I have completed steps a-d, I am specifically looking for direction on how to complete questions 1-4 at the bottom. Thank you!

  

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2. A breakfast cereal company sells different kinds of cereals made of various ingredients. Different ingredients result in different nutrition facts of each cereal type. The store manager wants to find out how different nutrition facts affect cereal popularity in the consumer market. One way to tackle such a problem is by quantifying the predictive relationship between cereal popularity and the corresponding nutrition facts contained in the cereal. Before you start building the predictive models, you need to examine and pre-process the cereal data. A training data sample was collected and saved in the excel file (cereal.xlsx). The dataset has a total of 206 cereal samples labeled as one of the five popular levels, where 5 strongly popular, 4 = popular, 3 = Neutral, 2 = unpopular, and 1 = strongly unpopular. Each cereal sample consists of nine predictors related to the nutrition facts in the cereal, including dietary fiber, sodium, monounsaturated fat, polyunsaturated fat, total_carbohydrate, sugar, protein, saturated fat, and cholesterol. Among the nine predictors, cholesterol and sodium are measured in mg while the rest are measured in g. (note: mg and g are weight units). To start, a. Make sure you have below five packages installed in your RStudio, and then library the five packages. Applied Predictive Modeling caret e1071 corrplot FactoMineR b. Import cereal.xlsx to RStudio. c. Convert the imported cereal to data frame. Code hint: cereal 2. A breakfast cereal company sells different kinds of cereals made of various ingredients. Different ingredients result in different nutrition facts of each cereal type. The store manager wants to find out how different nutrition facts affect cereal popularity in the consumer market. One way to tackle such a problem is by quantifying the predictive relationship between cereal popularity and the corresponding nutrition facts contained in the cereal. Before you start building the predictive models, you need to examine and pre-process the cereal data. A training data sample was collected and saved in the excel file (cereal.xlsx). The dataset has a total of 206 cereal samples labeled as one of the five popular levels, where 5 strongly popular, 4 = popular, 3 = Neutral, 2 = unpopular, and 1 = strongly unpopular. Each cereal sample consists of nine predictors related to the nutrition facts in the cereal, including dietary fiber, sodium, monounsaturated fat, polyunsaturated fat, total_carbohydrate, sugar, protein, saturated fat, and cholesterol. Among the nine predictors, cholesterol and sodium are measured in mg while the rest are measured in g. (note: mg and g are weight units). To start, a. Make sure you have below five packages installed in your RStudio, and then library the five packages. Applied Predictive Modeling caret e1071 corrplot FactoMineR b. Import cereal.xlsx to RStudio. c. Convert the imported cereal to data frame. Code hint: cereal