Team Project Spotlight: How Students Built a Quote Scraping System from the Ground Up
A team of data science students recently completed an impressive project that demonstrates the full data pipeline from web scraping to visualization. The project, named ‘CREP-5 codes,’ focused on collecting quotes from a website, storing them systematically, and performing insightful analysis.
The Web Scraping Process
Team member Ranali Sinde handled the scraping portion of the project. The team targeted ‘codestoscrape.com’, a website containing numerous famous quotes. Using Python with the requests and Beautiful Soup libraries, they analyzed the site’s structure and identified that each quote was contained within a div tag with a ‘code’ class.
The scraping process extracted three key elements from each quote: the quote text itself, the author’s name, and associated tags. Since the website featured multiple pages, the team implemented a while loop to navigate through all pages, gathering data until completion.
The end result was a comprehensive dataset containing over 100 quotes from more than 50 unique authors, tagged with over 80 different descriptors. This data was organized into a CSV file using pandas, preparing it for further analysis.
SQL-Based Analysis
Once the data was collected, team member Devish Savarkar imported the CSV into a MySQL database for more advanced analysis. The database contained three main columns: author, quotes, and tags.
Several interesting queries were developed:
- Author frequency analysis revealed Albert Einstein as the most quoted author, followed by Jane Austen and Leigh Monrir
- Tag analysis determined that ‘life’, ‘inspiration’, and ‘truth’ were the most commonly used tags
- Quote length analysis identified both the longest quotes (around 300 characters) and the shortest quotes
The SQL component of the project demonstrated how relational databases can be used to extract meaningful patterns from text data.
Data Visualization
Anupam Kumar transformed the analytical findings into visual representations using pandas, matplotlib, seaborn, and word cloud libraries. After verifying the data was clean without missing values or duplicates, Anupam created several visualizations:
- Bar charts showing the most quoted authors
- Word clouds displaying the most common words used in quotes
- Pie charts illustrating the proportion of the top five tags
- Histograms showing the distribution of quote lengths
- Box plots comparing quote lengths by author
- Heat maps revealing correlations between quote length and number of tags
- Count plots of the most common tags
These visualizations transformed raw data into an accessible story, making patterns and insights immediately apparent to viewers.
Teamwork and Learning Outcomes
The students emphasized that teamwork was critical to their success. When faced with challenges—such as unexpected SQL query results or visualization issues—they collaborated through screen sharing and open discussion to find solutions.
Each team member highlighted valuable learning outcomes from the project:
- Gaining confidence in web scraping techniques
- Developing skills in SQL for text data analysis
- Learning to select appropriate visualization methods for different data types
What began as a simple data collection exercise evolved into a comprehensive project covering the entire data science workflow—from extraction to storage, analysis, and presentation.
The ‘CREP-5 codes’ project demonstrates how even straightforward data sources like quotes can yield meaningful insights when approached with the right analytical tools and teamwork.