Amazon Web Scraping Project

Overview
This project involves web scraping Amazon's product listings for "PlayStation 4" to collect data on various listings, including product names, prices, ratings, and availability. The extracted data is stored in a CSV file for further analysis and research purposes.

Table of Contents

• Overview
• Getting Started
• Prerequisites
• Installation
• Usage
• Project Structure
• Data Extraction
• Visualizations
• Contributing
• License
• Contact

Getting Started

These instructions will guide you on setting up the project and running the code on your local machine.

Prerequisites

• Python 3.7+
• Jupyter Notebook or any compatible IDE to run .ipynb files
• Libraries: requests, BeautifulSoup, pandas
  • Install these libraries using:

    pip install requests beautifulsoup4 pandas

Installation

1. Clone the repository:

   git clone https://github.com/your-username/amazon-web-scraping.git

2. Navigate to the project directory:

   cd amazon-web-scraping

3. Open the Jupyter Notebook:
   • Use Jupyter Notebook to open Amazon Web Scraping.ipynb or Amazon Web Scraping Sample.ipynb files.

Usage

1. Run the Notebook:
   • Execute the cells in the notebook to scrape the Amazon product page and extract data for "PlayStation 4" listings.
2. Data Extraction:
   • The scraped data will be saved in a CSV file named amazon_data.csv, which includes columns like product name, price, rating, and availability.

Project Structure

• Amazon Web Scraping.ipynb: Main Jupyter Notebook for scraping data.
• Amazon Web Scraping Sample.ipynb: Sample notebook demonstrating the scraping process.
• amazon_data.csv: Output CSV file containing the scraped data.
• image.png: Screenshot of the Amazon search results page.
• 

Data Extraction

• Data Fields: The following fields are extracted for each product:

  • Product Name
  • Price
  • Rating
  • Availability

• CSV Output: The extracted data is stored in amazon_data.csv, making it easy to analyze and visualize the information.

Visualizations

The following visualizations were created to provide insights into the data:

Distribution of Product Ratings

Distribution of Product Prices

Scatter Plot of Price vs Rating

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/YourFeature)
3. Commit your changes (git commit -m 'Add feature')
4. Push to the branch (git push origin feature/YourFeature)
5. Create a Pull Request

License

This project is licensed under the MIT License.

Contact

For questions or support, please reach out to:

• Your Name Kirolos Daniel
• Email: kiroemad2389@gmail.com