Advanced Imputation

In many real-world datasets, there is a lot of missing or corrupted data. In many cases, simply throwing out the bad parts of the dataset is unhelpful and wasteful. You can use imputation to fill missing or empty values with reasonable substitutes, like a constant value or the mean of similar features to make the missing data as accurate to the true data as possible. A more advanced and accurate technique is to use machine learning to predict the best values to fill.