Most often than not, you'll encounter a dataset  in your data science projects where you'll have missing data in at least one column. In some cases, you can just ignore that row by taking it out of the dataset. But that'll not be the case always. Sometimes, that row would be crucial for the training, maybe because the dataset itself is very small and you can't afford to lose any row, or maybe it holds some important data, or for some other reason. When this is the case, a very important question to answer is, how do you fill in the blanks? There are many approaches to solving this problem, and one of them is using SciKit's Imputer class. If you're