You receive a dataset with missing values in multiple columns. What is the most effective approach to handle these missing values?

Predictive modeling leverages machine learning algorithms to predict missing values based on the relationship between other features. It is highly accurate and preserves the dataset’s integrity, which is crucial for data-driven analysis. Advanced techniques, like k-Nearest Neighbors or regression imputation, ensure minimal bias and better prediction accuracy compared to simpler methods. Why Other Options Are Wrong : A) Deleting rows reduces the dataset size, leading to loss of valuable information. B) Replacing with column mean ignores data variability and may distort outcomes. C) Leaving missing values untreated can affect the performance of analytical models. E) Adding a new column may highlight missing data but does not address the underlying issue.