Step 1: Naively impute missing data points of each variable (e.g., with mean value)
Step 2: Put NAs back in the age variable where it was missing.
Step 3: Train age on income and gender (linear regression) with available data
Step 4 Use the fitted model to predict the missing age values.
Step 5: Repeat Steps 2–4 separately for each variable that has missing data, namely income and gender.