Machine Learning-Based Analytical Process for Predicting the Occurrence of Gender-Based Violence

Gender-Based Violence (GBV) is a pervasive issue that poses a significant threat to individuals and communities worldwide. Many countries, faces the pressing challenge of combating GBV as an impediment towards ensuring the safety and well-being of its citizens. Despite the coordinated efforts taken by several governments and its people, there remains a cause for concern regarding curbing GBV globally. Women, children, and other vulnerable members of society continue to be exposed to this significant threat. Current measures are primarily reactive, with focus on punishing perpetrators rather than foreseeing and preventing future occurrences. This study provides a machine leaning-based analytical process for predicting the occurrence of GBV to aid in early prevention strategies. A preliminary case study of the analytical process was recommended using Namibia as one of the South African countries. External dataset of GBV were extracted from kaggle platform containing some Namibia records for preliminary study and model testing. It was recommended in this study that Namibia should employ various machine learning (ML) algorithms as a test case. These algorithms should be compared and evaluated for their predictive accuracy in forecasting GBV events in Namibia. Additionally, the case study should conduct exploratory data analysis to identify key trends and drivers of GBV in Namibia, providing valuable insights for policymakers and intervention programs. By leveraging advanced ML techniques, the case study would contribute to evidence-based decision-making, policymaking, planning, and resource allocation aimed at reducing GBV incidents in Namibia. If the analytical process is duly followed and applied in Namibia, the research outcome would have the potentials to inform policymakers, law enforcement agencies, and social organizations about the underlying causes and risk factors associated with GBV, enabling them to implement effective intervention strategies.