This course guides learners through the structured development of predictive models using Random Forest techniques in R, specifically applied to employee attrition data. The course is divided into two comprehensive modules. The first module introduces the foundational concepts of classification and Random Forest algorithms, guiding learners to explain, identify, and prepare relevant variables. Learners also perform essential preprocessing tasks to shape the dataset for analysis.

In the second module, students construct, tune, and evaluate Random Forest models using real-world HR data. Through practical lessons, participants will apply parameter optimization techniques, analyze model performance using appropriate metrics, and justify their modeling choices using validation strategies. By the end of the course, learners will have the capability to build robust, interpretable machine learning models for workforce analytics and make informed data-driven decisions regarding employee retention.