Let’s talk about how we can make sure our analyses and models are accurate and reliable by addressing missing data. I’ll go over some different approaches for handling missing data in this part 1 and discuss which methods work best for a few types of variables.
- Age
When it comes to numerical variables like “Age”, there are a couple of ways to handle missing values. One option is to fill in the gaps with either the median or a predetermined value. Another approach is to use regression models to estimate the missing values based on other data that are available.
2. Latitude & Longitude
When it comes to mapping and spatial analysis, geospatial data is crucial. If we stumble upon latitude and longitude values that are missing, it might be useful to check out external geocoding services that can assist us in determining coordinates using other location information.
In my next blog, I’ll be posting about the remaining missing value variables.