Crime Classification Machine Learning Web Application

Overview

This is a full-stack web application for crime classification using machine learning, built with Django (backend) and React (frontend). One can download the model from here https://github.com/bikasaha/CYBER-CRIME-CLASSIFIER and subsequently paste it in ml_folder which is inside the backend directory as mentioned below and run the below mentioned commands to run the project

Project Structure

crime_classification_project/
├── backend/             # Django backend
│   ├── ml_model/        # Machine learning model files
│   ├── classification/  # Django app for ML predictions
│   └── crime_classifier/# Django project settings
│
└── frontend/            # React frontend
    ├── src/             # React component source code
    └── public/          # Static assets

Prerequisites

• Python 3.8+
• Node.js 14+
• pip
• npm

Installation

Backend Setup

1. Navigate to backend directory

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
python -m spacy download en_core_web_sm

2. Configure Django

python manage.py migrate
python manage.py runserver

Frontend Setup

1. Navigate to frontend directory

cd frontend
npm install
npm start

Environment Configuration

• Update SECRET_KEY in backend/crime_classifier/settings.py
• Adjust CORS_ALLOWED_ORIGINS for production deployment

Model Placement

• Place model files in backend/ml_model/BEST/
• Ensure label_mapping_WO_CV.json is in backend/ml_model/

Features

• Text-based crime classification
• Machine learning inference
• Responsive web interface
• Detailed prediction probabilities

Technologies

• Backend: Django, PyTorch
• Frontend: React, Tailwind CSS
• ML Libraries: Transformers, SpaCy, NLTK

Deployment

• Backend: Django server
• Frontend: React development server
• Recommended: Use Gunicorn/uWSGI for production backend

Troubleshooting

• Ensure all dependencies are installed
• Check CORS settings
• Verify model file paths

Contributing

1. Fork the repository
2. Create feature branch
3. Commit changes
4. Push to branch
5. Create pull request

License

Apache License 2.0