Build and deploy a stroke prediction model using R
By External Provider
About this course
In this project, you’ll help a leading healthcare organization build a model to predict the likelihood of a patient suffering a stroke. The model could help improve a patient’s outcomes. Working with a real-world dataset, you’ll use R to load, clean, process, and analyze the data and then train multiple classification models to determine the best one for making accurate predictions. Upon completion, you’ll produce a well-validated prediction model that showcases your ability to perform a complete data analysis project involving feature engineering, handling missing data, model evaluation, model selection, and model deployment. There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers. ROLE: Data Analyst SKILLS: R, Data Analysis, Predictive Modeling PREREQUISITES: Load, clean, explore, manipulate, and visualize data in R, Use R to build a prediction model Use R documentations and vignettes to write new codes
What you will learn
- Explore the dataset to identify the most important patient and/or clinical characteristics
- Build a well-validated stroke prediction model for clinical use
- Deploy the model to enhance the organization's clinical decision-making