Abstract: Systems, methods, and computer-readable storage media for aggregating the outputs of multiple machine learning models, then using the output of yet another machine learning model as a multiplier to obtain a final prediction. A system can receiving a plurality of data sets, each data set being associated with at least one data type, and train machine learning models, each model associated with one or more of the different data types. Upon execution, the multiple machine learning models can each produce a prediction which is aggregated together to form an aggregated prediction. The multiplier from the additional machine learning model can then be applied to the aggregated prediction, resulting in a final prediction.

Abstract: A collapsible container that includes a base, a first side panel hingedly attached to the base, a second side panel hingedly attached to the base, a first end panel hingedly attached to the base, with the first end panel including a door, and a second end panel hingedly attached to the base. The collapsible container can further include a top cover. The collapsible container is configured to be positioned in a collapsed position and an assembled position. In an assembled position, the collapsible container is configured to hold items during a domestic or international move.