Analysis And Prediction Of Crime Against Women Using Machine Learning

Abstract

A critical worldwide issue, the rising number of crimes perpetrated against women calls for fresh approaches to the problem's prevention and solution. The purpose of this research is to examine crime data from the past, find trends and patterns, and identify possible crime hotspots by using M-L algorithms. Using predictive modeling techniques and complex data analytics, this research aims to provide important information that lawmakers and law enforcement may use to protect women and reduce the likelihood that they will be victims.
Gathering and preparing massive datasets including geographical data, socioeconomic variables, criminal records, and demographic information is the meat and potatoes of this project. Publicly accessible crime reports, police records, and government databases are some of the sources of datasets. For accurate and trustworthy analysis, a comprehensive preparation procedure is carried out to handle missing numbers, normalize the data, and eliminate inconsistencies.