🛡️ 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]

[ML Prediction]

[Smart Dashboard - Crime Analysis]

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🎯 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

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🛠️ 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

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🧱 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

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📄 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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