3 credits
Fall 2025 Lecture Upper DivisionData from last updated 4/30/2025
Fall 2025
Students build and evaluate predictive models to support regression-type and classification-type business problems using popular industry tools (e.g., RStudio, SAS Enterprise Miner). Methods covered include multiple linear regression, ridge regression, lasso, CART, logistic regression, LDA/QDA, SVM, and ensemble methods such as random forests.
Learning Outcomes1Generate a valid predictive analytics solution by following a structured process.
2Demonstrate practical understanding of frequently used predictive analytics techniques.
3Demonstrate a working knowledge of RStudio and SAS Enterprise Miner to build an access predictive models.
4Demonstrate project poster and presentation of a predictive analytics solution to a business problem.
Prerequisites
Restrictions
Attachments
GPA by professor
BoilerCoursesis an unofficial catalog for Purdue courses
made by Purdue students.
made by Purdue students.