Master Decision Trees in R: Build, Predict & Evaluate

Overview

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.

Syllabus

Foundations of Decision Tree Modeling

This module introduces learners to the fundamentals of decision tree modeling using R. It covers the basics of tree structure, data preparation, and the creation of classification models. By the end of this module, learners will understand how to preprocess data, construct decision trees, and evaluate model performance effectively.

Foundations of Decision Trees in Bank Loan Default Prediction

This module introduces learners to the fundamentals of Decision Tree modeling and its application in Bank Loan Default Prediction. Participants will explore the basics of analytics, understand the problem statement, and prepare their tools and datasets in R to begin predictive modeling with confidence.

Advanced Applications of Decision Trees in R

This module explores advanced applications of decision trees in R, focusing on real-world datasets, regression trees, and visualization. Learners will practice prediction tasks, implement splitting strategies, and compare R packages for decision tree modeling.

Building & Evaluating the Model

This module focuses on applying Decision Tree modeling in R by preparing datasets, training models, and evaluating predictive performance. Learners will gain hands-on experience in coding, interpreting results using a confusion matrix, and understanding how decision trees support financial risk prediction.