This module introduces learners to the fundamentals of employee attrition prediction using Random Forest algorithms in R. It begins with an overview of the business problem, explores the machine learning methodology behind Random Forest, and establishes a strong conceptual framework. Learners will also examine the structure and significance of the dataset, understand variable types and transformations, and perform essential pre-modeling tasks such as data cleaning and encoding. By the end of this module, learners will be able to prepare data and understand Random Forest fundamentals essential for building predictive models.

This module focuses on implementing, tuning, and validating Random Forest models for employee attrition prediction. Learners will begin by developing a predictive model using cleaned and preprocessed data. They will then explore techniques to optimize model performance, including parameter tuning and validation methods. Emphasis is placed on understanding how hyperparameters influence model behavior and ensuring robust evaluation using appropriate metrics. By the end of the module, learners will be able to build, fine-tune, and validate a Random Forest model that generalizes well to unseen data.