The World Happiness Report is released every year, ranking each country by who is “happier” and explaining the variables and data they have used. This project attempts to build from that base and create a machine learning algorithm that can predict if a country will be in a “happy” or “could be happier” category. Findings show that taking a broader scope of variables can better help predict happiness. Policy implications are discussed in using both big data and considering social indicators to make better and lasting policies.
","abstract_html":"<p>The World Happiness Report is released every year, ranking each country by who is “happier” and explaining the variables and data they have used. This project attempts to build from that base and create a machine learning algorithm that can predict if a country will be in a “happy” or “could be happier” category. Findings show that taking a broader scope of variables can better help predict happiness. Policy implications are discussed in using both big data and considering social indicators to make better and lasting policies.</p>","abstract_has_math":false,"creators":["Kahl, Sarah M"],"institution":"The Graduate School and University Center of The City University of New York","degree_name":"Master of Science","degree_level":"Master","degree_discipline":"Data Analysis & Visualization","degree_department":null,"school":null,"contributors":[],"advisors":["Timothy Shortell"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-01T07:00:00Z","date_published":"2022-06-01T07:00:00Z","updated_at":"2026-07-24T01:58:22Z","subjects":["Digital Humanities","Other Computer Engineering","Other Psychology","Other Public Affairs, Public Policy and Public Administration","Other Sociology","Public Policy","Quantitative, Qualitative, Comparative, and Historical Methodologies","Social Psychology","Social Statistics","Work, Economy and Organizations","Data","Machine Learning","Python","Data Visualization","Data Management","Data Analysis"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/gc_etds/4906","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Timothy Shortell"]},{"key":"dc:creator","label":"Author","values":["Kahl, Sarah M"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2022-04-28T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Data Analysis & Visualization"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The Graduate School and University Center of The City University of New York"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Digital Humanities","Other Computer Engineering","Other Psychology","Other Public Affairs, Public Policy and Public Administration","Other Sociology","Public Policy","Quantitative, Qualitative, Comparative, and Historical Methodologies","Social Psychology","Social Statistics","Work, Economy and Organizations","Data","Machine Learning","Python","Data Visualization","Data Management","Data Analysis"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/gc_etds/4906"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The World Happiness Report is released every year, ranking each country by who is “happier” and explaining the variables and data they have used. This project attempts to build from that base and create a machine learning algorithm that can predict if a country will be in a “happy” or “could be happier” category. Findings show that taking a broader scope of variables can better help predict happiness. Policy implications are discussed in using both big data and considering social indicators to make better and lasting policies.
"]},{"key":"dc:title","label":"Title","values":["Happiness and Policy Implications: A Sociological View"]}]}],"canonical_facts":{"dc:contributor.advisor":["Timothy Shortell"],"dc:creator":["Kahl, Sarah M"],"dc:date.available":["2022-04-28T07:00:00Z"],"dc:description.abstract":["The World Happiness Report is released every year, ranking each country by who is “happier” and explaining the variables and data they have used. This project attempts to build from that base and create a machine learning algorithm that can predict if a country will be in a “happy” or “could be happier” category. Findings show that taking a broader scope of variables can better help predict happiness. Policy implications are discussed in using both big data and considering social indicators to make better and lasting policies.
"],"dc:identifier":["https://academicworks.cuny.edu/gc_etds/4906"],"dc:subject":["Digital Humanities","Other Computer Engineering","Other Psychology","Other Public Affairs, Public Policy and Public Administration","Other Sociology","Public Policy","Quantitative, Qualitative, Comparative, and Historical Methodologies","Social Psychology","Social Statistics","Work, Economy and Organizations","Data","Machine Learning","Python","Data Visualization","Data Management","Data Analysis"],"dc:title":["Happiness and Policy Implications: A Sociological View"],"thesis:degree_discipline":["Data Analysis & Visualization"],"thesis:degree_level":["Master"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["The Graduate School and University Center of The City University of New York"]},"updated_at":"2026-07-24T01:58:22Z"}