Dimensionality reduction is sometimes feature extraction, and it is the process of combining the existing input variables into a new set of a much reduced number of input variables. One of the most used methods for this type of feature engineering is principle component analysis (PCA), which utilizes the variance in data to come up with a reduced number of input variables that don't look like the original input variables.