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Introduction
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Exploring the data
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Fine distribution
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Visualization
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Article Violation
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Feature Engineering
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Filtering
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GDP
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Outliers
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Skip
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Workflow
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Looking at the model
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Making new data
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Predicting new data
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Filtering results
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Summary
Description:
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.

Modeling GDPR Violations in R With Tidymodels

Julia Silge
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