Learners will be able to install and configure Python tools, apply machine learning workflows, transform healthcare data, implement logistic regression, and evaluate prediction models using ROC curves. This course equips students with the practical skills to design, build, and test real-world machine learning solutions for healthcare analytics.

Through step-by-step guidance, learners begin with setting up Anaconda and essential Python libraries, then progress to understanding the Pima Indians Diabetes dataset, exploring machine learning steps, and applying logistic regression for binary classification. The course emphasizes hands-on practice in Jupyter Notebook, where students preprocess data by handling headers, encoding categorical values, and splitting datasets into training and testing sets. Finally, learners validate model performance with ROC curves to interpret diagnostic accuracy. By completing this course, learners will gain the confidence to translate healthcare datasets into actionable predictions. Unlike generic machine learning tutorials, this course is unique because it focuses on a real medical case study, bridges theory with coding practice, and builds both conceptual understanding and applied skills in predictive modeling.