Logistic Regression in RStudio: Data Modeling Fundamentals

Making sense of binary outcomes—like whether a customer will buy a product or if a transaction is fraudulent—is a core skill in data science. Logistic regression is the foundational statistical method used to solve these classification problems. This text-based course guides you from the absolute basics of binary data to fitting, interpreting, and evaluating your own predictive models. You will learn how to prepare your data, run regression analysis in RStudio, and confidently explain the results. What you'll learn: - Learn the core mathematical concepts and logic behind logistic regression and binary classification. - Prepare and clean raw datasets in RStudio using modern tidyverse workflows. - Fit logistic regression models using the glm function and interpret odds ratios and coefficients. - Evaluate model performance using confusion matrices, accuracy, and ROC curves. - Validate models by implementing training and testing data splits to prevent overfitting. - Address real-world modeling challenges like class imbalance and multicollinearity. The course begins with essential terminology and statistical foundations before walking you through step-by-step coding examples in RStudio. You will progress from data preparation to model fitting, evaluation, and diagnostics. This course is designed for beginners, data analysts, and aspiring data scientists who want a solid foundation in predictive modeling. No prior experience with logistic regression is required. Start reading today to unlock the power of predictive classification in R.