토목공학과

# 머신러닝_단순회귀분석

cars=read.csv("cars_2022.csv")

head(cars)

out=lm(dist~speed, data=cars)

summary(out)

out_0=lm(dist~speed+0, data=cars)

summary(out_0)

new=data.frame(speed=27)

predict(out_0, new, interval = "confidence")

predict(out_0, new, interval = "prediction")

# 머신러닝_다중 회귀분석

salary=read.csv("salary_2022.csv")

head(salary)

model=lm(salary~experience+score, data=salary)

summary(model)

predict(model, data.frame("experience"=c(9,8), "score"=c(85,90)), interval="confidence")

# 머신러닝_판별분석

turkey=read.csv("turkey.csv") 

head(turkey, n=20)

turkey=na.omit(turkey)

head(turkey, n=20)

install.packages("MASS")

library(MASS)

model_lda=lda(TYPE~HUM+ULN, data=turkey)

model_lda

predict(model_lda,data.frame("HUM"=c(140,145), "ULN"=c(145,135)))

model_qda=qda(TYPE~HUM+ULN, data=turkey)

model_qda

predict(model_qda,data.frame("HUM"=c(140,145), "ULN"=c(145,135)))

#머신러닝_신경망분석

install.packages("nnet")

library(nnet)

cb=read.delim("Hshopping.txt", stringsAsFactors = FALSE)

head(cb,n=20)

str(cb)

cb$성별=as.factor(cb$성별)

cb$출연자=as.factor(cb$출연자)

cb$반품여부=as.factor(cb$반품여부)

str(cb)

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

library(caret)

set.seed(1)

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

cb.train=cb[inTrain,]

cb.test=cb[-inTrain,]

set.seed(1234567)

nn_model=nnet(반품여부~성별+나이+구매금액+출연자, 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$반품여부)