Date of Degree
Data Analysis & Visualization
Matthew K. Gold
Categorical Data Analysis | Data Science | Digital Humanities | Interactive Arts | Multivariate Analysis | Other Statistics and Probability | Statistical Models
interactive, visualization, website, regression, classification, unsupervised learning
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.)
Ko, Oscar, "Analyzing Relationships with Machine Learning" (2023). CUNY Academic Works.
Archived Website Project
Analyzing_Relationships_with_Machine_Learning-main.zip (7823 kB)
ZIP file of GitHub Repository
Categorical Data Analysis Commons, Data Science Commons, Digital Humanities Commons, Interactive Arts Commons, Multivariate Analysis Commons, Other Statistics and Probability Commons, Statistical Models Commons
Online component: https://oscarkodes.github.io/Analyzing_Relationships_with_Machine_Learning/Website/