Performed exploratory data analysis (EDA) in python on the world happiness report datasets (for years 2015, 2016, 2017, 2018, and 2019) from Kaggle; to analyze how measurements of well-being (GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption) can effectively help assess the progress of nations across the world.
Python | (Libraries: pandas, NumPy, Scikit-learn, Matplotlib, Seaborn) | Plotly python graphing library
• Performed univariate, bivariate, and multivariate analysis.
Image 1: Choropleth map depicting the 2019 world happiness rank of all the countries across the globe
Image 2:
• Used the Pearson correlation coefficient to measure the strength of a linear association between the variables in the dataset.
• Utilized simple linear regression to find and accurately model relations between the variables.
Image 3: Analyzed altering ranks of the top 20 countries from 2015 to 2019
Image 4: Analysis of government corruption perceptions region-wise across the world
Image 5: Analysis of India's GDP per capita with other countries from 2015 to 2019
Image 6: Performed hierarchical cluster analysis (agglomerative clustering) and k-means clustering
