Being able to predict students’ performance has been a primary driver for the adoption of learning analytics and has attracted many scientists to the field. Predictive modeling focuses on using students’ data to forecast outcomes such as student grades, enabling teachers and administrators to offer just-in-time support to students at risk. This chapter uses advanced predictive methods, namely machine learning, where the goal is to predict continuous variables like grades. The chapter uses advanced and popular AI/machine learning algorithms like Random Forest, K-Nearest Neighbor, Linear Regression, Neural Networks, and Support Vector Machines. The chapter provides a practical guide to building and evaluating predictive models with R using two approaches: one is the classic approach for predictive modeling with R, and the other more modern approach using the tidymodels suite.