Webscraping-SQL-EDA | Masai School Build-Week Project

Role: Team Member (1 of 3)
Focus Areas: Web Scraping · SQL Insights · Exploratory Data Analysis (EDA)

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Project Overview

This build-week project (Masai School) focused on scraping book data from the web, analyzing it using SQL, and deriving insights through Exploratory Data Analysis (EDA).

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Tech Stack

• Python (web scraping, data cleaning, EDA via Jupyter Notebook)
• SQL (data storage and querying: .sql scripts)
• CSV (intermediate storage)
• Jupyter Notebook (.ipynb)
• Optional: PowerPoint (.pptx) for presentations

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Project Workflow

1. Web Scraping: Used Python to extract book details (title, price, ratings, etc.) into book_data.csv.
2. Data Cleaning: Handled missing values, duplicates, and formatting errors.
3. SQL Analysis: Used .sql scripts (BooksData_Insights.sql) to derive insights (e.g., availability, expensive books, etc.).
4. EDA & Visualization: Conducted in notebook (EDA.ipynb) — included charts like histograms, boxplot, and summary tables.
5. Presentation: Summarized findings in a PowerPoint (Web-Scraping-SQL-Insights-and-EDA.pptx).

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Key Insights

• Price Distribution: Majority of books are priced under £30.
• Ratings: Most books have 1–3 star ratings; 4–5 star books are higher priced.
• Availability: Most books are fully available.
• Price vs Rating: Higher-rated books (4–5 stars) generally have higher prices, indicating correlation between quality and price.

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Team Members

Name	Role
Tanmay Manna	Team Lead — Web Scraping
Diya Shah	SQL Insights
Prince Raj Gupta	Exploratory Data Analysis (EDA)

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How to Run Locally

# Clone repo
git clone https://github.com/princerg/webscraping-sql-eda.git
cd webscraping-sql-eda

# Install dependencies
pip install -r requirements.txt

# Run Jupyter Notebook
jupyter notebook notebooks/EDA.ipynb

# Execute SQL insights
# Open BooksData_Insights.sql in MySQL Workbench and run queries