8. Let’s take a look at the variables from the dataset, and explore the way that they might impact attended. Using your training set data only, make a proportional barplot for each one of your prospective input variables. Each barplot should show one of your input variables as a category on the x-axis, with attended as the fill variable. You should build proportional barplots (you can achieve this by adding position=”fill” inside your geom layer).

I created this Barplots for the question in 8. Let’s take a look at the variables from the dataset, and explore the way that they might impact attended. Using your training set data only, make a proportional barplot for each one of your prospective input variables. Each barplot should show one of your input variables as a category on the x-axis, with attended as the fill variable. You should build proportional barplots (you can achieve this by adding position=”fill” inside your geom layer). a. Based on the barplots that you see here, select any variable(s) that seem like they will not have a strong amount of predictive power in a naive Bayes model. Drop any such variable.

I created a plot using ggplot2.