Crime Pattern Analysis and Prediction Using Regression Models

This research intends to address the pressing concern of women's safety by developing predictive models for identifying regional women's crimes, utilizing a range of regression models including Support Vector Regression (SVR), Logistic Regression, Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Linear Regression (LR), and Polynomial Regression. With a comprehensive dataset encompassing historical crime statistics, demographic attributes, and socioeconomic factors, the study aims to construct accurate predictive models by considering crucial elements such as population density, education levels, employment rates, urbanization metrics, and police coverage. Through rigorous cross-validation and comparison using metrics like mean squared error, R-squared, and accuracy, the research seeks to identify the most effective model for predicting crimes against women in different Indian regions. The outcomes of this study have far-reaching implications, offering data-driven insights for authorities, policymakers, and advocacy groups to enhance women's safety by strategically allocating resources, optimizing methods, and implementing proactive measures, thus contributing to an overall safer environment and improved well-being for women throughout the nation.