This document contains code for analyzing several datasets using regression and classification models in R. For the PISA dataset, it performs linear regression to predict reading scores and evaluates the model. For an automotive dataset, it builds linear and polynomial regression models to relate sales to economic factors. It also explores logistic regression models for healthcare and loan default datasets.

1. #part1
pisaTrain = read.csv("pisa2009train.csv")
pisaTest = read.csv("pisa2009test.csv")
str(pisaTrain)
summary(pisaTrain)
pairs(pisaTrain[,18:24])
#average readinScore per gender
tapply(pisaTrain$readingScore, pisaTrain$male, mean)
#missing variables
summary(pisaTrain)
pisaTrain = na.omit(pisaTrain)
pisaTest = na.omit(pisaTest)
summary(pisaTrain)
summary(pisaTest)
plot(pisaTest$raceeth)
str(pisaTest$raceeth)
pisaTrain$raceeth = relevel(pisaTrain$raceeth, "White")
pisaTest$raceeth = relevel(pisaTest$raceeth, "White")
lmScore = lm(readingScore~., data=pisaTrain)
summary(lmScore)
SSE = sum(lmScore$residuals^2)

2. RMSE =sqrt(mean(lmScore$residuals^2))
predTest = predict(lmScore, newdata=pisaTest)
summary(predTest)
rmse=sqrt(mean((predTest-pisaTest$readingScore)^2))
SSE=sum((predTest-pisaTest$readingScore)^2)
baseline = mean(pisaTrain$readingScore)
SST=sum((baseline-pisaTest$readingScore)^2)
r2=1-SSE/SST
plot(lmScore)
hist(pisaTrain$readingScore)
#part2
Elantra = read.csv("elantra.csv")
fix(Elantra)
pairs(Elantra)
cor(Elantra)
library(car)
fit <- lm(ElantraSales~., data=Elantra)
vif(fit)
# Global test of model assumptions
install.packages("gvlma")
library(gvlma)
gvmodel <- gvlma(fit)
summary(gvmodel)

3. ElantraTrain = subset(Elantra, Year <= 2012)
ElantraTest = subset(Elantra, Year > 2012)
ElantraLM = lm(ElantraSales ~ Unemployment + Queries + CPI_energy + CPI_all, data=ElantraTrain)
summary(ElantraLM)
ElantraLM = lm(ElantraSales ~ Unemployment + Queries + CPI_energy + CPI_all + Month,
data=ElantraTrain)
summary(ElantraLM)
ElantraTrain$MonthFactor = as.factor(ElantraTrain$Month)
ElantraTest$MonthFactor = as.factor(ElantraTest$Month)
ElantraLM = lm(ElantraSales ~ Unemployment + Queries + CPI_energy + CPI_all + MonthFactor,
data=ElantraTrain)
summary(ElantraLM)
cor(ElantraTrain[c("Unemployment","Month","Queries","CPI_energy","CPI_all")])
PredictTest = predict(ElantraLM, newdata=ElantraTest)
SSE = sum((PredictTest - ElantraTest$ElantraSales)^2)
SST = sum((mean(ElantraTrain$ElantraSales) - ElantraTest$ElantraSales)^2)
#part 3
mod1=lm(medv~lstat*age,data = Boston)
summary(mod1)
mod2=lm(medv~lstat+age+lstat*age,data = Boston)
summary(mod2)

4. mod3=lm(medv~lstat:age,data = Boston)
summary(mod3)
#part4 Nonlinear Relationship
install.packages("ILSR")
library(ISLR)
?Auto
pairs(Auto)
attach(Auto)
plot(horsepower,mpg)
summary(lm(mpg~horsepower))
par(mfrow=c(2,2))
plot(lm(mpg~horsepower))
summary(lm(mpg~horsepower+I(horsepower^2)))
plot(lm(mpg~horsepower+I(horsepower^2)))
summary(lm(mpg~poly(horsepower,7)))
plot(lm(mpg~poly(horsepower,7)))
#cross validation
install.packages("boot")
library(boot)
set.seed(2017)

5. cv.error.10=rep(0,10)
for (i in 1:10){
glm.fit=glm(mpg~poly(horsepower,i),data=Auto)
cv.error.10[i]=cv.glm(Auto,glm.fit,K=10)$delta[1]
}
cv.error.10
par(mfrow=c(1,1))
plot(seq(1:10),cv.error.10,type = "l")
#logit
qual=read.csv("quality.csv")
fix(qual)
summary(qual)
str(qual)
pairs(qual)
hist(qual$PoorCare)
table(qual$PoorCare)
install.packages("caTools")
library(caTools)
split=sample.split(qual$PoorCare,SplitRatio = 0.8)
split
qTrain=subset(qual,split==TRUE)
qTest=subset(qual,split==FALSE)

6. plot(qTrain$OfficeVisits,qTrain$Narcotics,col=qTrain$PoorCare+1)
logitmodel=glm(PoorCare ~OfficeVisits+Narcotics,data=qTrain,family=binomial)
summary(logitmodel)
logitmodel1=glm(PoorCare ~.,data=qTrain,family=binomial)
summary(logitmodel1)
predTrain=predict(logitmodel,type = "response")
hist(predTrain)
tapply(predTrain,qTrain$PoorCare,mean)
t(table(qTrain$PoorCare,predTrain>0.5))
t(table(qTrain$PoorCare,predTrain>0.1))
t(table(qTrain$PoorCare,predTrain>0.07))
install.packages("ROCR")
library(ROCR)
ROCpredict=prediction(predTrain,qTrain$PoorCare)
ROCperf=performance(ROCpredict,"tpr","fpr")
plot(ROCperf)
plot(ROCperf,print.cutoffs.at=c(0,25,0.5,0.75))
auc.perf = performance(ROCpredict, measure = "auc")
auc.perf@y.values
plot(qTrain$OfficeVisits,qTrain$Narcotics,col=qTrain$PoorCare+1)
#build decision boundary

7. coefs = coef(logitmodel)
x = c(0,40)
y = c((-1/coefs[3]) * (coefs[2] * x + coefs[1]))
lines(x, y, col="black", lwd=2)
predTst = predict(logitmodel,type="response",newdata = qTest)
t(table(qTest$PoorCare,predTst>0.5))
#Framingham
Fdata = read.csv("framingham.csv")
str(Fdata)
library(caTools)
set.seed(2018)
split=sample.split(Fdata$TenYearCHD,SplitRatio = 0.8)
Ftrain=subset(Fdata,split==TRUE)
Ftest=subset(Fdata,split==FALSE)
table(Ftrain$TenYearCHD)
table(Ftest$TenYearCHD)
frLogitMod=glm(TenYearCHD~.,data=Ftrain)
summary(frLogitMod)
predTst = predict(frLogitMod,type="response",newdata = Ftest)
t(table(Ftest$TenYearCHD,predTst>0.5 ))
#roc for test
ROCpredict=prediction(predTst,Ftest$TenYearCHD)

8. ROCperf=performance(ROCpredict,"tpr","fpr")
plot(ROCperf)
auc.perf = performance(ROCpredict, measure = "auc")
auc.perf@y.values
#loans
Dloans=read.csv("loans.csv")
fix(Dloans)
table(Dloans$credit.policy)
str(Dloans)
#identify Na variable
summary(Dloans)
missing = subset(Dloans, is.na(log.annual.inc) | is.na(days.with.cr.line) | is.na(revol.util) |
is.na(inq.last.6mths) | is.na(delinq.2yrs) | is.na(pub.rec))
nrow(missing)
library(caTools)
set.seed(2018)
split=sample.split(Dloans$not.fully.paid,SplitRatio = 0.8)
Dtrain=subset(Dloans,split==TRUE)
Dtest=subset(Dloans,split==FALSE)
#imputation
install.packages("mice")
library(mice)

9. #train imputation
vars.for.imputation = setdiff(names(Dtrain), "not.fully.paid")
imputed = complete(mice(Dtrain[vars.for.imputation]))
Dtrain[vars.for.imputation] = imputed
summary(Dtrain)
#test imputation
vars.for.imputation = setdiff(names(Dtest), "not.fully.paid")
imputed = complete(mice(Dtest[vars.for.imputation]))
Dtest[vars.for.imputation] = imputed
summary(Dtest)
#prediction
mod1 = glm(not.fully.paid~., data=Dtrain, family="binomial")
summary(mod1)
#prediction
Dtest$predicted.risk = predict(mod1, newdata=Dtest, type="response")
t(table(Dtest$not.fully.paid, Dtest$predicted.risk > 0.5))
#ROC
library(ROCR)
pred = prediction(Dtest$predicted.risk, Dtest$not.fully.paid)
as.numeric(performance(pred, "auc")@y.values)
ROCperf=performance(pred,"tpr","fpr")
plot(ROCperf)