Throughout this project, I embarked on an in-depth exploration of the factors influencing global happiness levels using data from the World Happiness Report (WHR) and other sources.
Leveraging my skills in data analysis, I conducted a series of analyses focusing on income inequality, generosity, and social support as potential determinants of World Happiness Score (WHS). Through Python and R programming, I employed statistical modeling techniques, including regression analysis and hypothesis testing, to uncover intricate relationships within the data. The project encompassed three main analyses: examining the top G10 countries' happiness scores, investigating the relationship between WHS and the Gini Index, and exploring the mediating effects of generosity and social support on the association between income inequality and happiness.
Each analysis provided valuable insights into the complex interplay between socioeconomic factors and subjective well-being, highlighting the importance of addressing wealth inequality and fostering supportive social environments for promoting global happiness and well-being.
This project not only showcased my proficiency in data science but also underscored my dedication to understanding and addressing pressing societal issues with empirical evidence and analytical rigor.
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