International Journal of Scientific Research and Engineering Development

The system is developed using Python and integrates a Streamlit-based user interface for real-time interaction. The dataset consists of multiple attributes such as location, crime type, frequency, and time slots, which are preprocessed through data cleaning, normalization, and feature selection techniques. A classification model is trained on this data to predict safety levels with an observed accuracy of approximately 85–92% depending on dataset variations. Performance evaluation shows that the average prediction time per query is approximately 0.5 to 1.2 seconds, ensuring near real-time response for users. The system also incorporates user interaction logging, enabling analysis of user behavior patterns such as frequent location queries and peak usage time slots.Experimental results indicate that the proposed system effectively identifies high-risk zones and provides quick and reliable predictions. The combination of efficient execution time, satisfactory accuracy, and userfriendly design makes SafeTour a practical solution for enhancing travel safety. The system can be further extended with real-time data integration and adaptive learning models for improved performance.

Ayesha Naveed Shaikh, Pavan Hargovindsing Chouhan, Labika Sameer Mulani,"SAFETOUR-TECH: Smart Tourist Safety Monitoring & Incident Response System" International Journal of Scientific Research and Engineering Development, V9(2): Page(1554-1556) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.