Sexual harassment in public places is overwhelmingly experienced by women and girls. Sexual harassment is, in fact, the most common form of violence against women and girls and that young women are particularly targeted. Sexual harassment has significant and widespread impacts, both on individuals as well as on society. Sexual harassment in public reduces women and girls’ freedom to enjoy public life, and can negatively affect feelings of safety, bodily autonomy and mental health.This project proposes a data-driven method to analyze crime data and behavioral patterns using machine learning algorithms and thus predict emerging crime hotspots for additional police attention.Each community has different crime trends in different areas. These trends are analyzed using machine learning principles which help to predict how crimes against women have significantly increased in various areas of a community. It also helps in rapid visualization and identification of communities which are densely affected with crimes. This approach proves to be quite effective and can also be used for analyzing national crime scenario.

K-means Clustering, Random Forest, Google maps GPS, stemming

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