Findings and Conclusions The analysis explored demographic and health-related factors associated with stroke prediction. Data preprocessing included checking for missing values, summarizing variables, and preparing the dataset for modeling. A logistic regression model was trained and evaluated using a train-test split and a confusion matrix. The model demonstrated the ability to classify stroke cases and can be saved for future use. Overall, this project illustrates a complete machine learning workflow in R, including data preparation, model development, evaluation, and basic deployment.