This is a project in the Applied Data Science Capstone course on Coursera. The purpose is to explore neighborhoods in Toronto and group them into clusters.
Toronto neighborhood data is retrieved from Wikipedia using Beautiful Soup, processed and stored into a dataframe.
https://github.com/rickysoo/toronto/blob/master/toronto1.csv
Toronto neighborhood location data is retrieved using Geopy library and stored into a dataframe.
https://github.com/rickysoo/toronto/blob/master/toronto2.csv
Places in Toronto neighborhoods are explored using Foursquare API. Data is wrangled and neighborhoods are grouped into clusters using k-means clustering algorithm. Analysis and interpretation are made into each cluster.
https://github.com/rickysoo/toronto/blob/master/toronto3.csv
Note: Interactive maps are used in Part 3 but they might not show up on Github.com. You may view the full maps at https://nbviewer.jupyter.org/github/rickysoo/toronto/blob/master/Toronto-Part3.ipynb
103 neighborhoods in Toronto have been grouped into 3 clusters as below:
- Cluster 0 (14 neighborhoods) - Mostly "Leisure" venues
- Cluster 1 (87 neighborhoods) - Mostly "Food & beverages" venues
- Cluster 2 (2 neighborhoods) - Mostly "Lifestyle" venues