Project 2

I recently explored the Washington Post’s database on fatal police shootings to identify potential predictors of these incidents. My analysis process provides an example of how to extract insights from data. Initial examination showed variables like victim race and mental health status were right-skewed, while the fleeing variable was left-skewed. This informs appropriate modeling choices. Evaluating correlations revealed race and fleeing had a strong positive correlation with fatal shootings, making them sensible predictor variables. Mental illness had a weaker correlation. Testing a K-nearest neighbors (KNN) model quantified race and fleeing as significant predictors, with fleeing having greater explanatory power. However, limitations suggest room for improvement. While KNN provided initial value in identifying relationships, testing other techniques like random forests could potentially boost predictive performance since the data has pronounced skew. In summary, thoughtful preliminary analysis enabled data-driven identification of promising predictors. But iterative modeling improvements are needed to maximize insights. Proper analytic process allows extracting nuanced conclusions This example demonstrates practices like assessing distributions, identifying correlations, fitting models, and intelligently iterating – all valuable when moving from data to insights.