A Hybrid Machine Learning and Regression Approach for Validating a Multi-Dimensional Crime Index in the Context of Crime Against Women

The increasing rate of crime against women has become a critical issue globally, necessitating an accurate and comprehensive framework for measuring and understanding its multifaceted nature.This paper presents a hybrid machine learning and regression approach to validate a multi-dimensional crime index designed to assess the prevalence, severity, and impact of crimes against women.The proposed approach integrates multiple data sources, including crime reports, social media discussions, and judicial records, to construct a dynamic crime index that considers various socio-economic, cultural, and legal factors.A machine learning model is used to classify and predict crime incidents based on geographical, temporal, and demographic variables.The regression model then evaluates the relationship between these factors and the crime index, identifying the key drivers of violence against women.By applying supervised learning algorithms such as Random Forest, Gradient Boosting, and Support Vector Machines (SVM) in conjunction with regression techniques, the system aims to offer an actionable crime index that can be used by policymakers, law enforcement, and advocacy groups to prioritize resources and design interventions.The hybrid approach is validated against real-world crime datasets and tested for accuracy, interpretability, and robustness.Key findings demonstrate that this model can identify high-risk areas and trends, offering valuable insights into the underlying causes and dynamics of crimes against women.The proposed system not only facilitates better-targeted interventions but also serves as a tool for monitoring and evaluating the effectiveness of crime prevention policies.This study contributes to the ongoing efforts to combat violence against women by providing an innovative, data-driven approach that enables informed decisionmaking and fosters a safer environment for women.