Predictive Analytics Model for Term Deposit Investment

Overview

This course empowers learners to apply predictive analytics techniques using the CART (Classification and Regression Tree) algorithm in the context of term deposit investment decisions. Structured around real-world marketing scenarios, learners will explore the end-to-end process of building decision tree models—from understanding business objectives and interpreting variables to developing, optimizing, and validating CART models. Through practical video lessons, participants will gain hands-on experience in constructing binary classification models, tuning parameters, and using pruning strategies to avoid overfitting. The course emphasizes data preprocessing, model transparency, and interpretation of results for effective decision-making in financial marketing campaigns. By the end of the course, learners will be able to describe data characteristics, construct classification models, and evaluate their performance on unseen data, in alignment with industry-standard practices.

Syllabus

Predictive Modeling with CART for Term Deposit Investments

This module introduces learners to the foundational concepts and practical implementation of the Classification and Regression Tree (CART) algorithm for predicting term deposit investment outcomes. Through step-by-step walkthroughs, learners explore the structure and logic behind CART, examine the variables involved in modeling, and gain hands-on experience in building, optimizing, and validating decision tree models. The module emphasizes model interpretability, parameter tuning, and best practices for applying predictive analytics in financial marketing contexts.