicators, making it a useful single metric to compare countries.
• Specific indices such as Safety Index or Health Care Index allow for focused analysis on areas like security or healthcare quality.
• Cost of Living Index and Purchasing Power Index can help determine the affordability of living in each country.

How the Data Was Collected

• The dataset was built using web scraping techniques in Python.
• The data was extracted from the "Quality of Life Rankings by Country" page on Numbeo.
• Libraries used:
  • requests for retrieving webpage content.
  • BeautifulSoup for parsing the HTML and extracting relevant information.
  • pandas for organizing and storing the data in a structured format.

Possible Applications

1. Relocation Decision Making:
   Use the dataset to compare countries and identify destinations with high quality of life, safety, and healthcare.

2. Global Analysis:
   Perform exploratory data analysis (EDA) to identify trends and correlations across quality of life metrics.

3. Visualization:
   Plot global maps, bar charts, or other visualizations to better understand the data.

4. Predictive Modeling:
   Use this dataset as a base for machine learning tasks, like predicting Quality of Life Index based on other metrics.

Luxembourg stands out as the European leader in quality of life for 2025, achieving a score of 220 on the Quality of Life Index. The Netherlands follows closely behind with 211 points, while Albania and Ukraine rank at the bottom with scores of 104 and 115 respectively. This index provides a thorough assessment of living conditions across Europe, reflecting various factors that shape the overall well-being of populations and extending beyond purely economic metrics. Understanding the quality of life index The quality of life index is a multifaceted measure that incorporates factors such as purchasing power, pollution levels, housing affordability, cost of living, safety, healthcare quality, traffic conditions, and climate, to measure the overall quality of life of a Country. Higher overall index scores indicate better living conditions. However, in subindexes such as pollution, cost of living, and traffic commute time, lower values correspond to improved quality of life. Challenges affecting life satisfaction Despite the fact that European countries register high levels of life quality by for example leading the ranking of happiest countries in the world, life satisfaction across the European Union has been on a downward trend since 2018. The EU's overall life satisfaction score dropped from 7.3 out of 10 in 2018 to 7.1 in 2022. This decline can be attributed to various factors, including the COVID-19 pandemic and economic challenges such as high inflation. Rising housing costs, in particular, have emerged as a critical concern, significantly affecting quality of life. This issue has played a central role in shaping voter priorities for the European Parliamentary Elections in 2024 and becoming one of the most pressing challenges for Europeans, profoundly influencing both daily experiences and long-term well-being.

Quality of Life Index (higher is better) is an estimation of overall quality of life by using an empirical formula which takes into account purchasing power index (higher is better), pollution index (lower is better), house price to income ratio (lower is better), cost of living index (lower is better), safety index (higher is better), health care index (higher is better), traffic commute time index (lower is better) and climate index (higher is better).

Current formula (written in Java programming language):

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 10 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

For details how purchasing power (including rent) index, pollution index, property price to income ratios, cost of living index, safety index, climate index, health index and traffic index are calculated please look up their respective pages.

Formulas used in the past

Formula used between June 2017 and Decembar 2017

We decided to decrease weight from costOfLivingIndex in this formula:

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 5 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for 2017 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

Quality of life index, link: https://www.numbeo.com/quality-of-life/indices_explained.jsp

Happiness store, link: https://www.kaggle.com/unsdsn/world-happiness/home

According to the survey, as of February 2023, four out of the six countries in the Gulf Cooperation Council ranked amongst the top ** in the world for expatriate quality of life. Qatar and the United Arab Emirates topped the list for quality of life, whereas Saudi Arabia and Kuwait came last in the region. Quality of life; an amalgamation of many metrics Since quality of life is dependent on many indicators, it can give us a good insight into many aspects of state welfare policies and services. Saudi Arabia, where the number of foreign workers in the private sector topped *** million, also ranked as having one of the region's lowest quality of life for expatriates. Qatar, which had the second-highest quality of life for expatriates living in the GCC, was ranked as one of the most challenging countries in the region for ease of settling in. The UAE and Qatar, both of which ranked the highest in the survey, also have the highest average salaries and living standards in the region. Foreign workers are a key pillar of the GCC economy Countries in the GCC all have sizable expatriate populations for which their economies are heavily reliant. Roughly ********** of the workforce in the GCC is foreign. Although the share of foreign workers in the GCC has slightly decreased in recent years, they still considerably outweigh the local workforce. Most of these workers comprise the unskilled portion of the occupational category in the GCC. However, with diversifying investments and programs such as Vision 2030, countries have seen a rise in the number of skilled foreign workers.

In 2023, Uruguay and Chile had the highest Digital Quality of Life index in Latin America and the Caribbean region, at **** and **** points on a scale from zero to one, respectively. In comparison, Venezuela and Honduras scored the lowest index among the presented countries. The index ranks the quality of digital wellbeing in a country.

Title: Top Cities Worldwide: Quality of Life Index 2024 Subtitle: Ranking the World's Best Cities for Living Based on Key Metrics

Source of Data: The dataset was collected from Numbeo.com, a publicly accessible database that provides data on various quality-of-life indicators across cities worldwide. Numbeo aggregates user-contributed data validated through statistical methods to ensure reliability.

Data Collection Method: Data was acquired through web scraping. Care was taken to follow ethical web scraping practices, adhering to Numbeo’s terms of service and respecting their robots.txt file.

Columns Description:

The dataset includes the following columns:

• Rank: City ranking based on the Quality of Life Index.
• City: Name of the city.
• Country: Country where the city is located.
• Quality of Life Index: Overall index measuring quality of life, calculated based on various sub-indices.
• Purchasing Power Index: Measures relative purchasing power in the city.
• Safety Index: Indicates how safe the city is based on crime rates.
• Health Care Index: Reflects the quality and accessibility of healthcare services.
• Cost of Living Index: Represents the cost of living, including housing, food, and transportation.
• Property Price to Income Ratio: A measure of housing affordability, calculated as the ratio of property prices to average incomes.
• Traffic Commute Time Index: Average time spent commuting within the city.

Limitations and Considerations:

• User-Generated Data: Since data on Numbeo is user-contributed, it may be subject to biases.
• Data Update Frequency: As Numbeo updates its data regularly, the dataset represents a snapshot in time and may require periodic updates.

Usage Note: The dataset is intended for research and analytical purposes. Users should verify the data's applicability for their specific use cases, considering the limitations mentioned above.

The European Quality of Life survey (EQLS) examines both the objective circumstances of European citizens' lives, and how they feel about those circumstances, and their lives in general. It looks at a range of issues, such as employment, income, education, housing, family, health and work-life balance. It also looks at subjective topics, such as people's levels of happiness, how satisfied they are with their lives, and how they perceive the quality of their societies. The survey is carried out every four years.The European Foundation for the Improvement of Living and Working Conditions (Eurofound) commissioned GfK EU3C to carry out the survey. The survey was carried in the 27 European Member States (EU27), and the survey was also implemented in seven non-EU countries. The survey covers residents aged 18 and over. For the purposes of the rankings in this report, London is treated as a 35th European country.The themes covered in the analysis below are: volunteering, community relations, trust in society, public services ratings, well-being, health, wealth and poverty, housing, and skills and employment. The tables following the analysis on page 4 show figures and rankings for: London, rest of the UK, Europe average, the highest ranked country, and the lowest ranked country. Internet use data for all European NUTS1 areas included in spreadsheet. Note figures based on low sample sizes marked in pink.

According to the Digital Quality of Life Index, Singapore had the highest digital quality of life among countries in the Asia-Pacific region in 2023. In comparison, Cambodia scored the lowest among the assessed Asia-Pacific countries in 2023, reaching **** index points.

The Social Progress Index ranks countries based on the well-being and quality of life of their citizens, considering factors such as access to education, healthcare, human rights, and environmental sustainability.

Data is sourced from OECD and IMF.

The Better Life Index 2024 dataset provides comprehensive indicators across various dimensions of well-being for multiple countries. It encompasses factors such as economic prosperity, housing quality, education, health, safety, and overall life satisfaction.

This dataset can be used to compare and contrast the quality of life across different nations, identify patterns, and explore correlations between various socio-economic factors and individuals' subjective well-being.

Target label: Life Satisfaction

Countries where people live for a long time, as a rule, provide their citizens with high-quality medical care and help them lead a healthy lifestyle. On the contrary, in countries with low life expectancy, there are usually economic difficulties, poverty and lack of access to health services.

Quality of Life Indicators: Population with high household expenditure by geographical area. Annual. National.

Estonia and Lithuania had the highest Digital Quality of Life index in Central and Eastern Europe in 2023, at **** and *** points on a scale from zero to one, respectively. In comparison, Bosnia and Herzegovina scored the lowest among the presented CEE countries. The index ranks the quality of digital wellbeing in a country.

Quality of life varies significantly worldwide, influenced by factors like cost of living, healthcare, safety, and infrastructure. This case study analyzes Quality of Life Index rankings from 2022 and 2023 to identify trends, regional shifts, and key factors affecting global well-being.

🔍 What’s inside?

The 10 best and 10 worst ranked countries for both 2022 & 2023 Comparative analysis of ranking changes Visualizations: Bar charts, heatmaps, and Tableau maps for better insights Regional breakdowns to see if certain areas consistently rank high or low Key takeaways on what makes a country livable This project is designed to help analysts, travelers, and policymakers understand global quality-of-life trends.

📊 Tools Used: Excel, Google Sheets, Tableau, Python (optional for deeper analysis)

💡 Key Questions Explored:

Which countries improved or declined the most? Are there patterns in the best/worst-ranked regions? How do economic and social factors correlate with rankings?

As of 2024, South Africa and Morocco scored highest in the Digital Quality of Life index in Africa, with **** points each. Mauritius and Egypt followed closely with scores of **** points and **** points, respectively. African countries ranked significantly lower compared to other regions, with South Africa ranking 66th, while DR Congo came last in the 120th place.

Life Expectancy of the World Population

The dataset from Worldometer provides a ranked list of countries based on life expectancy at birth, which represents the average number of years a newborn is expected to live under current mortality rates. It includes global, regional, and country-specific life expectancy figures, with separate data for males and females. The dataset highlights disparities in longevity across nations, with countries like Hong Kong, Japan, and South Korea having the highest life expectancies. This data serves as a key indicator of public health, quality of life, and healthcare effectiveness, offering valuable insights for policymakers, researchers, and global health organizations.

Data Analysis & Machine Learning Approaches for Life Expectancy Data

Data Analysis Approaches Life expectancy data can be analyzed using descriptive statistics (mean, variance, distribution) and correlation analysis to identify relationships with factors like GDP, healthcare, and education. Time series analysis helps track longevity trends over time, while clustering techniques (e.g., K-Means) group countries with similar patterns. Additionally, geospatial analysis can visualize regional disparities in life expectancy.

Machine Learning Models For prediction, linear and multiple regression models estimate life expectancy based on socioeconomic indicators, while polynomial regression captures non-linear trends. Decision trees and Random Forests classify countries into high- and low-life expectancy groups. Deep learning techniques like neural networks (ANNs) can model complex relationships, while LSTMs are useful for time-series forecasting.

For pattern detection, K-Means clustering groups countries based on life expectancy trends, and DBSCAN identifies anomalies. Principal Component Analysis (PCA) helps in feature selection, improving model efficiency. These methods provide insights into longevity trends, helping policymakers and researchers improve public health strategies.

Life expectancy at birth. Data based on the latest United Nations Population Division estimates.

Source: https://www.worldometers.info/demographics/life-expectancy/#countries-ranked-by-life-expectancy

Table showing the country rankings based in the different metrics analysed

The Quality of Nationality Index reflects the level of well-being, freedom, and opportunities available to citizens of different countries. It takes into account factors such as access to education, healthcare, human rights, and economic opportunities.

Background: Given the limited healthcare resources in low and middle income countries (LMICs), effective rehabilitation strategies that can be realistically adopted in such settings are required.Objective: A systematic review of literature was conducted to identify pragmatic solutions and outcomes capable of enhancing stroke recovery and quality of life of stroke survivors for low- and middle- income countries.Methods: PubMed, HINARI, and Directory of Open Access Journals databases were searched for published Randomized Controlled Trials (RCTs) till November 2018. Only completed trials published in English with non-pharmacological interventions on adult stroke survivors were included in the review while published protocols, pilot studies and feasibility analysis of trials were excluded. Obtained data were synthesized thematically and descriptively analyzed.Results: One thousand nine hundred and ninety six studies were identified while 347 (65.22% high quality) RCTs were found to be eligible for the review. The most commonly assessed variables (and outcome measure utility) were activities of daily living [75.79% of the studies, with Barthel Index (37.02%)], motor function [66.57%; with Fugl Meyer scale (71.88%)], and gait [31.12%; with 6 min walk test (38.67%)]. Majority of the innovatively high technology interventions such as robot therapy (95.24%), virtual reality (94.44%), transcranial direct current stimulation (78.95%), transcranial magnetic stimulation (88.0%) and functional electrical stimulation (85.00%) were conducted in high income countries. Several traditional and low-cost interventions such as constraint-induced movement therapy (CIMT), resistant and aerobic exercises (R&AE), task oriented therapy (TOT), body weight supported treadmill training (BWSTT) were reported to significantly contribute to the recovery of motor function, activity, participation, and improvement of quality of life after stroke.Conclusion: Several pragmatic, in terms of affordability, accessibility and utility, stroke rehabilitation solutions, and outcome measures that can be used in resource-limited settings were found to be effective in facilitating and enhancing post-stroke recovery and quality of life.

Impact of CO2 on Quality of Life around the World

Study of how different countries are affected by CO2 levels

Outline - About The Dataset - Data Extraction and Cleaning - CO2 Emission According to Size and Density - CO2 Emission According to Political Ideology - Impact Of CO2 On The Temperature - Unsupervised Machine Learning On The Quality of CO2 - Conclusion - Areas for Further Research

About The Dataset

The public data set in this repo consists of data on the daily life of people around the world. The objective is to link certain systematic CO2 levels to cause more severe risk and quality of life problems for the population.

Examples: - Temperature- Since CO2 is a greenhouse gas, warmer temperatures can pose as a negative effect. - Humidity- Due to increased CO2 levels, there can be more water cloudy and less sunlight. - Population- A higher population density can result in higher CO2 emissions. - Area- Larger area countries can hold more trees and ecosystem and thus be able to filter more CO2.

Pain Points or Answers the Data Set will Solve

• Can we conclude that the average humidity is higher in countries with higher CO2 emissions?
• Can we conclude that the average temperature is higher in countries with higher CO2 emission?
• Can we conclude that there is a positive relationship between countries with higher CO2 emissions and higher density?
• What are the political factors that lead to higher levels of CO2? (example are deregulated economies better for our ecosystem, do socialist economies have higher CO2?)
• Can we use machine learning to determine how CO2 emission will affect the quality of life in a certain country?
• Can we conclude that a higher population causes higher CO2 emission?

Purpose

• Find out what levels of CO2 will it start to negatively impact the quality of life in different countries.
• Understand the balances needed to have higher economic growth while maintaining an eco-friendly environment.
• Grasp an understanding of the ecosystem of the world.
• Determine global environmentalist strategies to employ.
• Determine regulatory frameworks to prevent over-emission.

Usage

Other than gaining knowledge and expecting value from looking at the trends, this dataset is useful to determine how to deal with future emissions.

Multilateral agreements between countries to reduce emissions by a certain date to help the ecosystem of the world. Governments may implement regulations and mandates on companies to emit less and thus help the ecosystem. Individuals and companies can look at the data to understand what impact they have on the ecosystem.

Hypothesis

The null hypothesis is:

• Countries with a higher population have higher CO2 emission levels
• Countries with a higher density have higher CO2 emission levels
• Socialist countries have higher CO2 emission levels
• Democratic/Capitalistic countries have higher CO2 emission levels ### The alternative hypothesis is:
• Countries with a smaller population have higher CO2 emission levels
• Countries with smaller density have higher CO2 emission levels
• Borderline Dictatorship countries have higher CO2 emission levels
• Monarchist countries have higher CO2 emission levels _

The Data-set consists mainly of two dataframes:

• CO2 Emission by Country
• Indoor Environmental Data

The information from each dataframes will be fed into a database to provide data for the tables and charts.

The tables for the UN database are: - indicators - countries - series

CO2 Emission by Country Information:

Link to Dataset:

-https://data.worldbank.org/indicator/EN.ATM.CO2E.KT

Information On Dataset

The C02 by Country Data Set provides the C02 levels of countries around the world per year. The Data Set comes from The World Banks Atlas Method which uses individual countries data reported to the United Nations Framework Convention on Climate Change (UNFCCC) and other international sources. This Data Set is a great resource to determine individual country emission situation and to see how they affect each other.

Information in the Dataset:

• Country (Title)
• Code
• 60- 68
• Code (Range of C02 levels)

Sorry for the long description.