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cosine similarity - A measurement that quantifies the similarity between two or more vectors. - It is the cosine of the angle between vectors. - The cosine similarity is described mathematically as the division between the dot product of vectors and the product of the euclidean norms or magnitude of each vector. - Reference : towardsdatascience.com/understanding-cosine-similarity-and-its-applica..

Euclidean distance - It is the length of a line segment between the two points. - The distance between two objects that are not points is usually defined to be the smallest distance among pairs of points from the two objects. - Smaller, Closer. In three dimensions, for points given by their Cartesian coordinates, the distance is Reference : en.wikipedia.org/wiki/Euclidean_distance def distance(x..

Sørensen similarity - One of Token-based algorithms which is the expected input is a set of tokens, rather than complete strings. The idea is to find the similar tokens in both sets. More the number of common tokens, more is the similarity between the sets. A string can be transformed into sets by splitting using a delimiter. This way, we can transform a sentence into tokens of words or n-grams ..

Jaccard Similarity - One of Token-based algorithms which is the expected input is a set of tokens, rather than complete strings. The idea is to find the similar tokens in both sets. More the number of common tokens, more is the similarity between the sets. A string can be transformed into sets by splitting using a delimiter. This way, we can transform a sentence into tokens of words or n-grams c..

Jaro-Winkler distance - One of edit distance based algorithms which is falling under this category try to compute the number of operations needed to transforms one string to another. More the number of operations, less is the similarity between the two strings. One point to note, in this case, every index character of the string is given equal importance. - It uses a prefix scale p which gives m..