1. A method, comprising: accessing, by a computer system, input data comprising: training data comprising a first plurality of data records, each of the first plurality of data records having numeric data values at least for first and second variables in a plurality of variables; and a second plurality of data records comprising at least a first not fully specified data record that does not include a first numeric data value for the first variable in the plurality of variables; generating, by the computer system and using the training data, a representation of a relationship between the first variable and at least the second variable in the plurality of variables; and obtaining, by the computer system and using the generated representation, at least the first numeric data value for the first variable in the first not fully specified data record.
1. A method, comprising: accessing, by a computer system, input data comprising: training data comprising a first plurality of data records, each of the first plurality of data records having numeric data values at least for first and second variables in a plurality of variables; and a second plurality of data records comprising at least a first not fully specified data record that does not include a first numeric data value for the first variable in the plurality of variables; generating, by the computer system and using the training data, a representation of a relationship between the first variable and at least the second variable in the plurality of variables; and obtaining, by the computer system and using the generated representation, at least the first numeric data value for the first variable in the first not fully specified data record. 5. The method of claim 1 , wherein accessing the input data is performed using a spreadsheet user interface, and wherein generating the representation of the relationship comprises: generating one or more candidate query patterns that relate a blank cell in the spreadsheet user interface to its surrounding non-blank cells in the spreadsheet user interface; for each pattern in the one or more candidate query patterns: collecting data from the spreadsheet user interface that matches the pattern, generating, using a machine learning technique and the collected data, a respective candidate mathematical model representing a relationship between a variable associated with the blank cell and one or more variables associated with the surrounding non-blank cells; determining a confidence associated with the generated candidate mathematical model; and selecting the generated candidate mathematical model having a highest confidence level as the representation of the relationship.