1. Describe the Naive Bayes Classifier algorithm and its uses, what is the suitable data scale (level) of predicted outcome in Naive Bayes Classifier? What is the data scale (level) of predictor variables in Naive Bayes Classifier?
2. Describe the KNN algorithm and its uses, what is the data suitable scale (level) of predicted outcome in KNN? What is the data Scale (level) of predictor variables in KNN?
3. Provide unique examples of business situations that you would use Naive Bayes Classifier or KNN for making decisions such as classifying your potential customers.

You are encouraged to help each other in understanding Unit Activities and practicing with scripts and answering the questions in the discussion forum.

While you are watching the videos, and while you are reading the required texts, (Book chapter and articles) in "Unit 7: Activities" experiment with scripts and exercises in the Book chapter and the content and/or similar scripts. Practice with R scripts discussed in those resources using the installed Rstudio on your computer.

Call the libraries you use within every one of your scripts (even if you have called it in your command line) because your professor should be able to run your script on his/her computer. When you use one of the functions from a library, specify the library you are using by "::" notation. For example:

library(ggplot2) library(mlbench) library(caret) ggplot2::ggplot(.......) caret::train(........)

Add comments to your ".R" script such that the reader would understand what you are intending by each command.

(OPTIONAL: If possible, for your working environment, then make an Rmarkdown document from your scripts with comments about what you have learned. Then Knit it to a pdf and upload three files to the Learning Activity Grade container.)