Predictive Modeling of Gun Violence Using Machine Learning: Understanding the Role of Demographic and Socioeconomic Factors at the County Level

In a society plagued by the implications of gun violence, the need for effective risk assessment measures has become evident.This project addresses the challenge of predicting gun violence risks at the county level in the United States by harnessing the power of machine learning-based predictive modeling.We hypothesize that demographic and socioeconomic factors, such as unemployment and poverty rates in 2021, influence the likelihood of gun violence in various regions.Through an intricate analysis of multifaceted datasets encompassing crime rates, socioeconomic indicators, and geographic characteristics, this research seeks to uncover the extent to which these factors impact the risk of gun violence at the county level.Analyzing 2021 data, the research identifies counties with specific poverty-related characteristics that significantly elevate the risk of shootings.The study's use of decision trees provides a glimpse into the predictive model's classification process.By decoding the complex dynamics of gun violence, this research opens new avenues for understanding and addressing the factors contributing to shootings, ultimately guiding the way toward a safer society.