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Learn to predict water availability in Sierra Leone using random forest models with tidymodels in this 45-minute screencast. Explore data manipulation, visualization, and machine learning techniques as you work with #TidyTuesday water source data. Follow along step-by-step to build, evaluate, and interpret models, covering topics such as data preparation, feature engineering, model tuning, and variable importance. Gain practical insights into applying data science methods to real-world environmental challenges and enhance your R programming skills.
Syllabus
Introduction
Data
Map
Country
DataFrame
Fill
Payment
Building the model
Data budget
Editing model
Resampling
Last fit
VIP package
Data set
Taught by
Julia Silge