The extracted dataset will contain structured information gathered from the target website(s) based on the project requirements. The data will be cleaned, formatted, and delivered in a structured file format, such as CSV, JSON, or Excel. Key Features: Includes all requested fields (e.g., product names, prices, descriptions, stock availability). Format: Delivered in CSV for easy analysis, with options for JSON or database export. Data Cleaning: Duplicates removed, missing values handled, and special characters normalized. Timestamps: Each entry will have a timestamp to indicate when it was scraped.

The scraper code will be a fully functional Python script designed to extract the required data from the target website(s). The script will be optimized for efficiency, error handling, and scalability. Depending on the project’s complexity, the scraper may use Scrapy, BeautifulSoup, Selenium, or Requests to navigate, extract, and store the data. Key Features: Technology Used: Python with Scrapy, BeautifulSoup, Selenium, or Requests. Modular Code: Clean, well-structured, and easy to modify or extend. Headless Browsing (if needed): Uses Selenium or Puppeteer for JavaScript-heavy websites. Error Handling & Logging: Catches errors, retries failed requests, and logs activities. Proxy & CAPTCHA Handling (if required): Supports rotating proxies and CAPTCHA bypass. Data Storage Options: Saves output in CSV, JSON, or databases (MySQL, PostgreSQL, MongoDB).