26. A computer system according to claim 24 wherein said computer system is programmed to utilize a sum of the merchant location level weights and the merchant group weights within a relevance engine to determine the relevance of each merchant location to each set of merchant locations; and output the set of merchant locations with the highest relevance as a prediction.
24. A computer system according to claim 23 wherein to compute a relevance for a single merchant location relative to a set of merchant locations within the transaction data, said computer system is programmed to: extract relevant features from a plurality of merchant locations grouped into sets to generate a document for each set; collect the generated documents within a dictionary; form a sparse matrix utilizing the dictionary whereby the relevance of each field value and tokenized field value in the generated documents is computed, utilizing the extracted relevant features based on at least one of a term frequency and an inverse document frequency; and join a matrix of merchant location level weights to a matrix of merchant group weights based on field types and field values within the sparse matrix.