Abstract: An Artificial Intelligence (AI)-based attribute prediction system generates predictions for attributes of highly customized equipment in response to received user requests. Processed historical data is initially used to generate feature combinations which are then employed along with a plurality of statistical and machine learning (ML) models in order to identify a best scoring model-feature combination in two selection cycles using multiple selection criteria. The predictions for an attribute are generated by the best scoring model and feature combination. Various insights regarding the features affecting the attribute can be additionally derived to provide recommendations to the user.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support functionality for identifying, quantifying, and mitigating risks. A dataset that includes a plurality of data items associated with one or more entities is generated and a set of rules is executed against the dataset to determine a sentiment for each data item of the plurality of data items. The dataset may be evaluated against a machine learning model configured to produce a set of risk categorizations that associate each of the plurality of data items with a risk category. Scoring metrics are generated for each of the data items based at least in part on the evaluating. The scoring metrics may account for an impact of dependencies and capabilities of the one or more entities on risks corresponding to the risk categorizations.

Abstract: An Artificial Intelligence (AI)-based attribute prediction system generates predictions for attributes of highly customized equipment in response to received user requests. Processed historical data is initially used to generate feature combinations which are then employed along with a plurality of statistical and machine learning (ML) models in order to identify a best scoring model-feature combination in two selection cycles using multiple selection criteria. The predictions for an attribute are generated by the best scoring model and feature combination. Various insights regarding the features affecting the attribute can be additionally derived to provide recommendations to the user.