This is essentially a step that is adopted in the early stages of an ML project pipeline. Data preprocessing involves transforming the raw data in a format that is acceptable as input by ML algorithms.

Feature hashing, missing values imputation, transforming variables from numeric to nominal, and vice versa, are a few data preprocessing steps among the numerous things that can be done to data during preprocessing.

Raw text documents' transformation into word vectors is an example of data preprocessing. The word vectors thus obtained can be fed to an ML algorithm to achieve documents classification or documents clustering.