Missing data refers to the absence of values for certain observations and is an unavoidable problem in most data sources. Scikit-learn does not support missing values as input, so we need to remove observations with missing data or transform them into permitted values. The act of replacing missing data with statistical estimates of missing values is called imputation. The goal of any imputation technique is to produce a complete dataset that can be used to train machine learning models. There are multiple imputation techniques we can apply to our data. The choice of imputation technique we use will depend on whether the data is missing at random, the number of missing values, and the machine learning model we intend to use. In this chapter, we will discuss several missing data imputation techniques.
This chapter will cover the following recipes:
- Removing observations with missing data
- Performing mean or median imputation
- Implementing mode or frequent category imputation
- Replacing missing values with an arbitrary number
- Capturing missing values in a bespoke category
- Replacing missing values with a value at the end of the distribution
- Implementing random sample imputation
- Adding a missing value indicator variable
- Performing multivariate imputation by chained equations
- Assembling an imputation pipeline with scikit-learn
- Assembling an imputation pipeline with Feature-engine
