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Have you ever tried to match data in two tables? Isn’t it too difficult to match values in tables without identical data like the names with spelling mistakes or extra space between two words? The analytics team faces the biggest problem when they get the task of normalizing two sets of similar data with varied variables. They need to do this to create a common record for modeling. Most of the time, the reason behind errors in tables is the manual entry of values. For instance, the forms filled by users or the spreadsheets maintained by the employees have the highest chances of incorrect data. Small organizations with limited data might not understand the problem well, but large scale businesses with humongous data know how painful it is to match or merge multiple tables. Now the big question here how to solve this problem? So, the solution to this problem is Fuzzy Matching. It is a technique used in computer-assisted translation for record linking. The technique works on...