Linear and Logistic Regression in R: Regression vs Classification

Understanding the difference between predicting a continuous value and classifying data into categories is the cornerstone of modern data science. This course provides a clear, conceptual, and practical introduction to linear and logistic regression using R. You will transition from grasping basic statistical concepts to actively preparing data, building predictive models, and interpreting their results to drive business decisions. Through clear written explanations and structured code examples, you will gain a solid understanding of how to evaluate model performance using industry-standard metrics. What you'll learn: - Understand the fundamental differences between regression and classification tasks - Prepare and clean raw datasets using modern R programming data manipulation workflows - Build linear regression models to predict continuous numerical outcomes - Develop logistic regression models for binary classification and decision-making - Evaluate model performance using key metrics such as R-squared, RMSE, accuracy, and ROC-AUC - Interpret model coefficients to extract actionable business insights The course begins with essential terminology and the conceptual foundations of predictive modeling. You will then progress step-by-step through data preprocessing, model construction in R, and the critical evaluation techniques needed to validate your findings. This course is designed for beginners, business analysts, and aspiring data scientists who want a practical introduction to machine learning. No prior programming or advanced statistical experience is required. Start your journey into data-driven decision-making and master the essentials of predictive modeling today.