zeinab elsadig

ca3

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