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##### How to implement Logistic regression in R?
###### Description

To implement the Logistic regression using R programming.

###### Process

Step 1 : Import the data

Step 2 : Check Correlation

Step 3 : Splitting the data set into train and test

Step 4 : Create a relationship model for the train data using glm() function in R

Step 5 : Summary of the linear model using summary() function

Step 6 : Predicting the dependent variable for the test data using predict() function.

Step 7 : Plot the confusion matrix.

###### Sapmle Code

#Logistic Linear Regression Model
#Get and Set Working Directory
print(getwd())
setwd(“/home/soft13”)
getwd()

#install.packages(“xlsx”)
library(“xlsx”)
my_data<-mtcars[,c(“am”,”wt”)]
View(my_data)

#Correlation Test
cor.test(my_data\$am,my_data\$wt,method=”pearson”)

#Check class bias
table(my_data\$am)

#Splitting into train and test data manually
#Train data
train_one<-my_data[which(my_data\$am==1),]
train_zero<-my_data[which(my_data\$am==0),]
train_1<-sample(1:nrow(train_one), 0.7*nrow(train_one))
train_0<-sample(1:nrow(train_zero), 0.7*nrow(train_zero))
training_1<-train_one[train_1,]
training_0<-train_zero[train_0,]
train<-rbind(training_1,training_0)

#Test data
testing_1<-train_one[-train_1,]
testing_0<-train_zero[-train_0,]
test<-rbind(testing_1,testing_0)

#Logistic Regression
linear_model<-glm(am ~ wt,data = train,family=”binomial”)
print(linear_model)
summary(linear_model)

#Prediction
pp<-plogis(predict(linear_model,test))
print(round(pp,digits = 3))

#Optimal Cut-off
#install.packages(“InformationValue”)
library(“InformationValue”)
optCutOff<-optimalCutoff(test\$am,pp)
print(optCutOff)

#Sensitivity
sensitivity(test\$am, pp)

#Specificity
specificity(test\$am, pp,threshold = optCutOff)

#ConfusionMatrix
confusionMatrix(test\$am,pp)