Learners completing this course will be able to differentiate regression and classification tasks, apply logistic regression models in R, preprocess raw datasets, evaluate models using confusion matrices, and optimize performance through ROC curves, AUC, and threshold adjustments. They will also gain hands-on experience with real-world applications in healthcare and finance, including diabetes prediction and credit risk assessment.

This course provides a step-by-step approach to mastering logistic regression, starting with foundational concepts and progressing to advanced applications. Learners will benefit from practical datasets, including advertisement, medical, and financial data, ensuring they acquire not just theoretical knowledge but also applied skills. Unique to this course is the integration of both technical depth (feature scaling, dimension reduction, model coefficients) and practical impact (loan approval, risk modeling). By the end, participants will be confident in building, interpreting, and validating supervised machine learning models with logistic regression in R, equipping them with valuable expertise for data science, analytics, and financial decision-making roles.