Data analysis and forecasting applied to World Happiness

World happiness report

Data analysis and forecasting applied to World Happiness The World Happiness Report is a landmark survey of the state of global happiness from 2015 to 2019. The report has gained 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.

Data

https://www.kaggle.com/unsdsn/world-happiness

The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation questions asked in the poll. These questions ask 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 the years 2015-2019.

Happiness score (or subjective well-being): national average response to the question: “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?"

The columns following the happiness score estimate the extent to which each of six factors 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 may help to explain why some countries rank higher than others:

GDP per capita: measure of a country's economic output that accounts for its number of people.
Health life expectancy: average number of years that a newborn can expect to live in "full health" — in other words, not hampered by disabling illnesses or injuries.
Social support (or having someone to count on in times of trouble): national average of the binary responses (either 0 or 1) to the question “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”
Freedom to make life choices is the national average of responses to the question “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”
Generosity is the residual of regressing national average of response to the question “Have you donated money to a charity in the past month?” on GDP per capita.
Corruption Perception: The measure is the national average of the survey responses to two questions: “Is corruption widespread throughout the government or not” and “Is corruption widespread within businesses or not?”

Although we know what these features are about, we don't know the unit of measurement used in the data.

Purposes of the project

Data analysis:

Give a clear picture of happiness around the world in 2019
Analyse trends in happiness from 2015 to 2019

Forecasting with Machine Learning(*)

Can we predict a country happiness if we know the gdp per capita, life expectancy and other factors values?
Can we predict a country happiness thanks to its history (happiness+factors)?

To answer these questions, we'll compare different regression models.

(*) Although data don't contain related information, the global pandemic may have a tremendous impact on the results

Workflow

In order to answer these questions in a structured and comprehensible way, we will go through following workflow

Cleaning: detecting and correcting (or removing) corrupt or inaccurate records from the datato ensure that further analysis will be based on meaningful and relevant information.
EDA is about knowing your data, gaining a certain amount of familiarity with the data and extracting first insights from it.
Data Visualization is the ability to tell a compelling story with data and can have different purposes (declarative or exploratory)
Features Engineering is the process of transforming the data into a better representation that maximize the efficiency of machine learning models
Machine Learning: expertise in ML algorithms and their evaluation.

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