Python libraries: Pandas | Numpy | Requests | BeautifulSoup | Folium | Matplotlib | Scikit-learn
Machine Learning technique: k-Means Clustering
I analyzed Copenhagen neighborhoods to find the best locations for a stay in the city. This analysis is the final Applied Capstone Project of my IBM Data Science Certificate. Check out the certificate right here: IBM Certificate
The analysis was performed with the help of:
- List of all Copenhagen neighborhoods from Regionh.dk
- Geolocation data from Google Maps Geocoding API
- Venues data from Foursquare API
- Two relevant clusters emerge from the analysis as potential locations for a stay at Copenhagen.
- The green Cluster represented the remote area of the city with convenient-based activity, while the red Cluster concerned the center with a more diversified activity.
- The back/southewest of the city presented more essentials facilities. In the other hand, the center/east appeared to be more appropriate for a longer stay in Copenhagen.