Foodspark - Food Data & insights

  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

Yelp is a localized search engine for companies in your area. People talk about their experiences with that company in the form of reviews, which is a great source of information. Customer input can assist in identifying and prioritizing advantages and problems for future business development. We now have access to a variety of sources thanks to the internet, where people are prepared to share their experiences with various companies and services. We may exploit this opportunity to gather some useful data and generate some actionable intelligence to provide the best possible client experience. We can collect a substantial portion of primary and secondary data sources, analyze them, and suggest areas for improvement by scraping all of those evaluations. Python has packages that make these activities very simple. We can choose the requests library for  web scraping  since it performs the job and is very easy to use By analyzing the website via a web browser, we may quickly understand the