This is the third and the most time-consuming step in any data science project. Data preparation takes place once we have understood the business problem and explored the data available. This step involves data integration, cleaning, wrangling, feature selection, and feature engineering. First and the foremost is data integration. There are times when data is available from various sources and hence needs to be combined based on certain keys or attributes for better usage.

Data cleaning and wrangling are very important steps. This involves handling missing values, data inconsistencies, fixing incorrect values, and converting data to ingestible formats such that they can be used by ML algorithms.

Data preparation is the most time-consuming step, taking over 60-70% of the overall time taken for any data science project. Apart from data integration and wrangling, this step involves selecting key features based on relevance, quality, assumptions, and constraints. This is also termed as feature selection. There are also times when we have to derive or generate features from existing ones. For example, deriving age from date of birth and so on, depending upon the use case requirements. This step is termed as feature engineering and is again required based on use case.