In this week's #TidyTuesday video, I go over the Palmer Penguins dataset. I then briefly describe each #TidyModels packages and its main functions. I use the Rsample package to create a train and test set as well as a validation set using kfolds crossvalidation. I also preprocess the data using the Recipes package as well as use Knn to impute missing data. I then create two models: an XGBoost model and a Knearest neighbor model using the Parsnip package. After that, I use the Dials package to create a grid of parameters for both models and then train the models using the Tune package. Finally, I use the Workflow package to train the models and evaluate the models using the Yardstick package. After modeling, I create an R Shiny App using shinydashboard and deploy the previously created model for realtime predictions.

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