A naive approach would be to try to use the words themselves, but this wouldn’t work with misspellings or transpositions. So, if a match is found in the first line, it returns the match object.

In our case using words as terms wouldn’t help us much, as most company names only contain one or two words. def awesome_cossim_top(A, B, ntop, lower_bound=0): return csr_matrix((data,indices,indptr),shape=(M,N)), from sklearn.metrics.pairwise import cosine_similarity, clean_org_names = pd.read_excel('Gov Orgs ONS.xlsx'), org_name_clean = clean_org_names['Institutions'].unique(), print('Vecorizing the data - this could take a few minutes for large datasets...'), from sklearn.neighbors import NearestNeighbors, org_column = 'buyer' #column to match against in the messy data, unique_org = list(unique_org) #need to convert back to a list, https://www.linkedin.com/in/josh-taylor-24806975/, The Roadmap of Mathematics for Deep Learning, An Ultimate Cheat Sheet for Data Visualization in Pandas, How to Get Into Data Science Without a Degree, 5 YouTubers Data Scientists And ML Engineers Should Subscribe To, How to Teach Yourself Data Science in 2020, How To Build Your Own Chatbot Using Deep Learning. We’re open sourcing it. Databases often have multiple entries that relate to the same entity, for example a person Return the difference of two or more sets as a new set. As a practical example, consider “Sarah Smith” vs “Sarah Jessica Smith”. The obvious problem here is that the amount of calculations necessary grow quadratic. For example, if we treat words as tokens, then the first few trigrams (3-grams) of the license will be: 'this work ‘as-is’', 'work ‘as-is’ we', Search the index for items whose key exceeds the threshold

Note that for this method, the scores are given as distances, meaning lower numbers are better.

A set that supports searching for members by N-gram string similarity. This is a problem, and you want to de-duplicate these. The basic idea is if we have two strings, Using just names for de-duplication of people seems a bit incomplete because you really need to be sure that they are indeed the same entities in the world to be identified as duplicates. Even when both data sets have been “cleansed” mismatches may still happen because of formatting differences.

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