What approach would you use to estimate missing values in a dataset?

Imputation is the process used to estimate and replace missing values in a dataset. It is a crucial step in data preprocessing as missing data can significantly affect the outcome of data analysis and modeling. The approach involves using statistical techniques to infer the missing values based on the available information in the dataset. Common imputation methods include mean, median, mode replacement, or more complex algorithms like k-nearest neighbors or regression models.

This technique ensures that the dataset remains complete and enables robust analysis without losing valuable information that could occur if entire rows or columns with missing values were discarded. Using imputation helps in maintaining the integrity of the dataset and leads to more accurate predictive models.

In contrast, interpolation estimates missing values based on existing values in a continuous dataset, often employed in time series analysis. Smoothing refers to techniques used to reduce noise in data, while normalization adjusts the scale of data, usually for analysis purposes. Although these approaches can be relevant in certain contexts, they do not specifically address the problem of missing values as effectively as imputation does.