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SafePassage πŸ›‘οΈ

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.

Overview

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.

Key Features

  • 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

  • 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)

  • Emergency SOS System β€” one-tap SOS alerts with automated email notifications to emergency contacts

  • Role-Based Access Control (RBAC) β€” secure, tiered access across user types and an admin control panel

  • Admin Dashboard β€” analytics, incident monitoring, risk zone management, and system logs

  • Location-Based Safety Tools β€” safe route suggestions, risk zone mapping, and safe haven discovery

Tech Stack

  • Backend: Python, Django

  • Machine Learning: scikit-learn (Random Forest), pandas, NCRB crime datasets

  • Database: SQLite

  • Frontend: Django Templates, HTML/CSS, JavaScript

  • Other: REST-style internal APIs, SMTP email integration for alerts

Project Structure

safepassage/

β”œβ”€β”€ ml_pipeline.py # ML training pipeline (risk model)

β”œβ”€β”€ ml-models # Trained model artifacts, plots, metrics

β”œβ”€β”€ dataset # NCRB crime datasets used for training

β”œβ”€β”€ requirements.txt

└── safepassage_backend # Django project

β”œβ”€β”€ safety # Core app: models, views, risk engine, ML integration

β”œβ”€β”€ templates # Tourist, worker, and admin UI templates

└── static # CSS, images

Setup

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

Notes

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.

About

An AI-driven personal safety and risk-analysis platform built with Django, featuring dual operating modes for tourists and night workers, Random Forest crime data scoring, and an automated SOS emergency response system.

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