Error detection and correction systems for data processing implemented via artificial intelligence

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,
• saving the filtered scalar values as input variables, and
• providing the input variables to the trained machine learning model to generate output variables,
• wherein a subsequent set of input variables is generated subsequent to the input variables;
• wherein the input variables and the subsequent set of input variables include episode identifiers;
• wherein the input variables are archived to a database;
• and
• wherein the subsequent set of input variables are archived to the database in response to the episode identifiers of the subsequent set of input variables matching the episode identifiers of the input variables.