Logistic regression, or logit regression is a regression model where the dependent variable is categorical. I have provided code below to perform end-to-end logistic regression in R including data preprocessing, training and evaluation. The dataset used can be downloaded from here.

# Topics Covered
# 
# 1. Reading data and Summary Statistics
# 2. Outlier Detection
# 3. Missing Value Treatment
# 4. Correlation and VIF
# 5. Feature Selection Using IV 
# 6. Creating Training and validation Sets
# 7. Running the Logistic Model on Training Set
# 8. Evaluating Performance on Validation Set
# a. ROC and AUC
# b. Confusion Matrix
# c. KS Statistic
# 9. Scoring the test data

##############################################################################
#Reading data and Summary Statistics

#change the working directory
setwd("C:\\Desktop\\Classification & Clustering")

train_data<-read.csv("data/train.csv")
test_data<-read.csv("data/test.csv")

#Summary Statistics
library(Hmisc)

head(train_data)
str(train_data)
summary(train_data)
describe(train_data)

head(test_data)
str(test_data)
summary(test_data)
describe(test_data)

# 2-way contingency tables
xtabs(~admit + prestige, data = train_data)

##############################################################################
# Outlier Detection

sapply(train_data[,1:3], function(x) quantile(x, c(.01,.05,.25,.5,.75,.90,.95, .99, 1),na.rm=TRUE) )

#gpa of 6.5 seems to be an outlier
train_data$gpa[train_data$gpa > 4] <- 4

##############################################################################
# Missing Value Imputation

sapply(train_data, function(x) sum(is.na(x)) )
train_data$gre[is.na(train_data$gre)] <- mean(train_data$gre, na.rm=TRUE)

train_data2<-train_data

sapply(train_data2, function(x) train_data2[,x][is.na(train_data2[,x])]<- mean(train_data2[,x], na.rm=TRUE))
##############################################################################
# Correlation and VIF

cor(train_data[,1:3])

library(usdm)
vif(train_data[,1:3])

##############################################################################
# Information Value

library(plyr)
library(sqldf)
library(rpart)

source("C:\\xyz.R")

file.sources = list.files("others", full.names=TRUE)
sapply(file.sources,source,.GlobalEnv)

data <- train_data
data$admit <- factor(data$admit, levels= c("1","0"))
levels(data$admit)

str(data)
iv.mult(data, y="admit", vars=c("gre","gpa","prestige"), summary="TRUE")

##############################################################################
# Create training and validation sets

set.seed(123)
smp_size <- floor(0.7 * nrow(train_data))

train_ind <- sample(seq_len(nrow(train_data)), size = smp_size)

training <- train_data[train_ind, ]
validation <- train_data[-train_ind, ]

##############################################################################
# Running the Logistic Model on Training set

?lm
?describe
?glm

admit ~ gre + gpa + prestige

mylogit <- glm(admit ~ gre + gpa + prestige, data = training, family = "binomial")

mylogit2 <- glm(admit ~ gpa + prestige, data = training, family = "binomial")

summary(mylogit2)
# See how prestige has been used as a dummy variable

confint(mylogit, level=.90)

# Caluclating Concordance
# Refer to the blog here to see about Concordance
# http://shashiasrblog.blogspot.in/2014/02/binary-logistic-regression-fast.html

fastConc<-function(model){
 # Get all actual observations and their fitted values into a frame
 fitted<-data.frame(cbind(model$y,model$fitted.values))
 colnames(fitted)<-c('respvar','score')
 # Subset only ones
 ones<-fitted[fitted[,1]==1,]
 # Subset only zeros
 zeros<-fitted[fitted[,1]==0,]

 # Initialise all the values
 pairs_tested<-nrow(ones)*nrow(zeros)
 conc<-0
 disc<-0

 # Get the values in a for-loop
 for(i in 1:nrow(ones))
 {
 conc<-conc + sum(ones[i,"score"]>zeros[,"score"])
 disc<-disc + sum(ones[i,"score"]<zeros[,"score"])
 }
 # Calculate concordance, discordance and ties
 concordance<-conc/pairs_tested
 discordance<-disc/pairs_tested
 ties_perc<-(1-concordance-discordance)
 return(list("Concordance"=concordance,
 "Discordance"=discordance,
 "Tied"=ties_perc,
 "Pairs"=pairs_tested))
}

fastConc(mylogit)
##############################################################################
#Check Performance on the Validation Set

val <-predict(mylogit, validation, type="response")

mydf <-cbind(validation,val)

mydf$response <- as.factor(ifelse(mydf$val>0.5, 1, 0))

library(ROCR)
logit_scores <- prediction(predictions=mydf$val, labels=mydf$admit)

#PLOT ROC CURVE
logit_perf <- performance(logit_scores, "tpr", "fpr")
plot(logit_perf,col = "darkblue",lwd=2,xaxs="i",yaxs="i",tck=NA, main="ROC Curve")
box()
abline(0,1, lty = 300, col = "green")
grid(col="aquamarine")

### AREA UNDER THE CURVE
logit_auc <- performance(logit_scores, "auc")
as.numeric([email protected]) ##AUC Value

#CONFUSION MATRIX
library(caret)
confusionMatrix(mydf$response,mydf$admit)

### KS STATISTIC
logit_ks <- max([email protected][[1]][email protected][[1]])
logit_ks

## LIFT CHART
lift.obj <- performance(logit_scores, measure="lift", x.measure="rpp")
plot(lift.obj,
 main="Lift Chart",
 xlab="% Populations",
 ylab="Lift",
 col="blue")
abline(1,0,col="grey")

#GAINS TABLE
#install.packages("gains")
library(gains)
# gains table
gains.cross <- gains(actual=mydf$admit , predicted=mydf$val, groups=10)
print(gains.cross)

##############################################################################
#Scoring the Test Data using the model we just created

pred <- predict(mylogit, test_data, type="response") 
final <- cbind(test_data,pred)

write.csv(final,"final_probs.csv")

##############################################################################
#REFERENCE MATERIAL

## http://www.ats.ucla.edu/stat/r/dae/logit.htm
## http://www.unc.edu/courses/2010fall/ecol/563/001/notes/lecture21%20Rcode.html
## Caret Package: http://topepo.github.io/caret/
## http://www.r-bloggers.com/gini-index-and-lorenz-curve-with-r/