Automated synthetic augmentation of machine learning model training datasets and input variables to improve accuracy

A system comprising:

• memory hardware configured to store instructions and
• processing hardware configured to execute the instructions, wherein 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;
• generating input variables by:
• assigning alphanumeric strings to elements of a raw data set,
• tokenizing each alphanumeric 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, and
• saving the filtered scalar values as input variables;
• and
• providing the input variables to the trained machine learning model to generate output variables.