Build a Predictive Text Model for Avatar: The Last Airbender with Tidymodels

Learn to build a predictive text model using R and tidymodels to identify speakers from Avatar: The Last Airbender dialogue. Explore the #TidyTuesday Last Airbender dataset, create visualizations with custom color palettes, and handle class imbalance in the data. Implement preprocessing techniques, evaluate model performance, and calculate model-agnostic variable importance scores to gain insights into the most influential features for speaker prediction.

Introduction
Welcome
The data
Exploration
Avatar Palette
DataFrame
Weighted Log Odds
New Table
Graphing
Building the model
Class imbalance
Preprocessing
Results
Evaluation
Variable importance
Variable important scores