Predictive Analytics and Modeling using CART Algorithm

This course provides a comprehensive introduction to predictive analytics and modeling using the CART (Classification and Regression Trees) algorithm. Students will learn how decision trees, a foundational machine learning technique, can be applied to both classification and regression problems. Through hands-on examples, you will learn how CART predicts outcomes from input variables, generating clear, interpretable models that support data-driven decision-making.

The course covers the key components of decision trees, including decision nodes, links, and leaves, as well as core processes like splitting, pruning, and tree selection. You will gain experience working with both numerical and categorical data and learn how to build models for large datasets efficiently.

By the end of this course, you will understand how CART forms the backbone of advanced ensemble methods such as bagged trees, random forests, and boosted trees. Practical applications in customer segmentation, marketing strategy, and other business scenarios will demonstrate how decision tree modeling drives value from data.

Whether you aspire to become a data scientist or aim to leverage data analytics in your professional field, this course equips you with the skills to create actionable insights and predictive models with confidence.

Key Learning Outcomes:

• Understand the fundamentals of decision tree algorithms for classification and regression.

• Build and interpret predictive models using CART.

• Apply splitting, pruning, and tree selection techniques to optimize models.

• Handle numerical and categorical data efficiently.

• Explore advanced applications, including random forests and boosted trees.

• Implement decision tree modeling in real-world business scenarios, such as customer segmentation and marketing analytics.

• Basic statistics and quantitative methods

• Familiarity with spreadsheets or data tools (Excel, Google Sheets)

• Introductory programming knowledge (Python or R)

• Understanding of basic machine learning concepts is a plus.

• Anyone interested in learning about data and analytics

• Aspiring data scientists and analysts

• Business professionals aiming to make data-driven decisions

• Students or professionals looking to understand predictive modeling

• Marketing, finance, or operations professionals wanting to leverage data insights

• Anyone curious about machine learning and decision tree techniques.