There are a few ways to overcome challenges with missing data. One is to simply impute the missing values, either with the mean or median of the column or using a more sophisticated technique like k-nearest neighbors. Another is to use a technique like decision trees, which can handle missing values without imputation. Finally, you can also try to avoid using features with a lot of missing values in your model altogether.
