Lecture 21
Predictive modeling

Date: Dec 3, 2024

Learning objectives

After today, you should have a better understanding of:

1. Define linear regression, its limitations, and objective function.
2. Describe the purpose of loss functions in regression.
3. Understand the conversion of data from a DataFrame to NumPy arrays.
4. Develop hands-on programming skills for implementing regression in Python.
5. Interpret the coefficients obtained through optimization and evaluate the model's performance.
6. Visualize linear regression models and their fit to data.
7. Discuss practical considerations for model interpretation, assumptions, and limitations.