Foodspark - Food Data & insights

By the conclusion of 2021, the online delivery service market is expected to be worth over $127 billion. By 2025, the market is estimated to rise to $192 billion in sales. Thousands of restaurants are listed on these platforms and applications, and millions of people employ them. As a result, food delivery data is crucial for businesses. Big data and analytics are being used by restaurants and food franchises to better understand consumer tastes and preferences. Companies can now gather data from meal delivery companies using  web scraping services  to modify rates, boost marketing techniques, and more. Web scraping is a tool that will help you get closer to your objectives if you're seeking to develop your restaurant or food delivery service. Why Scrape Food Delivery Data? The technique of extracting large volumes of data from a targeted website or app is known as web scraping. Food delivery companies will need to act swiftly to maximize their information as the competition among

  Grocery buying online has become a critical component of household efficiency. People do not have   time to visit a supermarket and sample new products. They don’t have time to carry bags back and forth from the store to their car and kitchen! The demand for online grocery stores will expand faster due to rising consumer demand. Nearly 10% of all grocery sales are predicted to be done online by 2022. If we examine the case’s value, we’re looking at a massive $133.8 billion! This figure has the potential to disrupt all grocery and food stakeholders since it alters the retail and distribution dynamics. Amazon Pantry, BigBasket, Buy Me A Pie, Cellfire, DMart Online Grocery Shopping, Favado, Flipkart Supermarket, Grocery iQ, Grocery Pal, Grofers, Kroger, Nature’s Basket, Our Groceries Shopping List, Out of Milk, Reliance Smart, Spencer’s Online Grocery, Target, Walmart, and ZopNow are among the major players in the online grocery market. We at Foodspark offer the best grocery app scrapin

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