Handling missing data
Addressing missing data involves making careful decisions to minimize its impact on analyses and models. The most common strategies include the following:
- Removing records with missing values
- Filling in missing values using various techniques such as mean, median, mode imputation, or more advanced methods such as regression-based imputation or k-nearest neighbors imputation
- Introducing binary indicator variables to flag missing data; this can inform models about the presence of missing values
- Leveraging subject matter expertise to understand the reasons for missing data and make informed decisions about how to handle it
Let’s deep dive into each of these methods and observe in detail the results on the dataset presented in the previous part.
Deletion of missing data
One approach to handling missing data is to simply remove records (rows) that contain missing values. It is a quick and simple strategy, and is generally more...
