Developing a Machine Learning Based Support System for Mitigating the Suppression Against Women and Children

Violence against women and children has emerged as a significant and growing concern worldwide. To avoid violence, various machine learning (ML) approaches could be used to estimate future violence. The main motive of this study is to provide a central platform for victims, store victim data in a database. In this research, we propose a system that is a web-based tool that stores data on violence against women and children in a database and generates crime forecast results by evaluating the collected data using machine learning techniques. The victim can also get proper information about their rights from this web application. A statistical analysis was carried out on certain datasets and few machine learning model were implemented and the best performed model was decided based on some performance measurement metrics where XG Boost (XB) performed well among others (R-squared test 0.99). Ultimately the XB model has been utilized to generate the forecasting crime report, thereby reducing the level of crime. Government and other law enforcement agencies can predict the future consequences of violence from the system and help victims to get proper justice and settlement. This web application is a support system that may greatly assist women in many parts of their daily lives while also resuming violence against women and children.