Analyzing Relationships with Machine Learning

Author

Oscar Ko, The Graduate Center, City University of New YorkFollow

Date of Degree

2-2023

Document Type

Capstone Project

Degree Name

M.S.

Program

Data Analysis & Visualization

Advisor

Ellie Frymire

Committee Members

Matthew K. Gold

Subject Categories

Categorical Data Analysis | Data Science | Digital Humanities | Interactive Arts | Multivariate Analysis | Other Statistics and Probability | Statistical Models

Keywords

interactive, visualization, website, regression, classification, unsupervised learning

Abstract

Procedurally, this project aims to take a dataset, analyze it, and offer insights to the audience in an easy-to-digest format. Conceptually, this project will seek to explore questions like: “Do couples that meet through online dating or dating apps have higher or lower quality relationships?”, “Can any features in this dataset help predict how a subject would rate their relationship quality?”, and “What other insights can I derive from using machine learning for exploratory analysis?” The intended audience for this project is anyone interested in romantic relationships or machine learning.

The dataset is from a Stanford University survey, “How Couples Meet and Stay Together 2017,” which asked subjects about their relationships and how they met. This dataset contains many features including relationship status, usage of online dating or dating apps, age, education level, political party, income, living situation, and quality of relationship. (The quality of a relationship is self-rated by the subject.)

Comments

Online component: https://oscarkodes.github.io/Analyzing_Relationships_with_Machine_Learning/Website/

Recommended Citation

Ko, Oscar, "Analyzing Relationships with Machine Learning" (2023). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/5225