Abstract: Disclosed herein are system, method, and computer program product embodiments for classifying data objects using machine learning. In an embodiment, an artificial neural network may be trained to identify explained variable values corresponding to data object attributes. For example, the explained variables may be a category and a subcategory with the subcategory having a hierarchical relationship to the category. The artificial neural network may then receive a data record having one or more attribute values. The neural network may then identify a first and second explained variable value corresponding to the one or more attribute values based on the trained neural network model. The first and second explained variable values may then be associated with the data record. For example, if the data record is stored in a database, the record may be updated to include the first and second explained variable values.

Abstract: Disclosed herein are system, method, and computer program product embodiments for classifying a new record. An embodiment operates by receiving a dataset unique to a user, wherein the dataset includes a plurality of records separate from the new record, and receiving a dataset schema. Thereafter, the dataset is validated based on the dataset schema. Subsequently, a request for creating a machine learning model based on a selected model template and dataset is received. After creating the custom machine learning model, a request for classifying the new record based on the created machine learning model is received. Upon determining the classification of the new record based on the custom machine learning model, the classification for the new record is outputted to the user.

Abstract: Disclosed herein are system, method, and computer program product embodiments for classifying data objects using machine learning. In an embodiment, an artificial neural network may be trained to identify explained variable values corresponding to data object attributes. For example, the explained variables may be a category and a subcategory with the subcategory having a hierarchical relationship to the category. The artificial neural network may then receive a data record having one or more attribute values. The neural network may then identify a first and second explained variable value corresponding to the one or more attribute values based on the trained neural network model. The first and second explained variable values may then be associated with the data record. For example, if the data record is stored in a database, the record may be updated to include the first and second explained variable values.