In this chapter, we examined how PCA works. We briefly discussed how to deal with a dataset in cases where most values are missing on some attributes. We examined how to determine the adequate number of components and the proportion of variance they explain. We also saw how to give a meaningful name to the component. Finally, we began examining linear relationships between attributes using correlations. In the next chapter, we will discuss association rules with apriori.