Task

You are to import and clean the same HealthCareData$\_2024.$csv$,$ that was used in the

previous assignment. Then run, tune and evaluate two supervised ML algorithms $($each

with two types of training data$)$ to identify the most accurate way of classifying

malicious events.

Part $1$ General data preparation and cleaning

a$)$ Import the HealthCareData$\_2024.$csv into R Studio. This version is the same as

Assignment $1.$

b$)$ Write the appropriate code in R Studio to prepare and clean the

HealthCareData$\_2024$ dataset as follows:

i$.$ Clean the whole dataset based on the feedback received for Assignment $1.$

ii$.$ For the feature NetworkInteractionType, merge the $$Regular$$ and

$$Unknown$$ categories together to form the category $$Others$.$ Hint: use the

forcats:: fct$\_$collapse$(.)$ function.

iii. Select only the complete cases using the na$.$omit$(.)$ function, and name the

dataset dat.cleaned.

Briefly outline the preparation and cleaning process in your report and why you

believe the above steps were necessary.

c$)$ Use the code below to generated two training datasets $($one unbalanced

mydata.ub$.$train and one balanced mydata.b$.$train$)$ along with the testing set

$($mydata$.$test$).$ Make sure you enter your student ID into the command

set.seed$(.).$

# Separate samples of normal and malicious events

dat.class$0<-$ dat.cleaned $\%>\%$ filter$($Classification $==$ "Normal"$)$ # normal

dat.class$1<-$ dat.cleaned $\%>\%$ filter$($Classification $==$ "Malicious"$)$ # malicious

# Randomly select $9600$ non$-$malicious and $400$ malicious samples using your student

ID$,$ then combine them to form a working data set

set.seed$($Enter your Student ID$)$

rows.train$0<-$ sample$(1$:nrow$($dat$.$class$0),$ size $=9600,$ replace $=$ FALSE$)$

rows.train$1<-$ sample$(1$:nrow$($dat$.$class$1),$ size $=400,$ replace $=$ FALSE$)$

# Your $10000$unbalanced$$ training samples

train.class$0<-$ dat.class$0[$rows$.$train$0,]$ # Non$-$malicious samples

train.class$1<-$ dat.class$1[$rows$.$train$1,]$ # Malicious samples

mydata.ub$.$train $<-$ rbind$($train$.$class$0,$ train.class$1)$

# Your $19200$balanced$$ training samples, i$.$e$.9600$ normal and malicious samples e

ach.

set.seed$($Enter your Student ID$)$

$6|$ P a g e

train.class$1\_2<-$ train.class$1[$sample$(1$:nrow$($train$.$class$1),$ size $=9600,$

replace $=$ TRUE$),]$

mydata.b$.$train $<-$ rbind$($train$.$class$0,$ train.class$1\_2)$

# Your testing samples

test.class$0<-$ dat.class$0[-$rows.train$0,]$

test.class$1<-$ dat.class$1[-$rows.train$1,]$

mydata.test $<-$ rbind$($test$.$class$0,$ test.class$1)$

Note that in the master data set, the percentage of malicious events is

approximately $4\%.$ This distribution is roughly represented by the unbalanced

data. The balanced data is generated based on up$-$sampling of the minority class

using bootstrapping. The idea here is to ensure the trained model is not biased

towards the majority class, i$.$e$.$ normal events.

Part $2$ Compare the performances of different ML algorithms

a$)$ Randomly select two supervised learning modelling algorithms to test against

one another by running the following code. Make sure you enter your student ID

into the command set.seed$(.).$ Your $2$ ML approaches are given by myModels.

set.seed$($Enter your student ID$)$

models.list$1<-$ c$("$Logistic Ridge Regression",

"Logistic LASSO Regression",

"Logistic Elastic$-$Net Regression"$)$

models.list$2<-$ c$("$Classification Tree",

"Bagging Tree",

"Random Forest"$)$

myModels $<-$ c$($sample$($models$.$list$1,$ size $=1),$

sample$($models$.$list$2,$ size $=1))$

myModels $\%>\%$ data.frame