Foodspark - Food Data & insights

This is the third in a series of articles that uses BeautifulSoup to  scrape Yelp restaurant reviews  and then apply Machine Learning to extract insights from the data. In this article, you will use the code to extract all the reviews in a list. The script will be as follows: import requests from bs4 import BeautifulSoup import time from textblob import TextBlob import pandas as pd#we use these argument to scrape the website rest_dict = [ { "name" : "the-cortez-raleigh", "link" : "https://www.yelp.com/biz/the-cortez-raleigh?osq=Restaurants&start=", "pages" : 3 }, { "name" : "rosewater-kitchen-and-bar-raleigh", "link" : "https://www.yelp.com/biz/rosewater-kitchen-and-bar-raleigh?osq=Restaurants&start=", "pages" : 3 } ]#scraping function def scrape

  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

  This blog will explain the working of the algorithm using  web scraping services  and what kind of steps will be required to build a structured algorithm. The following steps are frequently required when creating a sophisticated algorithm: You will start with a basic algorithm that solves a little problem. Need to scale it up such that it may be used to solve several instances of the same problem. The algorithm is made more complex by adding layers of complexity. After these processes are finished, you can gradually add more features, like Machine Learning, exploratory data analysis or insight extraction, and visualization. The Basic Algorithm This is the code used to  extract data from Yelp  page and give you an idea of what algorithm is used. import requests from bs4 import BeautifulSoup import timecomment_list = list() for pag in range(1, 29): time.sleep(5) URL = "https://www.yelp.com/biz/the-cortez-raleigh?osq=Restaurants&start="+str(pag*10)+"&sort_by=rat

  TripAdvisor reviews provide a wealth of information on airline and hotel costs that might help you grow your business. It also contains a wealth of information about major travel locations, hotels, and restaurants. You can use web scraping to automatically collect information from TripAdvisor reviews if you want to extract and use all of this information. Web scraping is the act of employing automated bots to collect data from a website's HTML version and delivering it in Excel or CSV format so you can process, analyze, and utilize it. Data scraping  reviews from TripAdvisor is the most effective data collection approach currently accessible, and it will considerably improve your capacity to synthesis, organize, and analyze existing patterns in the hospitality business. Why TripAdvisor Reviews are Necessary? How many TripAdvisor reviews are there? TripAdvisor has nearly 884 million reviews on hotels, lodgings, and other services as of 2020. As a result, TripAdvisor evaluations ma