Kaggle Titanic Competition Part II – Missing Values
There will be missing/incorrect data in nearly every non-trivial data set a data scientist ever encounters. It is as certain as death and taxes. This is especially true with big data and applies to data generated by humans in a social context or by computer systems/sensors. Some predictive models inherently are able to deal with missing data (neural networks come to mind) and others require that the missing values be dealt with separately. The RandomForestClassifier model in scikit-learn is not able to handle missing values, so we'll need to use some different approaches to assign values before training the [...]