The'functional needs repair' category of the target variable only makes up about 7% of the whole set. The implication is that whatever algorithm you end up using it's probably going to learn the other two balanced classes a lot better than this one. Such is data science: the struggle is real. The first thing we're going to do is create an'age' variable for the waterpoints as that seems highly relevant. The'population' variable also has a highly right-skewed distribution so we're going to change that as well: One of the most important points we learned from the week before and something that will stay with me is the idea of coming up with a baseline model as fast as one can.