Apparatuses, systems, methods, and computer program products are disclosed for data quality detection and compensation for machine learning. A quality analysis module electronically identifies one or more data quality issues in machine learning training data. A corrective action module modifies training data by performing one or more corrective actions in response to one or more data quality issues. A predictive analytics module creates a machine learning model that includes one or more learned functions based on modified training data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An apparatus comprising: a quality analysis module that electronically identifies one or more data quality issues in machine learning training data; a corrective action module that modifies the training data by performing one or more corrective actions in response to the one or more data quality issues; and a predictive analytics module that creates a machine learning model comprising one or more learned functions based on the modified training data. 2. The apparatus of claim 1, wherein the corrective action module replicates the one or more corrective actions to modify workload data using the one or more corrective actions, and the predictive analytics module applies the machine learning model to the modified workload data to generate a prediction. 3. The apparatus of claim 2, wherein the corrective action module applies one or more different corrective actions to the workload data in response to user input, and the predictive analytics module updates the machine learning model based on the one or more different corrective actions. 4. The apparatus of claim 1, further comprising a model-readiness module that provides one or more model-readiness scores to a user based on the one or more data quality issues, a model-readiness score comprising one or more of a score for the training data, a score for a feature of the training data, a score for a dependent variable, and a score for a potential data quality issue. 5. The apparatus of claim 1, wherein the corrective action module automatically performs the one or more corrective actions, notifies a user of the one or more corrective actions, and provides an interface for the user to reverse the one or more corrective actions. 6. The apparatus of claim 1, wherein the corrective action module determines the one or more corrective actions based on a quality level selected by a user. 7. The apparatus of claim 1, further comprising a graphical user interface (GUI) module that interactively presents the one or more data quality issues and one or more potential corrective actions to a user, wherein the one or more corrective actions are selected by the user from the one or more potential corrective actions. 8. The apparatus of claim 7, wherein the GUI module presents a subset of the one or more potential corrective actions as default corrective actions. 9. The apparatus of claim 8, wherein the GUI module presents an interface allowing the user to accept the default corrective actions as a set. 10. The apparatus of claim 1, wherein the predictive analytics module creates the machine learning model using a model algorithm type based on the one or more data quality issues. 11. The apparatus of claim 1, wherein the corrective action module selects the one or more corrective actions based on a model algorithm type used by the predictive analytics module to create the machine learning model. 12. The apparatus of claim 1, wherein the one or more corrective actions comprise one or more of: excluding a feature from the training data, excluding an observation from the training data, excluding one or more values from the training data, replacing one or more values in the training data, and adding one or more engineered features to the training data. 13. The apparatus of claim 1, wherein the one or more data quality issues comprise one or more of: a unique id feature, a date feature, a categorical feature for which a cardinality violates a threshold, a feature with missing values, and a feature with out-of-range values. 14. A computer program product comprising a computer readable storage medium storing computer usable program code executable to perform operations, the operations comprising: electronically identifying one or more data quality issues in machine learning training data; modifying the training data by performing one or more corrective actions in response to the one or more data quality issues; and creating a machine learning model comprising one or more learned functions based on the modified training data. 15. The computer program product of claim 14, the operations further comprising providing one or more model-readiness scores to a user based on the one or more data quality issues, a model-readiness score comprising one or more of a score for the training data, a score for a feature of the training data, a score for a dependent variable, and a score for a potential data quality issue. 16. The computer program product of claim 14, wherein the one or more corrective actions are automatically performed, the operations further comprising notifying a user of the one or more corrective actions, and providing an interface for the user to reverse the one or more corrective actions. 17. The computer program product of claim 14, wherein the one or more corrective actions are based on a quality level selected by a user. 18. The computer program product of claim 14, the operations further comprising interactively presenting the one or more data quality issues and one or more potential corrective actions to a user, wherein the one or more corrective actions are selected by the user from the one or more potential corrective actions. 19. A method comprising: electronically identifying one or more data quality issues in machine learning training data; modifying the training data by performing one or more corrective actions in response to the one or more data quality issues; and creating a machine learning model comprising one or more learned functions based on the modified training data. 20. The method of claim 19, further comprising: replicating the one or more corrective actions to modify workload data using the one or more corrective actions; and applying the machine learning model to the modified workload data to generate a prediction.