Master logistic regression in R and build the practical skills needed to solve real-world classification problems. In this course, you will learn how to distinguish regression from classification tasks, prepare data for modeling, build logistic regression models in R, and interpret model coefficients with confidence. As you progress, you will evaluate model performance using confusion matrices, ROC curves, AUC, and threshold analysis to improve classification results.

Through hands-on work with advertisement, healthcare, and financial datasets, you will apply logistic regression to predict diabetes outcomes, analyze credit risk, and support loan approval decisions. You'll also explore feature scaling, dimension reduction, dataset splitting, and model validation to develop reliable supervised machine learning models. Designed for learners interested in data science, machine learning, analytics, and financial modeling, this course emphasizes practical implementation alongside core concepts. By the end of the course, you will be able to preprocess raw datasets, build and evaluate logistic regression models in R, optimize model performance, and apply predictive modeling techniques to healthcare and financial decision-making scenarios. If you want to strengthen your R programming and classification modeling skills through practical, data-driven applications, this course provides a structured path from foundational concepts to real-world predictive analytics.