Predictive Modeling with Logistic Regression in R

Understanding how to predict categorical outcomes is a fundamental skill in data science and business analytics. This text-based course guides you through the core concepts of binary classification using the R programming language. You will transition from a beginner to confidently preparing data, fitting logistic regression models, and interpreting predictions. By reading clear explanations and analyzing structured code snippets, you will learn to evaluate model performance using modern diagnostics. What you'll learn: Differentiate between regression and classification tasks in data science; Understand the mathematical foundations of the logistic function and odds ratios; Prepare and preprocess data for binary classification in R; Fit logistic regression models using both base R and modern tidyverse workflows; Evaluate model performance using confusion matrices and ROC curves; Interpret model coefficients to make actionable business decisions. The course begins with foundational statistical concepts before moving into step-by-step code implementations. You will explore practical scenarios, progressing from data preparation to model diagnostics and prediction interpretation. This program is designed for aspiring data analysts, researchers, and beginners to R who want to master classification models. No prior predictive modeling experience is required, though a basic familiarity with R syntax is helpful. Start reading today to unlock the power of predictive classification with R.