Understanding world happiness using machine learning techniques

Paper

Ibnat, F., Gyalmo, Jigmey, Alom, Zulfikar, Awal, Md Abdul and Azim, Mohammad Abdul. 2022. "Understanding world happiness using machine learning techniques." 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2). Rajshahi, Bangladesh 26 - 27 Dec 2021 Bangladesh. https://doi.org/10.1109/IC4ME253898.2021.9768407
Paper/Presentation Title

Understanding world happiness using machine learning techniques

Presentation TypePaper
AuthorsIbnat, F., Gyalmo, Jigmey, Alom, Zulfikar, Awal, Md Abdul and Azim, Mohammad Abdul
Number of Pages4
Year2022
Place of PublicationBangladesh
ISBN9781665406376
9781665406383
Digital Object Identifier (DOI)https://doi.org/10.1109/IC4ME253898.2021.9768407
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9768407
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9768399/proceeding
Conference/Event2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)
Event Details
2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)
Delivery
In person
Event Date
26 to end of 27 Dec 2021
Event Location
Rajshahi, Bangladesh
Abstract

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.

KeywordsMachine learning; Artificial intelligence; World Happiness
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
Public Notes

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Byline AffiliationsAsian University for Women, Bangladesh
Khulna University, Bangladesh
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