What Is Fuzzy Lookup In Excel

 Fuzzy text matching is the process of finding text strings that are similar, but not exactly identical to a given reference string.

It's often used when working with data that may contain errors or inconsistencies such as misspellings, typo error, or variations in formatting.

Using fuzzy text matching, you can identify and group together similar strings, which can help to clean up data to make it more useful.

Fuzzy text matching is a technique used to compare text strings that may have minor differences.

One common approach is to use a similarity score based on the number of edits (insertions, deletions, or substitutions) required to transform one string into another.

The similarity score ranges from 0 to 1, where 1 means the strings are identical, and lower scores indicate greater differences.

Let us understand through an example

Consider the following pairs of strings:

• Apple and Apples - The Levenshtein distance is 1, because adding an "s" to "apple" yields "apples".

• Banana and Bananas - The Levenshtein distance is also 1, because adding an "s" to "banana" yields  Bananas .

• Car and Card - The Levenshtein distance is 1, because replacing the final "r" in "car" with a "d" yields  Card .

• Dog and Cat - The Levenshtein distance is 3, because three edits are required to transform "dog" into "cat" (replace "d" with "c", delete "o", and replace "g" with "t").

There are many approaches on fuzzy lookup. One approach of fuzzy text matching is to use regular expressions, which allow you to search for patterns within text.

Regular expressions can be used to match approximate patterns by allowing for variations in the text, such as optional characters or spelling variations.

For example, the regular expression "Colou?r" would match both "color" and "Colour", because the "u" is optional.

Some examples of how fuzzy text matching can be useful:

1. Deduplication - When working with large datasets, it's common to encounter duplicates, which can skew your analysis and waste valuable storage space.

Fuzzy text matching can be used to identify and group together strings that are similar, but not necessarily identical, which can help you to identify duplicates more effectively.

2. Data cleaning -Fuzzy text matching can also be used to clean up messy or inconsistent data.

For example, you may have a dataset of product names and descriptions, and you want to standardize the formatting to make it more consistent.

Fuzzy text matching can be used to identify similar strings and suggest changes that can help to standardize the data.

3. Record linkage - In some cases, you may have data from multiple sources that you want to combine or match up.

For example, you may have a dataset of customer orders from an e-commerce website, and you want to match up each order with the corresponding customer from a separate dataset. Fuzzy text matching can be used to identify customers with similar names or addresses, which can help to link up the data.

4. Text mining - Fuzzy text matching can also be used in natural language processing and text mining applications.

For example, you may want to identify all the mentions of a particular keyword or phrase in

To be continued...