R Notebook

library(ISLR)
library(tidyverse)

names(Smarket)
summary(Smarket)
cor(Smarket[,-9])

Logisitc Regression

glm.fit<- glm(Direction~Lag1+Lag2+Lag3+Lag4+Lag5+Volume,data=Smarket,family="binomial")

summary(glm.fit)

coef(glm.fit)

summary(glm.fit)$coef[,4]

glm.probs<- predict(glm.fit,type="response")
glm.probs[1:10]

contrasts(Smarket$Direction)

glm.pred<- rep("Down",1250)

glm.pred[glm.probs>0.5]="Up"

table(glm.pred,Smarket$Direction)

Linear Discriminant Analysis

train<- Smarket%>%filter(Year<2005)
test<-  Smarket%>%filter(Year==2005)

library(MASS)
lda.fit<- lda(Direction~Lag1+Lag2,data=train)
lda.pred<- predict(lda.fit,newdata=test)
names(lda.pred)

lda.class<- lda.pred$class

lda.class

table(lda.class,test$Direction)

predict(lda.fit,newdata =data.frame(Lag1=c(1.2,1.5),Lag2=c(1.1,-0.8)),type="response")

Quadratic Discriminant Analysis

qda.fit<- qda(Direction~Lag1+Lag2,data=train)

qda.fit

qda.class<- predict(qda.fit,test)$class

table(qda.class,test$Direction)

mean(qda.class==test$Direction)

K-nearest Neighbors

library(dplyr)
library(class)

train<- Smarket%>%filter(Year<2005)
test<-  Smarket%>%filter(Year==2005)

train.X <-train%>% dplyr::select(Lag1, Lag2)
test.X <-test%>% dplyr::select(Lag1,Lag2)

train.Direction<- train$Direction

set.seed(1)

knn.pred<- knn(train.X,test.X,train.Direction,k=1)


table(knn.pred,test$Direction)

        
knn.pred Down Up
    Down   43 58
    Up     68 83

mean(knn.pred==test$Direction)

[1] 0.5