Date of Degree


Document Type

Capstone Project

Degree Name



Data Analysis & Visualization


Timothy Shortell

Subject Categories

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


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.

Kahl Thesis.ipynb (431 kB)
Jupyter notebook for Python code

Coefficients.xlsx (17 kB)
Coefficients workbook

Gender_Inequality_Index.xlsx (33 kB)
Gender Inequality Index workbook

GDP_Clean.xlsx (15 kB)
GDP workbook

SJ_Index_Clean.xlsx (91 kB)
SJ Index workbook

Environmental_Index_Clean.xlsx (23 kB)
Environmental Index workbook

Religion_Clean.xlsx (18 kB)
Religion workbook

World_Happiness_Report_Clean.xlsx (28 kB)
World Happiness Report workbook

Unemployment_in_mil.xlsx (14 kB)
Unemployment workbook