Analysis and Prediction of Crimes Against Women

The proposed work addresses the escalating concern of crimes against women in contemporary society. This comprehensive endeavor employs a data-driven approach to tackle this critical issue. The primary objectives encompass crime rate analysis, crime rate prediction, and the implementation of an emergency alert system. The crime rate analysis module employs the K-Means clustering algorithm to identify crime-prone areas and high-probability crime occurrences. In the crime rate prediction component, machine learning algorithms, including DBSCAN, Agglomerative Clustering, and Mean Shift, are used to forecast emerging crime trends. The emergency alert system offers women real-time alerts and emergency assistance, fostering a safer environment. The Silhouette Score, an evaluation metric, helps assess the efficiency of these algorithms in capturing inherent data patterns. Proposed work has accessed the performance metrices of K-Means