How to scrap the Housing Market
A short tutorial for beginner in Python
Toronto is known for its crazy housing market. It’s getting harder and harder to find an affordable and convenient place. Searching for “How to find an apartment in Toronto” on Google leads to dozens of pages of advice, which is a pretty good indicator that apartment hunting is a painful process.
As a Data Scientist trainee, I was sure that I could alleviate this pain a bit and simplify the way people search for a place to live in. The project I came up with aims to find out the relationships between the price of an apartment in the Greater Toronto Area (GTA), its location, surface, and the number of bedrooms. The business idea of this project is to help apartment seekers to find the best deal across different neighbourhoods in the GTA.
To conduct this project, I decided to use the popular website Craiglist. My idea was to extract the data from the website using a web scraping tool from Python (version 3.7.4), named Beautiful Soup.
