Explore data preprocessing and modeling techniques using tidymodels packages in R to analyze GDPR violations from #TidyTuesday data. Dive into the fine distribution, visualize article violations, and perform feature engineering. Learn to filter data, handle outliers, and create workflows for model building. Discover how to make predictions on new data and interpret results. Follow along with the provided code on Julia Silge's blog to gain hands-on experience in applying these data science techniques to real-world regulatory compliance data.
Overview
Syllabus
Introduction
Exploring the data
Fine distribution
Visualization
Article Violation
Feature Engineering
Filtering
GDP
Outliers
Skip
Workflow
Looking at the model
Making new data
Predicting new data
Filtering results
Summary
Taught by
Julia Silge
