Systems and methods for predicting correct or missing data and data anomalies

A system for correcting data anomalies in a dataset, comprising:

• a memory configured to store non-transitory computer readable instructions;
• and
• a processor communicatively coupled to the memory, wherein the processor, when executing the non-transitory computer readable instructions, is configured to:
• receive a dataset;
• apply a first machine-learning algorithm to the dataset, wherein the first machine-learning algorithm identifies at least one relationship between a first data column and a second data column in the dataset;
• based on the identification of the at least one relationship between the first data column and the second data column in the dataset, generate an itemset;
• and
• apply a second machine-learning algorithm to the itemset, wherein the second machine-learning algorithm identifies at least one frequency value associated with the at least one relationship between the first data column and the second data column in the first dataset.

A computer-readable media storing non-transitory computer executable instructions that when executed cause a computing system to perform a method for predicting and correcting data anomalies comprising:

• receiving a first dataset;
• comparing a first data column in the first dataset to a plurality of other data columns in the first dataset;
• based on the comparison results of the first data column and the plurality of other data columns, identifying a first relationship between the first data column and a second data column in the first dataset;
• applying a first machine-learning algorithm to the first data column and the second data column, wherein the first machine-learning algorithm identifies a second relationship between the first data column and the second data column in the first dataset;
• based on the identification of the second relationship between the first data column and the second data column in the first dataset, generating a second dataset, wherein the second dataset is a subset of the first dataset;
• concatenating a plurality of column headers in the second dataset;
• generating an itemset based on the concatenation of the plurality of column headers in the second dataset;
• and
• applying a second machine-learning algorithm to the itemset, wherein the second machine-learning algorithm identifies at least one frequency value associated with the second relationship between the first data column and the second data column in the first dataset.