Summary

In this chapter, we have learned about the foundational concepts, implementation techniques, and evaluation methods for supervised learning, with a focus on both regression and classification methods. This chapter covered linear regression, multiple regression, polynomial regression, logistic regression, K-nearest neighbors, decision trees, and Naive Bayes classifiers. For each algorithm, we also provided a step-by-step Python implementation, explained model assumptions, and demonstrated the use of pipelines for preprocessing and model training. Furthermore, we gained an in-depth understanding of evaluation metrics, including MAE, MSE, RMSE, R², accuracy, precision, recall, and F1 score, as well as data-splitting strategies such as train-test split, stratified sampling, and K-fold cross-validation.

In the next chapter, we will explore a different aspect of machine learning called unsupervised learning. In this chapter, we will learn how machine learning models without...