SAL_BW_Project_1 - Web Scraping, SQL Insights, and Data Visualization

Project Overview

This project focuses on extracting book data from Books to Scrape through web scraping, storing it in a CSV file, deriving SQL-based insights, and conducting Exploratory Data Analysis (EDA) with visualizations.

Project Workflow

1. Web Scraping: Extracted book details (Title, Price, Availability, Rating) and saved them in Cleaned_Build_week_project1.csv.
2. SQL Insights: Loaded the dataset into MySQL Workbench and generated insights using SQL queries.
3. EDA & Visualization: Analyzed data trends and visualized them using Python.

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Dataset

• Cleaned_Build_week_project1.csv: Contains scraped book data with the following columns:
  • Title: Book title
  • Price: Price in GBP (£)
  • Availability: Stock status
  • Rating: Book rating (1 to 5 stars)

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Files in this Repository

File Name	Description
BW_Web_scraping.ipynb	Jupyter notebook for web scraping
Cleaned_Build_week_project1.csv	Scraped book data in CSV format
BW_SQL_Queries.sql	SQL queries for analysis
BW_EDA_Data_Visualization.ipynb	Jupyter notebook for EDA & visualizations
BW_Insights	Presentation images summarizing insights

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How to Run the Project

1. Web Scraping

Run BW_Web_scraping.ipynb to scrape book data and save it as Cleaned_Build_week_project1.csv.

2. Load Data into SQL

1. Create a database and table using BW_SQL_Queries.sql.
2. Import Cleaned_Build_week_project1.csv into the database.

3. SQL Analysis

Execute queries in BW_SQL_Queries.sql to generate insights:

• Total books in stock
• Top 5 expensive books
• Average book rating
• Distribution of books by rating
• Books category by price
• Longest book title

4. Exploratory Data Analysis (EDA)

Run BW_EDA_Data_Visualization.ipynb to:

• Analyze data distributions
• Generate visualizations:
  • Bar chart for book ratings
  • Histogram for price distribution
  • Pie chart for stock availability
  • Heat map for price and rating correlation

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Insights from Analysis

• Book Ratings: Significant proportion of 1-star reviews (226), indicating possible quality concerns.
• Price Distribution: Prices range between £10-£60, with distinct price categories.
• Stock Status: All books are in stock, suggesting either efficient inventory management or low demand.
• Price vs. Rating: A near-zero correlation (0.03) suggests no significant impact of price on ratings.

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Project Submission

• GitHub Repository: [https://github.com/wajiha-khanam/Build_Week_Project]

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Contributors

• Wajiha Khanam
• Vaishnavi Karnik
• Swapnil Prasad