Foodspark - Food Data & insights

  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

  DaaS and Big Data in Advertising The goal of traditional advertising was to reach out to your whole target demographic in the same way. However, with the introduction of the internet, this situation has altered (especially behavioral targeted ads). Even so, after a certain period, the Click-Through Rates (CTRs) plateaued. You may have seen an increase in highly targeted ads, including bothersome remarketing ads, in recent months. However, according to one study, this appears to have worked, as seen by a 62% rise in CTRs in 2013 compared to the previous year. Today, businesses have access to a lot of data in the form of comments, tweets, followers, clicks, likes, and other forms of social media interaction, all of which have significant untapped potential. When combined with macro-level information from ad agencies, this unstructured data can create excellent communication opportunities. Companies that use big data in advertising should ask themselves two questions: how can they analy