Political Data Projects
Explore a sample of Power BI dashboards that I created with political foci in mind.
Using data from Mapping Police Violence, individual names of Americans who have been murdered in police custody (or the attempt to place them in police custody) since 2013. As of December 2025, this shows that the range of those murdered ranges in ages between 0 (a Black infant who was with their father) to 107 years of age (an elderly Black man who was experiencing a mental health episode).
Mapping Police Violence estimates that Pacific Islander and Black Americans are among the most likely to experience police violence, followed by Native Americans/Alaska Natives and those of any race of Hispanic/Latino cultural origins.
And, while the majority of interactions involved no reported mental illness or drug use, around 20% did. At the same time, police departments have reportedly purposely misreported suspects as having an "unknown race", while mental illness status is not always clear at the time of interaction with a suspect, potentially skewing this data through underreporting.
I collected this data from the Tech for Palestine initiative, which provides ongoing time-series data on official death counts and other statistical measures in Palestine.
Since the ceasefire in early October 2025, the official death toll in Gaza (which only accounts for direct, observed deaths counted by the Palestinian Authority) has plateaued, but not before well over 160,000 direct Palestinian injuries and well over 67,000 direct Palestinian deaths at the hands of the IDF, with both counts continuing to grow.
The first dashboard outlines the cumulative impacts of the Gaza War (the iteration of genocide beginning on October 7, 2023, until the present), while the second dashboard shows the number of Palestinians killed or injured in Gaza, as well as the number of buildings destroyed or damaged in Gaza.
As of December 2025, over 70,000 direct Palestinian deaths have been reported (despite the ceasefire), while it appears that the consumer price indices for goods, overall, had been decreasing in the months before the ceasefire became official.
Academic Data Projects
Explore a sample of Jupyter notebooks that I compiled with an academic focus.
The Zillow dataset provided housing prices based on features such as the year a home was built and its square footage, which were used to predict each home's tax-assessed value. This exercise gave my group the skills to practice machine learning techniques, such as linear regression, random forest regression, and gradient boosting, using features of each home and its tax-assessed value as training data to predict the tax-assessed values of new homes, with specific columns. We found that gradient boosting regression performed slightly better than linear and ridge regression (as evidenced by the model's lowest root mean squared error).
