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- Level Intermediate
- Duration 4 hours
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In this 1-hour long project-based course, you will learn how to (complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data, and examine the results using RMSE and a Confusion Matrix). Note: This course works best for learners who are based in the North America region. We're currently working on providing the same experience in other regions.Auto Summary
Discover how to predict diabetes using the power of Random Forest algorithms in R with this engaging and hands-on course. Perfect for those passionate about Data Science & AI, this 1-hour project-based course guides you through essential steps such as completing a training and test set, examining data distribution with R and ggplot2, applying a Random Forest model, and evaluating your results with RMSE and a Confusion Matrix. Offered by Coursera, this intermediate-level course is designed for learners with some prior experience in data analysis and programming in R. The course content is delivered in a concise 240-minute format, making it ideal for those seeking to enhance their skills efficiently. Plus, with a free subscription option, this course is accessible to anyone eager to advance their knowledge in predictive modeling and machine learning. Join now and take a significant step towards mastering data-driven insights in the healthcare domain.