Learn how to design, build, and evaluate Random Forest models in R for employee attrition prediction using a structured, hands-on workflow. This course introduces the fundamentals of classification and Random Forest algorithms before guiding you through data preparation, model construction, parameter tuning, validation, and performance evaluation using employee attrition data.

You will begin by exploring the employee attrition problem, understanding the dataset, identifying relevant variables, and preparing categorical and numerical data through essential preprocessing techniques. Next, you will construct a Random Forest classification model, optimize its performance through hyperparameter tuning, and evaluate its effectiveness using appropriate validation methods and performance metrics. This course is designed for learners who want to develop practical skills in machine learning with R, particularly for predictive analytics involving workforce data. Its structured progression from foundational concepts to model refinement helps you understand not only how to build a model, but also how to justify modeling decisions through systematic evaluation. By the end of the course, you will be able to prepare data, build and fine-tune Random Forest models, validate model performance, and interpret results to support informed, data-driven employee attrition analysis.