You work on a machine learning team at an online reseller of consumer products. You are performing feature engineering of your product data where you have a large, multi-column dataset with one column missing 40% of its data. Your team lead thinks that you can use some of the columns in the dataset to create the missing data.
Which feature engineering is the best approach to create approximate replacements for the missing data while also preserving the integrity of the dataset?

Answer options:

A.Binning
B.Yeo-Johnson transformation
C.Multivariate imputation
D.Mean imputation

Answer: C
Option A is incorrect. Binning is used for grouping values. It is used to minimize the impact of observation errors. Binning would not help you create approximate replacements for missing values.
Option B is incorrect. Yeo-Johnson transformation is used to give your data a more Gaussian distribution. It is not used to create approximate replacements for the missing data.
Option C is CORRECT.With multivariate imputation, you use other variables in the data set to predict missing values.
Option D is incorrect. Mean imputation replaces the missing values with the mean of observed values of that variable. This approach is the most simplistic method of the imputation of missing values. The multivariate imputation method is much more accurate.
Reference:
Please see the article titled Binning in Data Mining.
Please refer to the Machine Learning Mastery article titled How to Use Power Transforms for Machine Learning.
Please see the article titled Multiple Imputation in a Nutshell.