This ranges from 0 to 1, with 0 being the lowest (the least similar) and 1 being the highest (the most similar). Cosine similarity calculates a value known as the similarity by taking the cosine of the angle between two non-zero vectors. Hence this technique only works with vectors. It’s relatively straight forward to implement, and provides a simple solution for finding similar text.Īs with many natural language processing (NLP) techniques, this method calculates a numerical value. One such method is called the cosine similarity. Luckily, there’s already a lot of methods available for finding names/documents that look similar. It can be overwhelming trying to find textual errors and anomalies in a large dataset. It’s relatively straight forward to implement, and provides a simple solution for finding similar text.īecause of how we usually gather customer data, incorrect customer information is a very common issue. C osine similarity is one of such techniques. That being said, there’s already a lot of techniques available for finding names/documents that look similar. Gathering incorrect customer information is a very common issue.
E ven if you analyse the data correctly, you could draw wrong and misleading conclusions from the analysis due to poor data. As a result, multiple entries of the same customer could appear as two distinct customers especially if they’re a returning customer. They could be misclicks such as selecting the wrong address from a dropdown menu after entering a postcode. These errors could be textual ones such as typos when entering a name. somebody else will enter the information for them on their behalf.the customer has to enter their information themselves through a user interface, or.And this is especially true for customer data. With manually entered data, it’s only a matter of time before something goes wrong.
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