A good friend of mine figured she is in urgent need of a second-hand car. She was also adamant that she will get one through the We Buy Cars dealership. I tried to convince her to be diverse and check out other dealerships but no! She only trusts We Buy Cars.
So, trying to be helpful, I thought let me get her the data from the dealership website and save it in a spreadsheet to save her Browse time.
First, I tried to scrape the HTML data from the website using the Requests and BeautifulSoup libraries. But that led to a dead end because the site does not allow that. So now with a different approach, I will get the data using their hidden API.
This project is a data analysis of second-hand hatchback vehicles from the WeBuyCars dealership in South Africa. The project uses Python to scrape data, perform an initial assessment, and analyze the car market. The entire process is documented in a Jupyter Notebook (buyCars.ipynb).
The primary objectives of this project were to:
The following Python libraries were used:
requests for making API calls.pandas for data manipulation and analysis.seaborn and matplotlib.pyplot for data visualization.os for file system operations.During the initial data assessment, I found that the dataset contained 1920 entries and 92 columns. A significant number of columns contained null values, particularly those related to auctions, as only a small subset of the vehicles were on auction at the time of data collection. It was also noted that some numerical columns, like NoGears, were stored as strings and would require cleaning before analysis.
TO DO!