Abstract: Finding accurate prediction objectives includes building, by a framework application, a data pool for each of a plurality of prediction objectives. A plurality of machine learning (ML) models is trained for each data pool, and each of the plurality of ML models is combined for each data pool. One or more accurate objectives are identified and selected on the basis of a performance of the combined plurality of ML models.

Abstract: A system to generate and maintain a database of service provider skills and rankings with various categories is disclosed. Skills and rankings are generated from a number of corpus texts as well as service provider content. The database is dynamically updated to reflect changes to the corpus texts and/or service provider content.

Abstract: A computer-implemented method for deduplicating records includes generating a block comprising of a group of records. The method also includes creating one or more record pairs from the block, and calculating one or more features based on one or more fields within the one or more record pairs. The method further includes inputting the one or more features into a machine language trained model to predict a probability score. The probability score indicates whether two records are duplicates. The method also includes storing the probability score as links between two vertices in a graph, and displaying one or more duplicate records by querying the graph.

Abstract: A computer-implemented method for deduplicating records includes generating a block comprising of a group of records. The method also includes creating one or more record pairs from the block, and calculating one or more features based on one or more fields within the one or more record pairs. The method further includes inputting the one or more features into a machine language trained model to predict a probability score. The probability score indicates whether two records are duplicates. The method also includes storing the probability score as links between two vertices in a graph, and displaying one or more duplicate records by querying the graph.

Abstract: The present invention provides a forecasting engine with the ability to minimize prediction error in a preferred direction. It comprises of a receiver configured to receive training data samples. In addition, the forecasting engine includes a building module configured to build a base learner model from the training data samples. In addition, the forecasting engine includes a custom error function that emphasizes prediction error along a pre-configured direction. In addition, the forecasting engine includes an error determination module configured to determine the prediction error made by the base learner model. In addition, the forecasting engine includes an error minimization module configured to construct a new model that has lesser prediction error than the base learner model, where prediction error is as defined by the custom error function.

Abstract: The present invention provides a forecasting engine with the ability to minimize prediction error in a preferred direction. It comprises of a receiver configured to receive training data samples. In addition, the forecasting engine includes a building module configured to build a base learner model from the training data samples. In addition, the forecasting engine includes a custom error function that emphasizes prediction error along a pre-configured direction. In addition, the forecasting engine includes an error determination module configured to determine the prediction error made by the base learner model. In addition, the forecasting engine includes an error minimization module configured to construct a new model that has lesser prediction error than the base learner model, where prediction error is as defined by the custom error function.