By the end of this course, learners will build, interpret, and evaluate decision tree models in R for both classification and regression tasks. They will gain hands-on skills in data preprocessing, feature engineering, and model training, while applying predictive techniques to real-world datasets including advertisements, diabetes outcomes, Caeseats sales, and bank loan defaults.

Through step-by-step coding practices, learners will implement decision tree algorithms using R packages like rpart and tree, visualize results, and evaluate performance with tools such as the confusion matrix. They will also learn to generate actionable insights for decision-making, with a particular emphasis on financial risk management applications. This course is uniquely designed to bridge theory with practice, combining structured progression for beginners with advanced applications for intermediate learners. By completing it, participants will not only master supervised learning with decision trees but also confidently apply their models to real-world business and financial scenarios, strengthening both their machine learning expertise and analytical decision-making skills.