Volume 9, Number 2

Gun Violence & Firearm Regulations: An Investigation of Correlations Between Weapon-Related Incidents & Current Legislative Policies Through Natural Language Processing and Machine-Learning

  Authors

Ronald Feng, USA

  Abstract

In recent years, gun violence in the United States has emerged as a significant public health concern with profound societal impact. Nevertheless, firearms regulations remain highly controversial and divisive. In most cases, federal laws are limited in scope and each state therefore implements its own gun policies and regulations. This study explores current data regarding gun violence and state level firearms laws to examine their relationship. To accomplish this task, sophisticated statistical and machine learning models were used to determine the most effective firearms laws to deter gun violence. This research further underscores the fact that firearms regulations are not widely supported in many regions of the country. The NLP (Natural Language Process) analysis on the gun violence data revealed public and crowded spaces are frequent targets for gun crimes. This study further found out certain firearms laws are more associated with gun incidents. Finally, this investigative study applied machine-learning modeling to identify the 6 most important firearms laws associated with gun violence. Our research paves the way for policymakers to evaluate the effectiveness of current firearm laws and provide data-driven solutions to resolve the current crisis.

  Keywords

Gun Control, Mass Shootings, School Shootings, Correlation, Firearm Laws, Legislation,