The Coursera Applied Data Science Capstone Project.
- Author: Diardano Raihan
- Email: diardano@gmail.com
- Social Media: LinkedIn, Medium
Goal:
- To figure out the best locations for opening up a new coffee shop in Toronto City.
Target Audience:
- Entrepeneurs, Business Owners, Stakeholders, Data Scientists
Project Documentation:
Datasets:
- 1st Data: The most updated record of traffic signal vehicle and pedestrian volumes in Toronto City.
- 2nd Data: The most updated record of crime incidents reported in Toronto City provided by Toronto Police Services.
- 3rd Data: The list of Toronto neighborhoods represented by postal codes and their boroughs.
- 4th Data: The popular or most common venues of a given neighborhood in Toronto.
The followings are the step by step process for working with the project:
We will start the project by scraping the following Wikipedia page. https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M
Objective:
- Obtain the data inside the html page containing a list of Toronto postal codes in the form of table and transform the data into a pandas dataframe!
You can see the process in Pre1_Web_Scraping.ipynb
Objective:
- Now, we will get the latitude and the longitude coordinates of each neighborhood in order to utilize the Foursquare location data later in the separate main project notebook.
You can see the process in Pre2_Coordinate_Retrieval.ipynb
Objective:
- We will explore, segment, and group neighborhoods into clusters to find similar neighborhoods in Toronto City. As far as this project is concerned, we will use the Foursquare location dataset and use Foursquare API to access it.
You can see the process in Pre3_Clustering_Neigborhoods_Toronto.ipynb
Objective:
- Compile everything to acomplish the project's goal.
You can see the process in Project_Notebook.ipynb
Thank you,
Diardano Raihan
LinkedIn Profile