EVOASTRA VENTURES

Aim:

To automate the extraction of detailed car listings from the AckoDrive website using Python-based web scraping techniques for data analysis and exploration.

Tools & Libraries Used:

• Python

• requests – to fetch HTML pages

• BeautifulSoup – for HTML parsing and data extraction

• re (Regular Expressions) – for precise pattern matching

• pandas & numpy – for data organization and handling missing values.

  
  

Approach:

• Designed a web scraper to traverse through multiple pages (page/1 to page/19) of AckoDrive's car listing section.

• Extracted multiple attributes for each car listed on the website using class selectors:

  
  

  • Brand & Model

  • Car Type (e.g., SUV, Sedan)

  • Fuel Type (e.g., Petrol, Diesel, CNG, Electric)

  • Transmission Type (Manual or Automatic)

  • Seating Capacity

  • Color Availability

  • Variant Details

  • Price

  • City-wise Location

    
    

• Used regex to isolate and clean specific values (like number of seaters, fuel types, and variants).

• Stored the extracted data into a structured pandas DataFrame, making it suitable for analysis, visualization, or export to CSV.

  
  

Results:

• Successfully scraped and structured detailed information for hundreds of car variants across various cities in India.

• Generated a clean, tabular dataset which can be used for further use cases such as price comparison, trend analysis, fuel preference insights, and location-based availability.

• Built a reusable and extendable scraping script with modular data extraction logic.

  
  

Outcome: This project demonstrates the ability to extract structured data from semi-structured web pages using Python, effectively handling nested HTML elements, applying pattern recognition via regular expressions, and transforming raw data into an analyzable format.