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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

##############################################################################

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

#Summary Statistics
library(Hmisc)

str(train_data)
summary(train_data)
describe(train_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

str(data)

##############################################################################
# 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"]0.5, 1, 0))

library(ROCR)

#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)

### 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/
## https://www.r-bloggers.com/gini-index-and-lorenz-curve-with-r/
```