A method of identifying location data in a data set comprises generating a data sample from the data set, training a plurality of models with the data sample to identify the location data in the data set, and applying the data set to the trained models to determine the location data within the data set. The plurality of models includes one or more first models to identify primary attributes of the location data indicating a geographical area and one or more second models to identify secondary attributes of the location data used to determine corresponding primary attributes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-8. (canceled) 9. A system for determining location data in a data set comprising at least one processor configured to: generate a data sample from the data set; train a plurality of models with the data sample to identify the location data in the data set, wherein the plurality of models includes one or more first models to identify primary attributes of the location data indicating a geographical area and one or more second models to identify secondary attributes of the location data used to determine corresponding primary attributes; and apply the data set to the trained models to determine the location data within the data set. 10. The system of claim 9, wherein the data set includes columns of data and one or more headers for the columns, and wherein the processor is configured to generate a data sample by: determining for insertion into the data sample a column header and one or more distinct frequently used values for each corresponding column. 11. The system of claim 9, wherein the processor is configured to train the plurality of models by: training the one or more first models with the data sample to identify the primary attributes; and training the one or more second models with the data sample and identified primary attributes from the one or more first models to determine the secondary attributes. 12. The system of claim 9, wherein the processor is further configured to: test the plurality of models with the data sample to produce location data; and re-train the plurality of models based on information received from a user pertaining to the location data produced by the plurality of models. 13. The system of claim 9, wherein the data set includes columns of data and one or more headers for the columns, and the one or more first models each identify a corresponding primary attribute and are configured to: extract header features and value features of the corresponding primary attribute from the data sample; and utilize the header features and value features to identify columns containing the corresponding primary attribute. 14. The system of claim 13, wherein the processor is configured to extract the header features by: identifying aliases within the data sample used to represent a name of the corresponding primary attribute; categorizing the identified aliases into categories each representing a corresponding header feature; and creating a header feature set based on the categories and utilize the header feature set to identify the header of columns of the corresponding primary attribute. 15. A computer program product for determining columns containing location data in a data set, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer processor to cause the computer processor to: generate a data sample from the data set; train a plurality of models with the data sample to identify the location data in the data set, wherein the plurality of models includes one or more first models to identify primary attributes of the location data indicating a geographical area and one or more second models to identify secondary attributes of the location data used to determine corresponding primary attributes; and apply the data set to the trained models to determine the location data within the data set. 16. The computer program product of claim 15, wherein the data set includes columns of data and one or more headers for the columns, and wherein the processor is configured to generate a data sample by: determining for insertion into the data sample a column header and one or more distinct frequently used values for each corresponding column. 17. The computer program product of claim 15, wherein the processor is configured to train the plurality of models by: training the one or more first models with the data sample to identify the primary attributes; and training the one or more second models with the data sample and identified primary attributes from the one or more first models to determine the secondary attributes. 18. The computer program product of claim 15, wherein the processor is further configured to: test the plurality of models with the data sample to produce location data; and re-train the plurality of models based on information received from a user pertaining to the location data produced by the plurality of models. 19. The computer program product of claim 15, wherein the data set includes columns of data and one or more headers for the columns, and the one or more first models each identify a corresponding primary attribute and are configured to: extract header features and value features of the corresponding primary attribute from the data sample; and utilize the header features and value features to identify columns containing the corresponding primary attribute. 20. The computer program product of claim 19, wherein the processor is configured to extract the header features by: identifying aliases within the data sample used to represent a name of the corresponding primary attribute; categorizing the identified aliases into categories each representing a corresponding header feature; and creating a header feature set based on the categories and utilize the header feature set to identify the header of columns of the corresponding primary attribute.