Analyzing Credit Scores with tidymodels in R

In this live training, you'll find out how to motivate the benefits of dimensionality reduction while exploring predictors of credit scores. Using ggplot2, you’ll see how UMAP can extract information-rich features that help to group credit scores. Then, you'll see how to build UMAP into a tidymodels workflow that fits a decision tree model to predict credit scores. Finally, you'll find out how to evaluate the performance of models with and without UMAP dimensionality reduction.
What will I learn?
- Learn the benefits of dimensionality reduction.
- Learn to perform feature extraction tidymodels recipes.
- Learn incorporate feature extraction in the model building process using tidymodels workflows.
Link to Challenge and Solution Workspace: bit.ly/3HTClx1
Explore the rest of DataCamp's Webinars and Live Trainings at www.datacamp.com/resources/we...

[@ghettoghost2657]

R is a great tool . I know a lot of eyes are on python right now but R has a lot of great use cases

[@genghisrex]

What pre-work do you need to do before you take any R course? Do you need to know statistic?

[@517127]

Honestly you don't need statistic to learn R, It is wise in order to work as data analyst or scientist. But you can learn and statistics at same time.