HEIRARCHICAL PREDICTION MODELS FOR UNSTRUCTURED TRANSACTION DATA

Systems and techniques are described for improving the evaluation of unstructured transaction data to, for example, recognize reoccurring data patterns or patterns of interest, predict future outcomes using historical indicators, identify attributes of interest, or evaluate likelihoods of certain conditions occurring. For example, a system can transform unstructured public record data obtained from multiple independent public data sources according to a hierarchical data model. The hierarchical data model can specify nodes within different data layers of a data hierarchy and classification labels corresponding to each of the nodes. In this way, the system can utilize data transformation techniques to permit the processing of information within unstructured transaction data that would have otherwise been impossible to perform without initially structuring the data according to the hierarchical data model.