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This repository contains a Python-based project that demonstrates web scraping and data analysis. The project involves extracting book-related data from theBooks to Scrape website, followed by exploratory data analysis (EDA) and visualizations to gain insights from the collected data.

The repository includes the following Jupyter Notebooks:

1. books-website-scraping.ipynb

   • Extracts book data such as titles, ratings, prices, and availability from the website.
   • Saves the scraped data into a CSV file for further analysis.

2. books-data-analysis.ipynb

   • Loads the scraped data from the CSV file.
   • Cleans and preprocesses the dataset (e.g., converting ratings to numerical values).
   • Performs EDA and visualizations to analyze pricing, ratings, and other trends.

• Web Scraping:

  • Extract book details including:
    • Book ID (UPC)
    • Title
    • Category
    • Rating
    • Price
    • Stock availability (Stock status)
    • Quantity available

• Exploratory Data Analysis (EDA):

  • Visualizes key metrics such as price distributions and rating trends.
  • Identifies relationships between features like price and rating.

• Web Scraping:

  • requests
  • BeautifulSoup

• Data Manipulation:

  • pandas

• Data Visualization:

  • matplotlib
  • seaborn
  • squarify

The scraped dataset is available on Kaggle:Books Data on Kaggle

1. ID: Unique Product Code (UPC) for each book.
2. Title: The title of the book.
3. Category: Genre or category of the book.
4. Price [£]: Price in GBP (£).
5. Rating: Star rating (One to Five) based on customer reviews.
6. Availability: Whether the book is in stock.
7. Quantity: The number of available copies.