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 EDHEC Business School
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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
- Applied Machine Learning
- Machine Learning Methods
- Data Transformation
- Interactive Data Visualization
- Statistical Modeling
- Machine Learning
- Data Manipulation
- Data Analysis
- Exploratory Data Analysis
- Data Cleansing
- Feature Engineering
- Classification And Regression Tree (CART)
- Predictive Modeling
- R Programming
- Application Deployment
- Statistical Analysis
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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