Exploring tidymodels through Exercises

In this project, we’ll take on the role of a data scientist analyzing the results of a cake-baking competition. The bakers worked hard to perfect the recipes and create the best cakes possible. Each cake was made with a unique combination of ingredients, including flour, sugar, eggs, butter, milk, baking powder, vanilla, and salt. Additionally, the bakers took great care to bake each cake at a specific temperature and for a specific duration to ensure optimal results. The judges then assigned the cakes a score based on their taste, texture, and appearance. The aggregate score ranges from 0 to 100, with higher scores indicating better quality cakes.

Based on the results of the competition, we need to create a model that can forecast the score of any given cake based on the ingredients, baking time, and baking temperature. To do so, we’ve been given a dataset called BakingData. The BakingData dataset includes the fields:

• flour (in grams)
• sugar (in grams)
• eggs (count)
• butter (in grams)
• milk (in ml)
• baking_powder (in grams)
• vanilla (in ml)
• salt (in grams)
• temp (baking temperature in degrees Celsius)
• time (baking time in minutes)
• score (the output field, on a scale of 0 to 100)

These exercises are designed to help build our understanding and skill with tidymodels. All tasks will be coded into the provided function definition. Outputs should be returned to the variables indicated.

When all the tasks are completed, take the time to go back and compare the performance of the different models created!