🍜 ShopeeFood Web Scraping

ShopeeFood_Web_Scraping is a Python project designed to scrape restaurant and food-related information from ShopeeFood Vietnam. The goal is to automate data collection for purposes such as food trend analysis, pricing research, and restaurant performance tracking.

🎯 Project Objectives

This project focuses on collecting and organizing data from ShopeeFood with the following key outputs:

• Restaurant Metadata
  • Name, address, categories, coordinates, and delivery info
• Menu Information
  • Dishes, prices, descriptions, availability
• Ratings and Reviews
  • Average rating, number of reviewers, tags (e.g., "tasty", "fast delivery")

🛠️ Technologies Used

• Python 3.x – core programming language
• Requests – for sending HTTP requests
• Selenium
• Pandas – for processing and exporting structured data
• JSON / CSV – for storing results
• Time / Random – for delay logic to avoid getting blocked

📦 Project Structure

SHOPEEFOOD_WEB_SCRAPING/  
├── .gitattributes  
├── links_shopee.txt  
├── README.md  
├── restaurant_shopefood.csv  
└── shopeefood_webscraping.ipynb  

Output Data

Format: CSV

Example CSV Output:

🔍 How the Code Works

Step 1: Get Restaurant Links

• The get_links.py script sends requests to category/location-based ShopeeFood URLs.
• Parses pagination pages to gather all restaurant page URLs.

Step 2: Extract Restaurant Details

• The get_restaurant_info.py script loops through restaurant URLs.
• Extracts key fields like name, location, menu, prices, ratings, etc.
• Cleans and structures the data for export.

Step 3: Save Data

• Data is stored in both .json (raw) and .csv (tabular) formats.
• Files are saved into the /output directory for later use or analysis.

📊 Potential Applications

• Market research on food delivery platforms
• Menu and pricing trend analysis
• Clustering restaurants by cuisine, rating, or delivery zones
• Building dashboards for F&B performance by city or district