How can Web Scraping be Used to Extract Indian Restaurants Data in Canada?

This blog was created as a consequence of using a statistical approach for the IBM data science professional program's Capstone project and gaining a better understanding of how scientists visualize. This project’s primary objectives were to develop a business issue, look for information online, and compare several Toronto neighborhoods leveraging Foursquare geolocation data to identify the ideal area to open a brand-new food joint. To achieve the project's goals, we will employ step-by-step tactics.

Description of Problem

Take the example of someone who wants to open a brand-new Indian restaurant. Additionally, the person is Indo-Canadian and stays in Toronto, one of Canada's most populous metropolitan areas. Consequently, the person would decide whether operating an eatery would be good. And if setting up a new restaurant food business in a lucrative neighborhood is a wise option for him.

Learn more about Restaurant Data Scraping Services & its benefits.

Benefits

This venture will benefit a broad spectrum of people.

A local entrepreneur who wants to start a brand-new eatery in a specific region.
Indians who want to live in locations with a more significant percentage of Indian restaurants and traditions.
A specialist or research analyst who employs analytical and qualitative data analysis to evaluate the neighborhood.

Read below for details on how to extract Indian Restaurants data in Canada.

Collection of Data

Data may be gathered from several resources and can be utilized for multiple reasons, including the following

There are various sources from which the data can be collected and used for different purposes:

1. Canada’s Area Codes Listing

They are given below the listing of Canada's neighborhoods’ postal codes taken through Wikipedia.

2. Locational Coordinates

The CSV record below contains the Longitude and Latitude of the neighborhoods within Canada.

CVS Link:

3. Obtaining Venue Information

For obtaining the venue's data and location in this context, we'll be using the Foursquare API. In this instance, the forum serves as a boundary before we employ Folium.

Source: Foursquare API.

For each venue, you must retrieve the following:

Name of the Venue
The API-defined categorization
Venue’s Latitude position
Venue’s Latitude position
Venue liked by users
Ranking
Suggestions provided by consumers

Organizing the Data

Organizing the Information of Zip Code

The data module will contain three variables: Neighborhood, Borough, and Zip code. The processing only applies to cells that have a borough designated. Unallocated borough units should be ignored.

There could be several neighborhoods in a particular area code area. For instance, M5A, which includes the communities of Regent Parks and Harbourfront, has been repeatedly included in the table of the Wikipedia page. These two units will be combined within a single row, as shown in sequence 11 of the following table, with both neighborhoods divided by a comma.

The neighborhood is similar to the borough in cells where the borough is present, but still, the area is not.