Car Sales Scraper & Visualization Demo

2. Choose car models

Sedan SUV Sedan vs SUV product lines.

3. Choose comparison metrics (multi-select)

Total sales (units) Total revenue ($) Total profit ($) Average price ($ per car) Average customer satisfaction (1–5) Average yearly maintenance cost ($)

4. Choose year range (2023–2025)

Start year: End year:

6. Scraper algorithm (single choice)

1) requests + BeautifulSoup (static HTML table) 2) JSON API request (simulated) 3) Headless browser / Selenium (simulated)

7. Car type dimension (single choice)

🚗 By body style (sedan / SUV / hatchback...) 🔋 By powertrain (ICE / hybrid / EV...) 🏢 By usage / segment (economy / luxury / commercial)

The selected dimension is encoded into the demo scraping URL and recorded in the log.

Data science learning points

How web scraping turns car websites into structured tables.
How to clean and aggregate data with pandas.
How to design consumer-focused metrics: sales, revenue, profit, satisfaction, cost.
How to compare brands and models across multiple years.
How visualization choices (line/bar/radar/heatmap/...) change the story.

Teaching objectives

Students can describe a basic web scraping pipeline.
Students can explain the difference between raw data and business KPIs.
Students can build comparisons between brands from the same dataset.
Students can choose appropriate chart types for different questions.
Students see a full path: website → dataset → metrics → charts.

Car Sales Scraper & Data Science Lab

1. Choose car brands (at least TWO)

2. Choose car models

3. Choose comparison metrics (multi-select)

4. Choose year range (2023–2025)

5. Choose chart types (multi-select)

6. Scraper algorithm (single choice)

7. Car type dimension (single choice)