1. A computer-implemented method for abstracting a dataset storing a multiplicity of data, comprising: determining, using one or more computer processors, a template including programming logic that, when executed, calculates one or more outputs based on one or more template inputs and one or more parameters, the one or more parameters being initially one or more undetermined parameters; inputting, using the one or more computer processors, the one or more undetermined parameters into a machine learning framework; retrieving, from a datastore storing a multiplicity of indexed data entries, a plurality of data entries reflecting a plurality of cases; inputting, using the one or more computer processors, a plurality of inputs into the machine learning framework, the plurality of inputs including the plurality of cases; determining, using the one or more computer processors and the machine learning framework, one or more values for the one or more undetermined parameters, respectively, the one or more values being values for the one or more undetermined parameters, respectively, and making the one or more undetermined parameters, one or more determined parameters; calculating, using the one or more computer processors, a sum of the plurality of cases; and generating, using the one or more computer processors, an approximation of the datastore using the one or more determined parameters and the sum of the plurality of cases.