This step is concerned with the transformation of data into a usable form. The raw data retrieved in the first step is in most cases unusable by ML algorithms. Formally, data wrangling is the process of cleaning, transforming, and mapping data from one form to another for consumption in later stages of the project life cycle. This step includes missing data imputation, typecasting, handling duplicates and outliers, and so on. We will cover these steps in the context of use-case driven chapters for a better understanding.