Summary
In this chapter, we deep-dived into handling missing values and outliers. We understood that missing values can distort our analyses and learned a range of imputation techniques, from simple mean imputation to advanced machine learning-based strategies. Similarly, we recognized that outliers could skew our results and deep-dived into methods to detect and manage them, both in univariate and multivariate contexts. By combining theory and practical examples, we gained a deeper understanding of the considerations, challenges, and strategies that go into ensuring the quality and reliability of our data.
Armed with these insights, we can now move on to the next chapter, where we will discuss scaling, normalization, and standardization of features.
