📊 Enterprise Data Scraping and Email Validation

📌 Project Overview

This project automates the collection of enterprise-related data from multiple sources using Python and Selenium for web scraping. It includes validating email addresses, extracting comments from a Facebook post, and gathering enterprise information (e.g., name, address, SIREN) from two French business directories. The scraped data is saved as CSV files for further analysis.

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📂 Data Sources

• Email Validation: VerifyEmailAddress.org to check email validity.
• Facebook Comments: Extracted from a specific post on MBI Network.
• Enterprise Info:
  • Le Figaro Entreprises
  • Manageo

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** Chrome Setup**

Configure and initialize Chrome WebDriver for scraping.

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from webdriver_manager.chrome import ChromeDriverManager

chrome_options = Options()
chrome_options.add_argument("--start-maximized")
driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=chrome_options)

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📦 Requirements

pip install selenium webdriver-manager pandas requests

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▶️ How to Run

1. Clone the repository:

   git clone https://github.com/ali27kh/Python_Web_Scraping.git
   cd Python_Web_Scraping

2. Install dependencies.
3. Run the scraping script.

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📌 Key Insights

• Email validation ensures reliable contact information using VerifyEmailAddress.org.
• Facebook comment scraping from posts.
• Le Figaro and Manageo provide complementary enterprise data.
• Selenium automation ensures robust handling of dynamic web content.

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📜 License

MIT License