Abstract: Crime against women in India has emerged as a critical public health and social issue, demanding rigorous analysis and effective preventive strategies. This study focuses on a comprehensive examination of crimes against women, both quantitatively and qualitatively, using official data released by the National Crime Records Bureau (NCRB) and made available through the Open Government Data (OGD) Platform India. The dataset encompasses various categories of crimes, including molestation, rape, dowry-related violence, exploitation, kidnapping, and cruelty by relatives. Analytical techniques involving data manipulation and visualization were applied to uncover crime trends, patterns, and regional variations. Descriptive analysis was conducted using charts and graphs to provide meaningful insights, while predictive model was employed to forecast future crime rates based on historical data. Among the classifiers tested, Random Forest demonstrated superior performance, achieving an accuracy of 99.67% in classification and 63.32% in prediction. The findings highlight the alarming rise in crimes against women and underscore the importance of leveraging data-driven approaches to inform policy decisions, strengthen law enforcement, and promote societal awareness aimed at reducing gender-based violence.

Keywords: The scope (India, NCRB, OGD), methods (data analysis, visualization, predictive model, Random Forest), crime categories (rape, dowry, molestation, etc.), and impact (public health, policy, gender-based violence).

Downloads: | DOI: 10.17148/IARJSET.2026.13366