Abstract: Methods and apparatuses are described for predictive analysis of transaction data using machine learning. A server computing device trains a plurality of machine learning models using historical transaction data for a set of entities as input to predict a likelihood of future transaction activity for each of the entities, each machine learning model trained on a different target transaction variable. The server computing device executes each of the plurality of machine learning models to generate, for each entity, a predicted likelihood value for a future transaction associated with the entity and each of the target transaction variables. The server computing device transmits the predicted likelihood values for each entity to a remote computing device for display.

Abstract: Methods and apparatuses are described for intelligent imputation of missing data in a machine learning (ML) dataset comprised of a plurality of features. Each feature includes a plurality of values, where at least a portion of the values for one or more features are missing. A server analyzes the ML dataset to generate characteristics of the missing values in the ML dataset. The server selects an imputation algorithm for filling in the missing values based upon the identified characteristics. The server determines a computing environment in which the imputation algorithm is executed based upon one or more of a size of the ML dataset or the selected algorithm. The server generates code that comprises instructions for executing the imputation algorithm on the ML dataset in the computing environment. The server integrates the code into an ML platform that executes the code to assign replacement values to the missing values.