Web Scraper (E-commerce website)

Web Scraper for Amazon Product Data Extraction 📊

This project is a Jupyter Notebook designed to scrape product information, including names, prices, and ratings, from Amazon's search results page for "iPhone." The extracted data is then stored in a structured format (CSV file) for easy analysis and use.

⭐Features

Web Scraping: Uses BeautifulSoup and requests to extract product data from Amazon. Data Storage: Extracted data is stored in a CSV file for easy access and analysis. Data Processing: Data is organized using pandas for further processing and manipulation.

⚙️Prerequisites

Before running the notebook, ensure you have the following Python packages installed:

1. requests
2. BeautifulSoup4
3. pandas You can install these packages using pip:

bash pip install requests beautifulsoup4 pandas

🚀Usage

1. Clone the Repository: bash git clone https://github.com/AdityaGhate29/PRODIGY_SD_05.git

cd amazon-product-scraper

3. Run the Jupyter Notebook:

Open the Jupyter Notebook in your preferred environment (e.g., Jupyter Notebook, JupyterLab, VSCode, etc.) and run each cell step-by-step.

3. Scraping Amazon Data:

The notebook will fetch the first page of search results for "iPhone" on Amazon and extract product names, prices, and ratings.

4. Saving Data:

The extracted data is saved in a CSV file named amazon_products.csv.

📝Notes

Disclaimer: Web scraping should be done responsibly and in compliance with the website's terms of service. This notebook is intended for educational purposes only. Amazon’s Anti-Scraping Measures: Amazon has measures in place to prevent automated scraping. If you encounter issues like CAPTCHAs or blocks, you may need to adjust your scraping strategy or use a service that handles these challenges.

🗝️License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝Contributing

Contributions are welcome! If you have any ideas, suggestions, or improvements, please create an issue or submit a pull request.

Acknowledgments

Inspired by the need to analyze product data from e-commerce websites. Thanks to the Python community for their excellent tools and libraries.