Understanding World Happiness using Machine Learning Techniques

Pursuing happiness is a fundamental and ultimate goal of every individual as well as every nation. Nevertheless, the mapping of happiness is complicated and arduous. The United Nations (UN), on its recognition, in 2012 resolution has addressed the importance of world happiness measures. From then on, the ‘World Happiness Report’ started ranking countries based on their national happiness status actively throughout the past years. Such reports, then, help to acknowledge the importance of gross happiness besides several other gross economic indicators of countries to understand their well-being. This research works with the World Happiness Report 2019 and aims to use machine learning, artificial intelligence, computational strategy. In particular, different machine learning tools such as Google Colab and weka is used in this paper to model the processed historical happiness index report. Using the data of 156 countries from the UN Development Project 2019, this work can identify which factors need to be improved by a particular country to increase the happiness of its citizens. The paper presents supervised machine-learning-based analytical models that can predict the life satisfaction score of any specific country based on the defined parameters, emphasizing the happiest countries and regions based on 2019 happiness report findings.