This chapter shifts the focus to the most popular domain of Supervised Learning: Data Classification. Students will learn how to build models that can "decide" and "predict" categorical labels for new data. From determining whether an email is spam to diagnosing a medical condition, this chapter provides a robust toolkit for making evidence-based predictions by learning from historical patterns. Core Topics covered: Introduction to Classification Logistic Regression K-Nearest Neighbors Decision Trees Random Forest and Ensemble Methods Model Evaluation Model Comparison and Selection Chapter Lab Activity: Medical Diagnosis Classification with Pima Data

This project analyzes foundational learning outcomes in mathematics and reading among 10-year-old children across selected African countries (Kenya, Mali, Mozambique, Senegal, Tanzania, and Uganda). Using survey-based data and weighted estimates, it compares the proportion of children meeting minimum proficiency levels in numeracy and literacy. The analysis highlights cross-country disparities and overall regional performance, providing evidence on learning gaps to inform education policy and improve foundational skills outcomes in Africa.