Amazon Scraper

A Python-based tool to scrape product data from Amazon for specified search queries. It extracts product titles, prices, reviews, and image links, saving results in JSON files. The script is designed for simplicity, scalability, and evasion of detection.

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Features

• Anti-Detection Measures:

  • Utilizes undetected-chromedriver to bypass Amazon's anti-bot mechanisms.
  • Avoids using Amazon's homepage to prevent CAPTCHA triggers.
  • Mimics human-like behavior with randomized typing and action delays.

• Scalability:

  • Supports multiple search queries from a JSON file.
  • Handles pagination for scraping multiple result pages per query.

• Data Organization:

  • Saves scraped data for each query in separate JSON files in the scraped_data folder.

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Installation

Prerequisites

• Python 3.8 or later
• Google Chrome
• ChromeDriver matching your Chrome version

Setup

1. Clone the repository:

   git clone https://github.com/username/repo-name.git
   cd repo-name

2. Install dependencies:

   pip install -r requirements.txt

3. Add search queries:

   • Create a user_queries.json file in the root directory with search terms:

     ["laptops", "wireless headphones", "gaming chairs"]

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Usage

Run the script with:

python amazon_scraper.py

The scraped data will be saved as JSON files in the scraped_data/ directory.

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File Structure

project-folder/
├── amazon_scraper.py       # Main script containing all functionality
├── user_queries.json       # Input file with search terms
├── scraped_data/           # Directory to save scraped data
├── requirements.txt        # List of dependencies

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Notes

• The scraper runs in --headless mode for efficiency.
• Ensure ChromeDriver matches your Chrome browser version.
• Debugging messages are logged to the console for troubleshooting.

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Dependencies

The script uses the following libraries:

• undetected-chromedriver
• selenium
• os and json for file operations
• time and random for human-like delays

Install all dependencies with:

pip install -r requirements.txt

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License

This project is licensed under the MIT License.

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Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.