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.



Build and deploy a stroke prediction model using R
Access provided by Dnipro University of Technology
7,993 already enrolled
Recommended experience
Objectives
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
Skills you'll demonstrate
- R Programming
- Tidyverse (R Package)
- Data Presentation
- Machine Learning
- Interactive Data Visualization
- Statistical Modeling
- Exploratory Data Analysis
- Machine Learning Methods
- Application Deployment
- Data Cleansing
- Feature Engineering
- Data Manipulation
- Statistical Analysis
- Predictive Analytics
- Classification And Regression Tree (CART)
- Data Analysis
- Predictive Modeling
- Data Import/Export
- Risk Modeling
- Advanced Analytics
Details to know
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About this Project
Project plan
This project requires you to independently complete the following steps:
Import data and data preprocessing
Build prediction models
Evaluate and select prediction models
Deploy the prediction model
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