Summary

In this chapter, you learned how important it is to prepare any given dataset and fix the main quality issues it has. This is critical because the cleaner a dataset is, the easier it will be for any machine learning model to easily learn about the relevant patterns. On top of this, most algorithms can't handle issues such as missing values, so they must be handled prior to the modeling phase. In this chapter, you covered the most frequent issues that are faced in data science projects: duplicate rows, incorrect data types, unexpected values, and missing values.

The goal of this chapter was to introduce you to the concepts that will help you to spot some of these issues and easily fix them so that you have the basic toolkit to be able to handle other cases. As a final note, throughout this chapter, we emphasized how important it is to discuss the issues we find with the business or the data engineering team we are working with. For instance, if you've detected...