Happiness and Policy Implications: A Sociological View

<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>

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Master

Discipline thesis:degree_discipline

Data Analysis & Visualization

Grantor

The Graduate School and University Center of The City University of New York

Year dc:date.available

2022

Author dc:creator

• Kahl, Sarah M

Advisor dc:contributor.advisor

• Timothy Shortell

• 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

Repository record dc:identifier

https://academicworks.cuny.edu/gc_etds/4906

OAI identifier oai:identifier

oai:academicworks.cuny.edu:gc_etds-5967