  We believe that "all men are created equal". With the rise of the police
shootings reported by media, more people in the U.S. think that police use
excessive force during law enforcement, especially to a specific group of
people. We want to apply multidimensional statistical analysis to reveal more
facts than the monotone mainstream media. Our paper has three parts. First, we
proposed a new method to quantify fatal police shooting news reporting
deviation of mainstream media, which includes CNN, FOX, ABC, and NBC. Second,
we analyzed the most comprehensive US fatal police shooting dataset from
Washington Post. We used FP-growth to reveal the frequent patterns and DBSCAN
clustering to find fatal shooting hotspots. We brought multi-attributes (social
economics, demographics, political tendency, education, gun ownership rate,
police training hours, etc.) to reveal connections under the iceberg. We found
that the police shooting rate of a state depends on many variables. The top
four most relevant attributes were state joined year, state land area, gun
ownership rate, and violent crime rate. Third, we proposed four regression
models to predict police shooting rates at the state level. The best model
Kstar could predict the fatal police shooting rate with about 88.53%
correlation coefficient. We also proposed classification models, including
Gradient Boosting Machine, Multi-class Classifier, Logistic Regression, and
Naive Bayes Classifier, to predict the race of fatal police shooting victims.
Our classification models show no significant evidence to conclude that racial
discrimination happened during fatal police shootings recorded by the WP
dataset.
