As part of the Final Project for A1C1 Univ.ai's Course, the team - Vishnu, Sakthisree, Niegil and Rishabh - started their journey to leverage Machine Learning for trying to answer the age-old question of - what exactly makes us happy?
The World Happiness Report was recommended to be a good starting point for guaging world wise bliss. Throughout our analysis, the data points surely helped us although towards the end we were able to understand that perhaps all of the variables in this report alone will not be sufficient for us to accurately measure the happiness of a country since "happiness" is very relative in nature.
There are six measurements taken per country for guaging the World Happiness Index. They consist of:
GDP per Capita - Gross Domestic Product per capita for the countries
Family - Satisfaction Rank of Family
Life Expectancy - Avg. expected years to live
Freedom - Perception of freedom quantified
Generosity - Numerical value estimated based on the perception of Generosity experienced by poll takers in their country.
Trust/Government Corruption - A quantification of the people's perceived trust in their governments.
Dystopia Score - Score based on comparison to hypothetically the saddest country in the world.
Dystopia Residual - Rank of any country in a particular year.
The Happiness Score calculated in the report is actually an average of the responses to the main life evaluation question asked in the Gallup World Poll (GWP), which uses the Cantril Ladder.
Cantril Ladder involved something called as Cantril step where they ask reponsents to think of a step with the most excellent life they can think of and with that as benchmark, score their current life.
Credits Remarks to:
- Univ.Ai Professor Pavlos Protopapas
- Kaggle Datasets
- Aashita Kesarwani - https://www.kaggle.com/aashita/guide-to-animated-bubble-charts-using-plotly - for demonstrating beautiful ways to plot bubble charts
- Jesper Sören Dramsch - https://www.kaggle.com/jesperdramsch/the-reason-we-re-happy - for demonstrating wonderful means of doing data analysis
- Jamaç Eren Ay - https://www.kaggle.com/yamaerenay/world-happiness-report-preprocessed - for preparing pre processed datasets and allowing it for free use for all
Given the data available per country to guage the Hapiness Index, our aim is to:
- Part A - Analyze and understand which factors affect the Happiness Index Score of countries
- Part B - Analyze and understand the relationship between Terror Attacks and Happiness Index
- Part C - Create a Model to predict the Happiness Index of a Country
- Part D - To see how much Health contributes to the Happiness Index? With the current pandemic at hand, predicting COVID-19 Cases in the coming days for countries.
- Part E - Creating a Dashbord for viewing COVID-19 Predictions
The Spearman's Rank Correlation Coefficient is used to discover the strength of a link between two sets of data.
The Spearman rank correlation coefficient, ρ considers the ranks of the values for the two variables.ρ will always be a value between -1 and 1.
The further away ρ is from zero, the stronger the relationship between the two variables. The sign of ρ corresponds to the direction of the relationship. If it is positive, then as one variable increases, the other tends to increase. If it is negative, then as one variable increases, the other tends to decrease.
You use Spearman’s correlation if your data have a non-linear relationship (like an exponential relationship) or you have one or more outliers. However, Spearman’s correlation is only appropriate if the relationship between your variables is monotonic.
Inference: From the above matrixes, it seems like Health, GDP Per Capita and freedom are the top 3 factors that correlate with happiness index.