https://github.com/prak112/coursera-ibm_capstone

Cluster analysis of specific venues within a given geographical zone (district/borough)
https://github.com/prak112/coursera-ibm_capstone

capstone-project data-analysis-python geospatial-analysis geospatial-visualization ibm-datascience-certification

Last synced: 1 day ago
JSON representation

Cluster analysis of specific venues within a given geographical zone (district/borough)

• Host: GitHub
• URL: https://github.com/prak112/coursera-ibm_capstone
• Owner: prak112
• License: mit
• Created: 2020-11-25T21:29:41.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2023-06-14T20:09:58.000Z (over 1 year ago)
• Last Synced: 2023-06-14T22:23:40.622Z (over 1 year ago)
• Topics: capstone-project, data-analysis-python, geospatial-analysis, geospatial-visualization, ibm-datascience-certification
• Language: Jupyter Notebook
• Homepage:
• Size: 4.88 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Coursera-IBM_Capstone

## Final Projects of IBM Data Science Professional Certification 

* **Data sources** - *Wikipedia, FourSquare API*

* **Libraries** - *BeautifulSoup, re, pandas, geocoder, folium, requests, sklearn, matplotlib*

* **Techniques** - *Data Import, Data Extraction, Data Cleaning & Understanding, Geospatial Visualization, Clustering*

## [project1](code/project1/popularVenues_Toronto.ipynb)

 **popularVenues_Toronto** - Few selected Boroughs of Toronto, Canada are analysed for the top venue categories across each neighborhood within the Boroughs. The identified top venue categories are clustered based on venue category similarity across each neighborhood.

## [project2](code/project2/marketHotspots_Helsinki.ipynb)

**marketHotspots_Helsinki** - Extended the *popularVenues_Toronto* strategy towards an imaginary business problem of allocating profitable locations within the districts of Helsinki, Finland for a chain of Indian restaurants. Possible restaurant hot-spots are identified and visualized based on:

* absence of other Indian restaurants in entire district

* absence of other Indian restaurants within popular venues