Unlock the power of decision tree modeling in R and learn how to build, evaluate, and interpret predictive models for both classification and regression tasks. This course provides a structured, hands-on introduction to decision trees, guiding you from core concepts and data preparation to implementing and assessing models using practical datasets.

You will begin by understanding the fundamentals of decision trees, including the differences between classification and regression trees. As you progress, you will apply data preprocessing techniques such as encoding and feature preparation, then build and evaluate classifiers using the rpart package and confusion matrix analysis. The course also explores advanced applications, including prediction, visualization, splitting techniques, and working with multiple R packages such as tree for classification and regression modeling. Designed for beginners while remaining valuable for intermediate learners, this course combines conceptual understanding with step-by-step coding practice. By the end of the course, you will be able to preprocess data, create and evaluate decision tree models, apply them to real-world datasets, interpret results with confidence, and use R to support predictive modeling tasks. If you want to strengthen your machine learning skills in R through practical decision tree modeling, this course provides a clear and progressive learning path.