Check similarity of table/csv of Product Names
Your problem is known as detection of near-duplicate documents, i.e. you have strings that are similar but not exact duplicate. The most common approaches are using cosine similarity and Jaccard similarity. You could check this page for more information 

First you have to convert your strings to a vector of features, this features could be tf-idf vectors of all the tokens (words) that appear in the database, could also be a vector of n-grams. For a discussion of semantic and syntactic features you can look here.

Finally you would have to check the similarity of every pair of documents, in your case 18000. The brute force approach is O(n^2), so this could be unfeasible. A common technique to deal with this issue is to use fingerprinting (hashing) together with Locality Sensitive Hashing (LSH). 

You can find an introduction and a general discussion of the whole topic in Chapter 3 of Mining Massive Datasets