Abstract: A computer-implemented method includes loading a training data set including a first bin and a second bin.

Abstract: A system includes memory hardware configured to store instructions, and processing hardware configured to execute the instructions. The instructions include training a machine learning model by loading a training data set, the training data set including a first bin and a second bin, applying an under-sampling technique to elements of the first bin to generate an updated first bin, applying an over-sampling technique to elements of the second bin to generate an updated second bin, generating an updated training data set by merging the updated first bin and the updated second bin, and training the machine learning model with the updated training data set, and providing the input variables to the trained machine learning model to generate output variables.

Abstract: A system includes memory hardware configured to store and processor hardware configured to execute instructions.

Abstract: A non-transitory computer-readable medium includes executable instructions including loading a training data set including a first bin and a second bin, applying an under-sampling technique to elements of the first bin to generate an updated first bin, applying an over-sampling technique to elements of the second bin to generate an updated second bin, generating an updated training data set by merging the updated first bin and the updated second bin, loading baseline hyperparameters, configuring a machine learning model with the baseline hyperparameters, providing the updated training data set to the machine learning model to determine baseline performance metrics, determining whether the baseline performance metrics are above a threshold, saving the baseline hyperparameters as optimal hyperparameters in response to determining that the baseline performance metrics are above the threshold, configuring the machine learning model with optimal hyperparameters, and providing input variables to the machine learnin

Abstract: A computer-implemented method includes training a machine learning model by loading a training data set, applying an under-sampling technique to elements of a first bin, applying an over-sampling technique to elements of the second bin, generating an updated training data set by merging the updated first bin and the updated second bin, and training the machine learning model with the updated training data set. Input variables are generated by assigning alphanumeric strings to elements of a raw data set, tokenizing each string, converting the tokenized strings to scalar values, performing frequency filtering to emphasize scalar values based on a frequency the scalar values appear in a set of data objects while de-emphasizing scalar values based on a frequency the scalar values appear in a group of sets of data objects. The filtered scalar values are provided as input variables to the trained machine learning model to generate output variables.