What predictive model to use to impute Gender? My data looks like this:



birth_date has 634,990 missing values
gender has 328,849 missing values

Both of these are a substantial amounts since I have 900k entries, so I can't discard empty rows. For birth_date someone recommended using Multivariate imputation by Chained equation (MICE). I don't know what predictive model I should use for gender. Of the non-missing data, there are 5x more males than females.

Can someone tell me what would be best practice here? What would be the best way to fill in the missing values for gender ?

I'm using the data to predict bike-ride duration and final destination (I know they're shown on the table above)
You can use one of the popular gradient boosting tree implementations such LightGBM and XGBoost as your predictive model. They can handle missing values during training and the results are often better than any imputation done in preprocessing.

The way they achieve this is by using the splitting of the decision trees they are built on. The missing values are ignored when finding the best split, then once it is found they are allocated to whichever side will minimize the loss function most.