What strategy should be used to handle missing values in a dataset before proceeding with analysis?

Using a predictive model to estimate missing values is a robust strategy for handling missing data, particularly because it leverages the relationships within the dataset to provide more accurate imputation than simpler methods. This approach involves building a model based on the available data to predict the missing values accurately. By doing this, the integrity and underlying patterns of the dataset can be preserved, which is crucial for subsequent analyses.

In contrast to other methods, this strategy enhances the dataset's reliability by accounting for the potential correlations and trends in data that might be lost if simpler imputation methods like mean imputation were used, or if rows were simply deleted. Predictive modeling can lead to better outcomes, especially in datasets where the missingness might carry useful information or where certain characteristics are closely tied together.

While other methods, such as using the mean to impute or deleting rows with missing data, may seem straightforward, they can introduce bias or unnecessarily reduce the size of your dataset, ultimately skewing analysis results. The predictive model strategy is thus a more comprehensive and effective handling of missing data.