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Crime Navigator is a powerful full-stack web application that leverages machine learning and interactive visualizations to predict and analyze urban crime trends. Designed for public safety, the platform processes real-time and historical crime data from NCRB.

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🛡️ Crime Navigator – AI-Powered Crime Analysis and Safe City Prediction System

Crime Navigator is a powerful full-stack web application that leverages machine learning and interactive visualizations to predict and analyze urban crime trends. Designed for public safety, the platform processes real-time and historical crime data from NCRB and empowers users, law enforcement, and policymakers to make data-driven decisions for a safer tomorrow.

[App Dashboard]

image

[ML Prediction]

WhatsApp Image 2025-07-12 at 19 52 36_cdc57cb7

[Smart Dashboard - Crime Analysis]

image

🎯 Key Features

🔐 1️⃣ Secure User Authentication

  • Role-based access with JWT
  • AES-encrypted analytics and PDF exports
  • HTTP-only cookies for session security

📊 2️⃣ Crime Data Visualization

  • Interactive charts using Chart.js and D3.js
  • Bar, Line, Radar, and Heatmaps for exploring trends
  • City-wise, year-wise, and crime-type filters

🔮 3️⃣ ML-Based Crime Prediction

  • Trained on 5.8M+ NCRB records from 19 Indian cities
  • Random Forest Regressor (MAE: 2.49 | R²: 0.93)
  • Predicts crime rate, case count, and severity label

📰 4️⃣ Real-Time Crime News Integration

  • Live crime-related headlines via NewsAPI
  • Region/category-based filtering
  • Clickable news cards with source and timestamps

🧠 5️⃣ Smart Dashboard Experience

  • Responsive frontend using React.js
  • Admin, Analyst, and Public User modes
  • Historical analysis + predictive overlays

🛠️ Tech Stack

🔹 Backend

  • Node.js + Express.js (REST APIs)
  • Flask (ML predictions with scikit-learn)
  • MongoDB (crime records & user data)

🔹 Frontend

  • React.js (v18)
  • Chart.js + D3.js
  • Bootstrap, Axios

🔹 ML/AI

  • Scikit-learn
  • Pandas, NumPy

🧱 System Architecture

/CrimeNavigator
├── /client
│   ├── /components
│   └── /pages
├── /server
│   ├── /routes
│   ├── /middleware
│   └── /controllers
├── /ml_model
│   ├── model.pkl
│   ├── app.py (Flask)
│   └── predict_pipeline.py
├── /public
│   ├── /assets
├── /data
│   └── NCRB_Crime_Records.csv
├── package.json
├── requirements.txt
└── README.md

📄 Installation and Setup

###  1. Clone the Repository
git clone https://github.com/avadheshgithub/Crime_Navigator-App.git
cd Crime_Navigator-App
### 2. Install Frontend Dependencies
cd client
npm install
### 3. Install Backend Dependencies
cd ../server
npm install
### 4. Setup Python Environment for ML
cd ../ml_model
pip install -r requirements.txt
### 5. Run the Application

# Start Flask (ML Backend)
python app.py

# In a new terminal, start Node.js Server
cd ../server
npm start

# In another terminal, start React Frontend
cd ../client
npm start

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Crime Navigator is a powerful full-stack web application that leverages machine learning and interactive visualizations to predict and analyze urban crime trends. Designed for public safety, the platform processes real-time and historical crime data from NCRB.

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