Practical Tree Based Modeling with Decision Trees: From Theory to Application
Learning Path | 2 Course Series

Synopsis

Description

This course provides a comprehensive introduction to tree-based modeling with decision trees, covering both theoretical concepts and practical applications. In the introductory section, participants will gain foundational knowledge of decision trees, understanding their structure and significance in predictive modeling. Building upon this, the course transitions into practical applications, starting with bank loan default prediction. Through a series of lectures, participants will learn how to preprocess data, write model code in R, and evaluate model performance using confusion matrices. Additionally, they will explore other practical scenarios such as analyzing an advertisement dataset and predicting diabetes occurrences. By the conclusion of the course, participants will have a solid understanding of decision tree modeling and its diverse applications across various domains.

Section 1: Introduction to Decision Trees

In this introductory section, participants will gain a foundational understanding of decision trees and their application in predictive modeling using R. The course will cover the basics of decision tree construction, including route nodes and decision criteria.

Section 2: Decision Trees - Bank Loan Default Prediction

Expanding upon the fundamentals covered in the previous section, participants will delve into a practical application of decision trees: predicting bank loan defaults. Through a comprehensive exploration of real-world datasets and hands-on exercises, participants will learn how to preprocess data, train decision tree models, and evaluate their performance for loan default prediction.

Section 3: Advertisement Dataset

This section introduces participants to another practical scenario: analyzing an advertisement dataset using decision trees. Participants will learn data preprocessing techniques, feature scaling, and model evaluation methods specific to the advertisement domain.

Section 4: Diabetes Dataset

In this section, participants will apply decision tree modeling to analyze a diabetes dataset. They will learn how to plot model classifiers, make predictions, and interpret results, gaining valuable insights into decision tree applications in healthcare analytics.

Section 5: Caeseats Dataset

Continuing their exploration, participants will work with the Caeseats dataset to further solidify their understanding of decision trees. They will learn data splitting techniques, model implementation using the tree package, and interpret the results for actionable insights.

Section 6: Conclusion

In the concluding section, participants will reflect on their learning journey and key takeaways from the course. They will summarize their understanding of decision trees modeling using R and its applications across various domains.