You work for a financial services company where you are building a model to analyze equity futures prices to predict price movement for your firm’s hedging strategy. You receive several data feeds, some of which contain missing values for some data points. The missing data points in your data feeds are of the categorical type, such as the expiration month or the exchange on which the futures contract is traded. Which strategy should you employ to deal with the missing data point values while attempting to maximize the accuracy of your model without introducing bias into the model?

Answer options:

A.Remove the observations that have the missing data.
B.Impute the missing values using the Mean/Median strategy.
C.Impute the missing values using the Most Frequent strategy.
D.Impute the missing values using a Deep Learning strategy.

Answer: D
Option A is incorrect because this approach will lead to the loss of data points with potentially useful information.
Option B is incorrect because, by definition, it can only be used with numeric data. It is not advisable to use the Mean/Median approach with categorical data points.
Option C is incorrect because while working with categorical data, this method can introduce bias into your data.
Option D is correct. Using a library such as the datawig python library, a deep learning approach uses deep neural networks to impute missing data values. This is the most accurate strategy, in the list of given options, at imputing categorical values. (See the datawig documentation: https://github.com/awslabs/datawig)
Reference:
Please see the article 6 Different Ways to Compensate for Missing Values In a Dataset (Data Imputation with examples)