In this chapter, we explored different types of classification algorithms for supervised machine learning. We leveraged the Australian weather data, designed a business problem around it, and explored various machine learning techniques on the same use case. We studied how to develop these models in R and studied the functioning of these algorithms in depth with mathematical abstractions. We summarized the results from each technique and studied a generalized approach to tackle common classification use cases.
In the next chapter, we will study feature selection, dimensionality reduction, and feature engineering for machine learning models.