SOS Alert System Using Machine Learning

Real-time emergency response solutions are necessary because women's safety is still a major worry in today's culture. The SOS Alert System, a comprehensive safety program created to identify, notify, and react in emergency circumstances, is presented in this paper. The system, which was created in Python and has a Tkinter-based graphical user interface, combines contact management, automated SOS notifications, real-time location sharing, and safety zone evaluation into a single platform. By examining variables including crime rate, population density, lighting, historical events, time of day, and present location, a Random Forest Classifier is used to assess site safety and produce a safety score with associated risk categories. The system's multi-channel emergency communication features, which include automated emergency notifications, SMS warnings, and location sharing via WhatsApp, guarantee prompt aid from pre-registered, reliable contacts. Furthermore, the platform offers interactive safety analytics via graphs such as scatter plots, pie charts, bar charts, and histograms, which make it possible to identify high-risk locations and track safety trends. According to experimental data, the SOS Alert System is a low-cost, user-friendly, and scalable solution that works well in urban and semi-urban settings by improving situational awareness and guaranteeing quicker emergency response.