So I was searching on how to handle missing data and came across this post from Machine Learning Mastery.
This article states that some algorithms can be made robust to missing data, such as Naive Bayes and KNN.
Not all algorithms fail when there is missing data.
There are algorithms that can be made robust to missing data, such as k-Nearest Neighbors that can ignore a column from a distance measure when a value is missing. Naive Bayes can also support missing values when making a prediction.
But then it says that sklearn implementations are not robust to missing data.
Sadly, the scikit-learn implementations of Naive Bayes, decision trees, and k-Nearest Neighbors are not robust to missing values.
Are there ML libs (preferably in Python, but could also be in other languages) that these algorithms are robust to missing data?