E-commerce Analytics Dashboard

Real-time analytics platform for e-commerce businesses with AI-powered sales predictions and inventory management.

✨ Features

• 📊 Real-time Analytics Dashboard - Monitor sales, revenue, and inventory with sub-second latency
• 🤖 AI-Powered Predictions - Machine learning models for sales forecasting and inventory optimization
• 📦 Inventory Management - Real-time stock tracking with automated low-stock alerts
• 🎨 Customizable Dashboards - Drag-and-drop widgets to build personalized analytics views
• 🔒 GDPR/CCPA Compliant - Built-in privacy controls for data export, deletion, and consent management
• ⚡ WebSocket Real-time Updates - Live data streaming for instant dashboard updates

🎯 Key Outcomes

� Screenshots

Landing Page

Premium landing page with animated gradients, feature highlights, and technology stack showcase

Analytics Dashboard

Real-time dashboard with metrics cards, sales charts, recent orders, low stock alerts, and AI predictions

�🛠️ Technology Stack

📁 Project Structure

ecommerce-analytics/
├── src/                        # Next.js Frontend
│   ├── app/                    # App Router pages
│   │   ├── dashboard/          # Dashboard pages
│   │   └── ...
│   └── components/             # React components
│       └── dashboard/          # Dashboard-specific components
├── backend/                    # NestJS Backend
│   └── src/
│       ├── modules/
│       │   ├── analytics/      # Real-time analytics
│       │   ├── auth/           # JWT authentication
│       │   ├── cache/          # Redis caching
│       │   ├── inventory/      # Stock management
│       │   ├── predictions/    # AI forecasting
│       │   ├── privacy/        # GDPR compliance
│       │   └── sales/          # Sales data
│       └── common/             # Shared utilities
├── ml-service/                 # Python ML Service
├── k8s/                        # Kubernetes manifests
├── docker-compose.yml          # Local development
└── docs/                       # Documentation

🚀 Quick Start

Prerequisites

• Node.js 18+
• Docker & Docker Compose
• PostgreSQL 16 (or use Docker)
• Redis 7 (or use Docker)

1. Clone and Install

# Clone the repository
git clone https://github.com/yourusername/ecommerce-analytics.git
cd ecommerce-analytics

# Install frontend dependencies
npm install

# Install backend dependencies
cd backend && npm install && cd ..

2. Environment Setup

# Copy environment files
cp backend/env.example backend/.env

# Edit .env with your configuration
# See env.example for all options

3. Start with Docker Compose (Recommended)

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

4. Or Start Manually

# Terminal 1: Start PostgreSQL and Redis
docker run -d -p 5432:5432 -e POSTGRES_PASSWORD=postgres postgres:16-alpine
docker run -d -p 6379:6379 redis:7-alpine

# Terminal 2: Start backend
cd backend && npm run start:dev

# Terminal 3: Start frontend
npm run dev

# Terminal 4: Start ML service
cd ml-service && python app.py

5. Access the Application

• Frontend Dashboard: http://localhost:3000
• Backend API: http://localhost:4000
• API Documentation: http://localhost:4000/api/docs
• ML Service: http://localhost:5000

📖 API Documentation

The API is documented with Swagger/OpenAPI. Access the documentation at:

http://localhost:4000/api/docs

Key Endpoints

☸️ Kubernetes Deployment

# Apply all manifests
kubectl apply -f k8s/

# Check deployments
kubectl get pods

# Check services
kubectl get services

# View HPA status
kubectl get hpa

🧪 Testing

# Frontend tests
npm run test

# Backend tests
cd backend && npm run test

# E2E tests
npm run test:e2e

📊 Performance Targets

🔐 Security & Compliance

• Authentication: JWT-based with secure token management
• Authorization: Role-based access control (RBAC)
• Data Encryption: TLS 1.3 in transit, encrypted at rest
• GDPR Compliance: Data export, deletion, consent management
• CCPA Compliance: Privacy rights dashboard

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Built with ❤️ by Jay Arre P. Talosig for data-driven e-commerce success