Introduction

In the previous chapter, you saw how critical it was to get a very good understanding of your data and learned about different techniques and tools to achieve this goal. While performing Exploratory Data Analysis (EDA) on a given dataset, you may find some potential issues that need to be addressed before the modeling stage. This is exactly the topic that will be covered in this chapter. You will learn how you can handle some of the most frequent data quality issues and prepare the dataset properly.

This chapter will introduce you to the issues that you will encounter frequently during your data scientist career (such as duplicated rows, incorrect data types, incorrect values, and missing values) and you will learn about the techniques you can use to easily fix them. But be careful – some issues that you come across don't necessarily need to be fixed. Some of the suspicious or unexpected values you find may be genuine from a business point of view. This...