Abstract: A system receives and automatically transforms utility pipe attribute data and pipe break data. The missing and/or incorrect entries in the pipe attributes and/or break data is automatically identified and correct values for these entries are is automatically imputed to generate improved datasets of the pipe attribute data and break data. The improved data can be used to build a model with machine learning. Predictions of future likelihood of failure for pipe sections in a network of pipes can be made based on the model. A national database can be created that is filled with environmental data that has been transformed, optimized, merged, and imputed. The national database can be used for many customers to save computational costs. The national database can be used to build the failure prediction model for utility companies thereby saving computational costs.