Which of the following processes helps improve data quality in predictive models?

Data cleaning is a crucial process that directly enhances the quality of data used in predictive models. This process involves identifying and correcting inaccuracies, inconsistencies, and missing values within the dataset. By ensuring that the data is accurate, complete, and formatted correctly, data cleaning significantly improves the reliability of the predictive analytics results. Clean data leads to better model performance because it helps in reducing noise and preventing misleading conclusions that could arise from unprocessed or flawed data.

For instance, if a dataset contains erroneous entries or duplicates, the predictive model may learn from these inaccuracies, which can severely impact its ability to make accurate predictions. Therefore, prioritizing data cleaning during the preprocessing phase helps build a stronger foundation for any analytical or predictive modeling efforts. This makes it an essential step in any workflow that aims to leverage data for predictive insights.