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Showing posts with the label Web scraping

How to Scrape Toronto Restaurants Map Data by Category using Web Scraping?

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  For scraping the restaurant’s data in Toronto, it is necessary to scrape the latitude and longitude of all the Toronto Restaurants available on the Yelp directory and then plot them on a public map. This could be filtered using the restaurant’s category. How to Perform Scraping for Toronto Restaurants Map Data? The Data The data will be  extracted from Yelp’s Toronto Restaurant  categories page. The project will click into each category on the website and retrieve all of the restaurants that display, including their latitude, longitude, number of reviews, rating, location, phone number, restaurant, and pricing range. Creating the Map You can try various options on how to plot the data, such as  Google Maps API , but the ultimate option would be to place it on public maps. What will You Search for? Toronto is recognized for its diversity, with BBC Radio describing it as the world's most varied metropolis. Over 50 % of the population was raised from outside Canada, with 232 differe

How Web Scraping is Used to Explore Indian Restaurants in Canada?

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  This blog is the result of working on a real dataset that works as a part of the IBM data science professional program Capstone project and gaining a feel of what scientists think in their life. The main goals of this project were to create a business problem, search the web for data, and evaluate several districts in Toronto using Foursquare location data to determine which neighborhood is best for starting a new food business. We will use step-by-step strategies to get the desired objectives in this project. Problem Description Consider the case of an individual who wishes to launch a new Indian restaurant. And the individual is Indo-Canadian and resides in Toronto, Canada's most populous city. As a result, he is unsure whether or not opening a restaurant is a wise idea. And if it's a good idea for him to open his new restaurant in which neighborhood, for it to be profitable. Advantages This project will assist a diverse range of individuals. Entrepreneur who wishes to open

How to Scrape Grocery Delivery Data Using Web Scraping?

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  The convenience and easy access provided by grocery delivery online platforms have helped people avoid their weekly trips to the nearest grocers and made them buy groceries online. This industry’s revenue is projected to increase by 20% annually from 2021 through 2031. Websites and apps like DoorDash, Amazon Fresh, InstaCart, etc. have witnessed a huge number of orders. Because of digital technology advanceme n ts, better logistics support, and the busiest personal and professional lives of the people, online grocery delivery websites have become very successful. If you want to expand and improve the grocery delivery services or start a new one,  web scraping  is the solution, which helps you, achieve the business targets. Why Scrape Grocery Delivery Data? The aims of all grocery delivery businesses using data scraping services can be diverse. You could target all the accessible data fields, or concentrate on some, which are important for completing particular business objectives. Le

How to Scrape Food Data with Google Maps Data Scraping Using Python & Google Colab?

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  Do you want a complete list of restaurants with their ratings and addresses whenever you visit a place or go for holidays? Off-course yes as it makes your way much easier. The easiest way to do it is using data scraping. Web scraping or data scraping imports data from a website to the local machine. The result is in the form of spreadsheets so that you can get an entire list of restaurants available around me having its address as well as ratings in the easy spreadsheet! Here at Foodspark, we use Python 3 scripts to  scrape restaurant and food data  as installing Python could be very useful. For proofreading the script, we use Google Colab for running the script as it helps us running the Python scripts on the cloud. As our objective is to find a complete listing of places, scraping Google Maps data is its answer. Using Google Maps scraping, we can extract a place’s name, coordinates, address, kind of place, ratings, phone number, as well as other important data. For a start, we can