Learn how to apply the Classification and Regression Tree (CART) algorithm to build predictive models for term deposit investment decisions. In this course, you will explore the complete predictive modeling workflow using real-world financial marketing scenarios, from understanding business objectives and interpreting data variables to developing, optimizing, and validating decision tree models.

You will begin by learning the foundations of CART and the characteristics of data used for term deposit prediction. Next, you will prepare data, construct binary classification models, and apply node splitting techniques to build effective decision trees. Finally, you will improve model performance through parameter tuning, pruning strategies, and validation techniques to reduce overfitting and evaluate model performance on unseen data. Designed for learners interested in predictive analytics, machine learning, financial marketing, and data-driven decision-making, this course emphasizes model transparency and interpretability while following industry-standard modeling practices. By the end of the course, you will be able to describe data characteristics, build CART classification models, optimize decision trees, and evaluate predictive model performance for term deposit investment scenarios.