Fork & Friends is a comprehensive data analysis and social recommendation platform leveraging the Yelp dataset. Built with React and modern web technologies, this platform combines big data analytics with AI-powered recommendations to help users discover new restaurants, connect with like-minded food enthusiasts, and explore dining trends across metropolitan areas. The platform processes over 6.6 million reviews and 192,000+ businesses to deliver intelligent insights and personalized recommendations.
- Comprehensive Data Analytics: Interactive dashboards for business, user, review, rating, and check-in analysis
- AI-Powered Recommendations: DeepSeek AI integration for intelligent friend and business suggestions
- Advanced Data Visualizations: D3.js powered charts, graphs, and interactive maps
- Multi-Analysis Dashboard: Six different analysis modules with detailed insights
- Responsive Design: Works seamlessly across desktop, tablet, and mobile devices
- Real-time Data Processing: Live updates from big data processing pipeline
- Geographic Analysis: Location-based insights across 10 metropolitan areas
- Sentiment Analysis: NLP-powered review sentiment and word association analysis
- Social Network Features: Friend recommendations based on dining preferences
- Interactive Word Clouds: Dynamic visualization of review content using POS tagging
- Collaborative Filtering: Advanced recommendation algorithms for personalized suggestions
- Framer Motion Animations: Smooth, engaging user interface animations
- React: Frontend library for building the user interface
- Vite: Next-generation frontend build tool for fast development
- React Router: Navigation and routing for single-page application
- Tailwind CSS & DaisyUI: Utility-first CSS framework with component library
- D3.js: Data visualization library for interactive charts and maps
- Framer Motion: Animation library for smooth UI transitions
- Leaflet & React Leaflet: Interactive maps for geographic data visualization
- Axios: HTTP client for API communication
- Lucide React: Modern icon library
- EmailJS: Client-side email service integration
- React Icons: Comprehensive icon library
Before you begin, ensure you have the following installed:
- Node.js (v16 or higher)
- npm or yarn
- The Fork & Friends backend API running (see Server Repository)
-
Clone the repository:
git clone https://github.com/NasimRanaFeroz/Fork-Friends_Front-End.git cd Fork-Friends_Front-End -
Install dependencies:
npm install # or yarn install -
Create a
.envfile in the root directory:# API Configuration REACT_APP_API_BASE_URL=your_backend_api_url # EmailJS Configuration REACT_APP_EMAILJS_SERVICE_ID=your_emailjs_service_id REACT_APP_EMAILJS_TEMPLATE_ID=your_emailjs_template_id REACT_APP_EMAILJS_PUBLIC_KEY=your_emailjs_public_key
-
Start the development server:
npm run dev # or yarn dev # Note: Due to special characters in folder path, you may need to run: node "node_modules/vite/bin/vite.js"
-
Open your browser and navigate to
http://localhost:5173
| Variable | Description |
|---|---|
| REACT_APP_API_BASE_URL | Backend API base URL for data fetching |
| REACT_APP_EMAILJS_SERVICE_ID | EmailJS service ID for contact form |
| REACT_APP_EMAILJS_TEMPLATE_ID | EmailJS template ID for email formatting |
| REACT_APP_EMAILJS_PUBLIC_KEY | EmailJS public key for authentication |
The main interface displays platform overview, featured analytics, team information, and quick access to analysis dashboards and recommendation services.
Navigate to /data-analysis to access six comprehensive analysis modules:
- Top merchants identification across the U.S.
- City and state rankings by merchant density
- Merchant ratings and category analysis
- Restaurant type exploration (Chinese, American, Mexican, etc.)
- User growth trends and yearly statistics
- Elite vs. regular user comparisons
- Top reviewers and most popular users identification
- User engagement metrics and silent vs. active user analysis
- Yearly review trend analysis
- Sentiment analysis and word frequency studies
- Word association analysis and common phrases
- Interactive word clouds with POS tagging
- Positive and negative sentiment breakdowns
- Rating distribution analysis (1-5 stars)
- Five-star business identification
- Weekly rating patterns and trends
- Business rating evolution over time
- Check-in patterns across different time periods
- City-wise check-in analysis
- Yearly check-in trends and growth
- Integrated multi-dimensional analysis
- Cross-dataset insights and correlations
- Top merchants by city with combined metrics
- AI-powered friend suggestions based on dining preferences
- Collaborative filtering using similar ratings and reviews
- Interest-based matching through DeepSeek AI integration
- Social network analysis for connection recommendations
- Personalized restaurant suggestions
- Location-based recommendations with map integration
- Cuisine preference analysis and matching
- User profile-based collaborative filtering
Learn about the team, project vision, and the comprehensive Yelp dataset including:
- 6,685,900 reviews analyzed
- 192,609 businesses processed
- 200,000+ pictures catalogued
- Data from 10 major metropolitan areas
Fork-Friends_Front-End/
├── node_modules/
├── public/
│ ├── index.html
│ ├── favicon.png
│ └── manifest.json
├── src/
│ ├── components/
│ │ ├── business/
│ │ ├── checkin/
│ │ ├── comprehensive/
│ │ ├── rating/
│ │ ├── review/
│ │ ├── user/
│ │ ├── BusinessAnalysis.jsx
│ │ ├── CheckInAnalysis.jsx
│ │ ├── ComprehensiveAnalysis.jsx
│ │ ├── Footer.jsx
│ │ ├── ImageSlider.jsx
│ │ ├── Navbar.jsx
│ │ ├── RatingAnalysis.jsx
│ │ ├── ReviewAnalysis.jsx
│ │ └── UserAnalysis.jsx
│ ├── pages/
│ │ ├── AboutUs.jsx
│ │ ├── Analysis.jsx
│ │ ├── BusinessRecommendation.jsx
│ │ ├── FAQ.jsx
│ │ ├── Feedback.jsx
│ │ ├── FriendRecommendation.jsx
│ │ ├── Home.jsx
│ │ └── LegalPage.jsx
│ ├── assets/
│ │ ├── ...
│ │ ├── reality.png
│ │ └── restaurant.png
│ ├── App.jsx
│ ├── index.css
│ └── main.jsx
├── .env
├── .gitignore
├── eslint.config.js
├── index.html
├── package.json
├── README.md
├── tailwind.config.js
└── vite.config.js
- Azizur Rahman - Project Lead & Full-Stack Developer
- Nasim Rana Feroz - Frontend Developer & UI/UX Designer
- MD Nezam Uddin - Backend Developer & Database Engineer
Note: This project processes publicly available Yelp dataset for educational and research purposes. All data analysis and recommendations are based on anonymized user data and publicly accessible business information.