I built a binary classifying algorithm that predicted whether clients would take a housing voucher. The assignment combined visualization, feature engineering, model specification, and a cost benefit analysis.

This analysis considers unemployment rates by county before and after the pandemic and their relationship with race. My findings concluded that Asian communities saw the greatest increase in unemployment, while Native Americans the least. Systemic unemployment is still a major problem faced by indigenous populations.

This hackathon assignment for GAFL 531 at Penn examines state health policy, CDC vaccinations, and election results from 2020. I found that states who voted for Joe Biden are more likely to require masks, impose restrictions on restaurants, and limit large gatherings. Additionally, they have higher vaccination rates than states who voted for Donald Trump.

This assignment examines 311 incidents in Philadelphia, for my Data Science in Public Policy class at Penn. I find that abandoned vehicles and broken street lights cluster in poorer neighborhoods, while trees and graffiti have little relation to neighborhood income.

A quick examination of the wealthiest rent-adjusted tracts in Philadelphia over time

For the final project in my Urban Spatial design class, my partner and I built a ML model to predict NJ transit delays by station. We included features such as weather, time, and spatial attributes.

This first assignment in my geospatial machine learning class asked us to estimate the impact that transit has on a variety of neighborhood characteristics. My team chose Queens County in NY and examined rent prices, poverty, population and college education. We found that transit had very limit impact on these neighborhood features.

My midterm assignment asked us to build a home pricing prediction model that could compete with zillow's zestimate. My team used a variety of home and spatial characteristics to build a model that predicted within $75k of a home's final sale price.

In this assignment for my geo-spatial machine learning class, I examined narcotics arrests from 2018 in Chicago and developed a prediction model for 2019.