An AI-driven personal safety platform designed for tourists and night-shift workers, combining real-time risk analysis, location tracking, and emergency response tools in a single web application.
SafePassage was built during an AI/ML internship, addressing a real safety gap: tourists and night workers often lack accessible, data-driven tools to assess area risk and get help quickly in an emergency. The platform offers two tailored operating modes β Tourist and Night Worker β each with dashboards, alerts, and workflows suited to that user's specific safety needs.
I conceived the product, defined the system architecture and AI/ML approach (crime-data-driven risk scoring, dual operating modes, RBAC-based access control), and directed the implementation, reviewing progress and functionality throughout development.
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AI-Driven Risk Analysis Engine β combines rule-based scoring with a trained Random Forest model on NCRB crime data to assess area risk in real time
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Dual Operating Modes β separate, purpose-built experiences for tourists (safe routes, scam alerts, cultural guides) and night workers (shift check-ins, safe havens, route planning)
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Emergency SOS System β one-tap SOS alerts with automated email notifications to emergency contacts
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Role-Based Access Control (RBAC) β secure, tiered access across user types and an admin control panel
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Admin Dashboard β analytics, incident monitoring, risk zone management, and system logs
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Location-Based Safety Tools β safe route suggestions, risk zone mapping, and safe haven discovery
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Backend: Python, Django
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Machine Learning: scikit-learn (Random Forest), pandas, NCRB crime datasets
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Database: SQLite
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Frontend: Django Templates, HTML/CSS, JavaScript
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Other: REST-style internal APIs, SMTP email integration for alerts
safepassage/
βββ ml_pipeline.py # ML training pipeline (risk model)
βββ ml-models # Trained model artifacts, plots, metrics
βββ dataset # NCRB crime datasets used for training
βββ requirements.txt
βββ safepassage_backend # Django project
βββ safety # Core app: models, views, risk engine, ML integration
βββ templates # Tourist, worker, and admin UI templates
βββ static # CSS, images
git clone https://github.com/Sr-2525/SafePassage.git cd SafePassage/safepassage_backend pip install -r ../requirements.txt cp .env.example .env # then fill in your own email credentials python manage.py migrate python manage.py runserver
This project was built as part of a company-run internship program, where implementation support was provided by a developer assigned through the program. The core concept, system design, risk-analysis approach, and technical direction throughout development were mine.