Imagine you are cleaning your room and organizing items step-by-step. Data preprocessing is similar! In this lesson, we'll prepare a dataset for analysis by integrating multiple preprocessing techniques. Our goal is to make the data clean and ready for useful insights.

Not all columns are useful for our analysis. Some might be redundant or irrelevant. For example, columns like deck, embark_town, alive, class, who, adult_male, and alone may not add much value. Let's drop these columns.

We use the .drop() function, which takes a list of columns names to drop as an argument .