Which of the following is the most appropriate method to handle missing data in a dataset for predictive modeling?

Explanation: Replacing missing data with statistical measures like mean (for continuous data), median (for skewed distributions), or mode (for categorical data) is a robust imputation technique. This approach minimizes the loss of data while maintaining the dataset's integrity. It is particularly effective when missing values are random (MCAR) and do not introduce significant bias. However, this method may not work well for datasets with a high proportion of missing values or when patterns in the missing data need to be preserved. Advanced imputation methods like k-Nearest Neighbors (KNN) or predictive models can be used in such cases. Option A: Deleting rows with missing values can result in significant data loss, reducing the dataset's representativeness. Option C: Ignoring missing data leads to inaccuracies and potential errors in analysis. Option D: Filling with arbitrary constants like zero can distort the dataset, introducing bias. Option E: Duplicating rows compromises the dataset's integrity and can lead to overfitting in predictive models.