This week, my partner and I met, reviewed the code, and divided up the writing of the report.
This week, my partner and I met, reviewed the code, and divided up the writing of the report.
This week, I applied Principal Component Analysis (PCA) and K-means clustering to explore patterns of protests, riots, and violence across U.S. states. PCA reduced the…
This week, I explored the relationship between three types of political violence events normalized per 1 million people: Protests_per_1M Riots_per_1M Violence_per_1M Using .corr() in Python,…
Building on last week’s work—where I grouped states by their counts of political protests, riots, and violence against civilians—I’ve now taken the next important step:…
After cleaning the United States conflict data from ACLED data, my question is: What groups of states share similar patterns of political protests and violence?…
This week, I worked with the Armed Conflict Location and Event Data (ACLED) dataset, which provides information on political and other violence in the United…
This week, I calculated Cramér’s V to measure the strength of the association between gender and race and found out that the Cramér’s V result…
Last week, our analysis of racial and gender residuals in fatal police shootings revealed White women had the highest rate (2.75), with observed counts exceeding…
This week, me and Tai decide to analyst the number of shootings related to gender and race. In the data, there are some people have…
This week, I counted shootings in each region. Since the dataset contains a state column but no region, I used R to classify the states…