토목공학과

주요코딩

# 2번

install.packages("arules")

library(arules)

tr=read.delim("dataTransactions.tab", stringsAsFactors=FALSE, fileEncoding = "euc-kr")

head(tr, n=20)

tr.filter=subset(tr, subset=!(corner %in% c("일반식품","가구")))

head(tr.filter, n=20)

tr.filter.uniq=unique(tr.filter)

trans=as(split(tr.filter.uniq$corner, tr.filter.uniq$custid), "transactions")

rules=apriori(trans, parameter=list(support=0.2, confidence=0.8))

summary(rules)

rules.target=subset(rules, rhs %in% "스포츠" & lift>1.5)

inspect(sort(rules.target, by="confidence"))

# 3번

turkey=read.csv("turkey.csv") 

head(turkey, n=20)

turkey=na.omit(turkey)

head(turkey, n=20)

install.packages("MASS")

library(MASS)

model1=lda(TYPE~HUM+RAD, data=turkey)

model1

predict(model1,data.frame("HUM"=c(150,150), "RAD"=c(135,150)))

model2=qda(TYPE~HUM+RAD, data=turkey)

model2

predict(model2,data.frame("HUM"=c(150,150), "RAD"=c(135,150)))

#4번

install.packages("nnet")

library(nnet)

cb=read.delim("Hshopping3.txt", stringsAsFactors = FALSE, fileEncoding = "euc-kr")

head(cb)

cb$sex=factor(cb$sex)

cb$corner=factor(cb$corner)

cb$refund=factor(cb$refund)

str(cb)

install.packages("caret", dependencies = TRUE)

library(caret)

set.seed(1)

inTrain=createDataPartition(y=cb$refund, p=0.6, list=FALSE)

cb.train=cb[inTrain,]

cb.test=cb[-inTrain,]

set.seed(1234567)

nn_model=nnet(refund~age+money+corner, data=cb.train, size=7, maxit=1000)

install.packages("NeuralNetTools")

library(NeuralNetTools)

garson(nn_model)

predicted=as.factor(predict(nn_model, newdata=cb.test, type="class"))

confusionMatrix(predicted,cb.test$refund)

# 5

set.seed(5)

inTrain=createDataPartition(y=cb$refund, p=0.6, list=FALSE)

cb.train=cb[inTrain,]

cb.test=cb[-inTrain,]

dim(cb.train); dim(cb.test)

install.packages("C50")

library(C50)

c5_options =C5.0Control(winnow = FALSE, noGlobalPruning= FALSE) 

#winnow=TRUE : feature selection 적용(다중공선성, 중요도 등 고려 일부 변수 자동 제거)

c5_model =C5.0(refund~ age+money+corner, data=cb.train, control=c5_options, rules=FALSE)

summary(c5_model)

#plot(c5_model)

cb.test$c5_pred =predict(c5_model, cb.test, type="class")

cb.test$c5_pred_prob =predict(c5_model, cb.test, type="prob")

head(cb.test)

confusionMatrix(cb.test$c5_pred, cb.test$refund)