🌟 QuoteVerse – Mining and Visualizing

A data-driven project that scrapes inspirational quotes from QuotesToScrape.com, stores them in a structured format, and performs insightful visualizations to uncover patterns in quotes, authors, and tags.

---

Tags:
NLP Text Mining Sentiment Analysis Data Visualization Python Web Scraping Machine Learning Natural Language Processing Word SQL Cloud Database Plotly Matplotlib Seaborn

---

📌 Project Overview

QuoteVerse is a web scraping and analytics project designed to collect quotes, authors, and tags from QuotesToScrape.com using BeautifulSoup4. The collected data is then cleaned, structured, and analyzed to produce insights and visualizations that reveal:

• The most quoted authors
• The most common tags/themes
• Distribution of quotes across topics

---

⚙️ Features

• Automated Web Scraping – Collect quotes, authors, and tags efficiently.
• Data Cleaning & Structuring – Store in CSV for easy processing.
• Exploratory Data Analysis – Discover trends in inspirational content.
• Beautiful Visualizations – Represent findings using charts and graphs.

---

🛠️ Tech Stack

• Languages: Python
• Libraries & Frameworks: BeautifulSoup, Pandas, Matplotlib, Seaborn, NLTK, Plotly
• Tools: Jupyter Notebook, Git, GitHub

---

📊 Project Workflow

🔹 Web Scraping (by Pranali)

• Scraped multiple paginated pages using Requests and BeautifulSoup.
• Extracted quote text, author names, and associated tags.
• Exported the cleaned data to CSV for further processing.

🔹 SQL Design & Querying (by Devesh)

• Created a relational database schema with tables for quotes, authors, and tags.
• Managed many-to-many relationships via a bridge table.
• Wrote SQL queries to identify top authors, common tags, and quote distributions.

🔹 Exploratory Data Analysis (by Anupam)

• Structured and cleaned the dataset using Pandas.
• Visualized data using bar charts, word clouds, histograms, and box plots.
• Derived insights such as author trends, quote length distributions, and thematic focus.

---

🤝 Team Collaboration

• Collaborated through code reviews, pair programming, and planning meetings.
• Conducted Q&A sessions to reinforce understanding and improve communication.
• Used GitHub for version control and coordinated tasks via regular team syncs.

---

🔍 Key Insights

• Top Authors: Albert Einstein and Jane Austen appeared most frequently.
• Popular Tags: “love,” “life,” and “inspirational” were dominant.
• Quote Length: Most quotes were concise, making them impactful and shareable.
• Thematic Overlap: Some authors consistently used similar tags, reflecting a strong thematic identity.

---

💡 Learnings

Technical:

• Hands-on experience in web scraping, relational modeling, and data storytelling.

Collaboration:

• Improved skills in teamwork, code reviews, and Git-based workflows.

Time Management:

• Balanced coding, documentation, and meetings under tight deadlines.

---

🧑‍💻 Contributors

• Pranali: Project Lead, Web Scraping, Data Structuring
• Devesh: SQL Design, Query Optimization
• Anupam: EDA & Visualization, Documentation