• Used 3 datasets, GDP per Capita and Suicide rates for 2000, 2005, 2010, 2015 & 2016, World Happiness Report 2015, and World Happiness Report 2016.
• Performed ETL to prepare the data to analysis, by removing columns, splitting datasets, merging the datasets using Python & Pandas library on Jupyter notebook
• Applied descriptive analysis on suicide rates for 2015 & 2016 to have a bigger idea about the data
using boxplot & exploring the top 5 countries for each year.
• Created a correlation matrix using heat map using seaborn & Pandas libraries.
• Created a function that will return a scatter plot with a regression line using seaborn library, the Pearson Correlation Coefficient, the p-value using stats from scipy, & returns if there is a correlation, if the correlation is negative or positive, if it is weak, moderate, strong, or very strong, and if it is significant or not
• Visualized residual plots to confirm the possible correlations are linear relationship