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.
Logistic Regression with R: Build & Predict
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Gain insight into a topic and learn the fundamentals.
7 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Differentiate regression vs classification and apply logistic models.
Preprocess datasets, evaluate with confusion matrices and ROC.
Apply logistic regression to healthcare and finance case studies.
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
September 2025
Assessments
12 assignments
Taught in English
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There are 3 modules in this course
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.
What's included
9 videos4 assignments1 plugin
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.
What's included
9 videos4 assignments
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.
What's included
9 videos4 assignments
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