2. A system comprising: at least one processor configured to perform: accessing input data comprising: training data comprising a first plurality of data records, each of the first plurality of data records having a numeric data value for each of a plurality of variables; and a second plurality of data records, each of the second plurality of data records having a numeric data value for at least one of the plurality of variables; identifying at least a first not fully specified data record within the second plurality of data records that does not include at least a first numeric data value for at least a first variable of the plurality of variables; determining, automatically and based on the training data, a relationship between the first variable and at least one other variable in the plurality of variables, wherein determining the relationship comprises: generating, using a machine learning technique and the first plurality of records in the training data, a mathematical model representing the relationship between the first variable and the at least one other variable; and obtaining, using the mathematical model representing the relationship between the first variable and the at least one other variable, at least the first numeric data value for the first variable in the first not fully specified data record.
2. A system comprising: at least one processor configured to perform: accessing input data comprising: training data comprising a first plurality of data records, each of the first plurality of data records having a numeric data value for each of a plurality of variables; and a second plurality of data records, each of the second plurality of data records having a numeric data value for at least one of the plurality of variables; identifying at least a first not fully specified data record within the second plurality of data records that does not include at least a first numeric data value for at least a first variable of the plurality of variables; determining, automatically and based on the training data, a relationship between the first variable and at least one other variable in the plurality of variables, wherein determining the relationship comprises: generating, using a machine learning technique and the first plurality of records in the training data, a mathematical model representing the relationship between the first variable and the at least one other variable; and obtaining, using the mathematical model representing the relationship between the first variable and the at least one other variable, at least the first numeric data value for the first variable in the first not fully specified data record. 17. The system according to claim 2 , wherein accessing the input data is performed using a spreadsheet user interface, and wherein generating the mathematical model includes: 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 the 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 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 mathematical model.