Logistic Regression with R: Build & Predict

Overview

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.

Syllabus

Foundations of Logistic Regression

This module introduces the fundamentals of logistic regression with R, guiding learners through data preparation, feature scaling, model fitting, and coefficient interpretation. Learners will gain the skills to prepare raw data and build a strong base for classification modeling.

Advanced Logistic Regression Applications

This module focuses on applying logistic regression to real-world datasets such as diabetes data, enhancing model performance through dimension reduction, and evaluating advanced metrics including ROC and AUC. Learners will master techniques to optimize classification outcomes.

Logistic Regression in Financial Risk Modeling

This module explores financial applications of logistic regression, including credit risk modeling, loan approval prediction, and dataset management. Learners will develop practical skills to build predictive models for financial decision-making.