Abstract: A system and a method are disclosed for a platform that determines the classification of a physical asset received from a provider. For a physical asset, the platform extracts intrinsic data from the asset and compiles extrinsic data by web scraping data associated with the asset. The platform generates a principal component vector comprising the intrinsic data and extrinsic data and inputs the principal component vector into a machine learning model. The machine-learning model is trained using training data that includes an aggregation of principal components of a physical asset as labeled by a historical classification and a time associated with the entry. The machine-learning model outputs the classification of the physical asset corresponding to a present time. The platform provides the classification to a user in visual association with a representation of the physical asset.

Abstract: A system and a method are disclosed for a platform that determines the classification of a physical asset received from a provider. For a physical asset, the platform extracts intrinsic data from the asset and compiles extrinsic data by web scraping data associated with the asset. The platform generates a principal component vector comprising the intrinsic data and extrinsic data and inputs the principal component vector into a machine learning model. The machine-learning model is trained using training data that includes an aggregation of principal components of a physical asset as labeled by a historical classification and a time associated with the entry. The machine-learning model outputs the classification of the physical asset corresponding to a present time. The platform provides the classification to a user in visual association with a representation of the physical asset.

Abstract: A system and a method are disclosed for a platform that determines the classification of a physical asset received from a provider. For a physical asset, the platform extracts intrinsic data from the asset and compiles extrinsic data by web scraping data associated with the asset. The platform generates a principal component vector comprising the intrinsic data and extrinsic data and inputs the principal component vector into a machine learning model. The machine-learning model is trained using training data that includes an aggregation of principal components of a physical asset as labeled by a historical classification and a time associated with the entry. The machine-learning model outputs the classification of the physical asset corresponding to a present time. The platform provides the classification to a user in visual association with a representation of the physical asset.