Confirming accuracy of LW scraped location data

*Context* We're still working with the Living Wage dataset. In this spreadsheet there are 2759 Google Places locations for 371 companies. Column F, maps_link, will take you to the location on Google Maps. Column J, google_place_id, is the unique ID for the place. Column A, "company_natural_key" is the company name. And column B, company_website_url, is a link to the company website. This data has been scraped from Google Places API, so it contains lots of false positives (look at row 93: the place_name "Lifeboat" is not a Brewdog pub) *Task* The task is to set column E to either INCLUDE or EXCLUDE. If the place is a valid location for the company, set column E to INCLUDE. If it's an invalid location for the company, set it to EXCLUDE. Otherwise, leave it as UNSPECIFIED I've run some code to auto-set the value. So 308 are already set to INCLUDE, 15 are already set to EXCLUDE. Which unfortunately leaves 2436 UNSPECIFIED. But 2194 of the UNSPECIFIED rows are for the 8 big national brands, particularly COOK (which gives an awful set of results because it's such a common word). To help decide whether a place is valid or not, you could refer to the google_classification column, although it's not always helpful. But you should be able to knock out a lot that way, especially with COOK and the other national brands. Your ultimate source of truth is always to be to go to the company website, find their store locator, and confirm the locations manually. If you find that the spreadsheet is missing places, add a note in the comments column. *National Companies* Some notes on the big national companies... In general, these don't have to be perfect. There is subjectivity anyway (e.g. Ikea "collection points" vs Ikea stores). A few too many or a few too few is not a problem. Majestic Wine - Wine store - We expect "200+" locations - Store locator: [login to view URL] - Search seems to be pretty good - only a few false positives Brewdog - Pub - We expect ~49 locations - Store locator: [login to view URL] - Search seems to work fairly well for these. Modern company, good with their online presence :) Burberry - Fashion store - We expect ~23 locations - Store locator: [login to view URL] - I don't think the Burberry "outlets" are official stores, so remove these. COOK - This is a shop that sells high quality ready meals - We expect ~110 locations - Store locator: [login to view URL] - This search is terrible and brings back loads. Hard to tell which are valid and which aren't. Will take some effort, I think :( Lush - Cosmetics store - We expect ~106 locations - Store locator: [login to view URL] - We are getting more like 300 back, so a lot of false positives here Curzon - Cinema - We expect ~13 locations - Store locator: [login to view URL] - They have an "other" category on their cinema list, which come back in the search. I think we should leave these in. Ikea - We expect ~27 locations - Store locator: [login to view URL] - Loads of rubbish comes back here, because the stores are so big (e.g. bus tops, and restaurants) Richer Sounds - Electronics store (mainly audio/video I think) - We expect ~53 locations - Store locator: [login to view URL] - Looks like search worked ok *Other points* There should be only UK locations in the dataset (England, Scotland, Wales, Northern Ireland; Not Republic of Ireland). If you find any outside of the UK, EXCLUDE them We do not want the companies' head offices. We only want stores/shops/branches where customers interact directly with the business