Overview#

This project focuses on web scraping and data analysis of car listings from Facebook Marketplace, with the aim of uncovering trends such as:

• 📊 Average price over the years
• 🛣️ Average kilometrage
• 📅 Listing frequency by year

It uses Python tools to scrape, clean, and visualize the data for a specific make and model.

Technologies Used#

• BeautifulSoup (BS4) – HTML parsing for web scraping
• Splinter – Browser automation to navigate and collect listings
• Pandas – Data manipulation and aggregation
• Matplotlib – Bar charts and visualizations

Parameters for Customization#

You can easily adjust the script to target other listings by changing:

• make: e.g., "Honda"
• model: e.g., "Civic"
• min_year to max_year: e.g., 2000 - 2020
• min_price, max_price, min_mileage, max_mileage
• days_listed: Filter recent listings only
• transmission: e.g., "automatic"

📍 Note: Toronto is used as the location due to limited pricing data for the Algerian market.

Key Insights#

The analysis generates the following:

• Average Price by Year
• Average Kilometrage by Year
• Number of Listings by Year

These insights help users understand how prices and usage vary over time for a specific car model.

Visual Results#

The final output includes bar charts for:

• ✅ Average Price by Year
• ✅ Average Kilometrage by Year
• ✅ Listings Volume by Year

These make it easy to compare trends and assess market value or car wear by year.