IBM Data Science Professional Certificate programme.
This is the Capstone Project.
- Language: Python.
- Scraping library: Python BeautifulSoup library.
- Other libraries: Numpy, Pandas, Matplotlib, sklearn (KMeans), Folium, etc.
- APIs: Foursquare converts postcodes into their equivalent latitude and longitude values.
- Creating a table for the city of Toronto that consist of three columns: PostalCode, Borough, and Neighbourhoods.
- In the newly created table 'not assigned' Borough and Neighbourhoods are removed and overwritten with the corresponding Borough values, respectively.
- The data stored in the table are extracted from the Wikipedia page at https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M using the BeautifulSoup library.
- Finding the latitude and longitude with Foursquare API.
- Adding two extra columns for latitude and longitude to the table (from PART 1) for Toronto diffferent neighbourhoods and boroughs.
- Generate maps to visualize the neighborhoods.
- Visualize how the neighbourhoods cluster together.