This chapter introduces the concept of Unsupervised Learning through the lens of Data Clustering. Students will learn how to find "hidden structures" in data without predefined labels, mastering the techniques used to group similar observations together. From identifying customer segments to discovering natural patterns in biology, this chapter provides the tools to make sense of unlabeled datasets by letting the data speak for itself. Core Topics covered: Introduction to Clustering K-Means Clustering Hierarchical Clustering DBSCAN Cluster Validation Gaussian Mixture Models Practical Clustering Workflow Chapter Lab Activity: Customer Segmentation with wholesales Data

This project builds a count regression model to predict the number of wine cases purchased by distributors based on chemical and marketing properties of roughly 12,000 commercially available wines. We walk through exploratory data analysis, data preparation including missing value flags and median imputation, and model building across Poisson, Negative Binomial, and Linear regression families. The key finding is that expert star ratings and label appeal dominate the prediction, while chemical variables add little beyond those two. The parsimonious Poisson model is selected as the final model based on AIC and interpretability.