setwd(‘C:/Users/manish/Desktop/dressdata’)

**#load data**

train <- read.csv(‘Train_Old.csv’)

**#create training and validation data from given data**

install.packages(‘caTools’)

library(caTools)

set.seed(88)

split <- sample.split(train$Recommended, SplitRatio = 0.75)

**#get training and test data**

dresstrain <- subset(train, split == TRUE)

dresstest <- subset(train, split == FALSE)

**#logistic regression model**

model <- glm (Recommended ~ .-ID, data = dresstrain, family = binomial)

summary(model)

predict <- predict(model, type = ‘response’)

**#confusion matrix**

table(dresstrain$Recommended, predict > 0.5)

**#ROCR Curve**

library(ROCR)

ROCRpred <- prediction(predict, dresstrain$Recommended)

ROCRperf <- performance(ROCRpred, ‘tpr’,’fpr’)

plot(ROCRperf, colorize = TRUE, text.adj = c(-0.2,1.7))

**#plot glm**

library(ggplot2)

ggplot(dresstrain, aes(x=Rating, y=Recommended)) + geom_point() +

stat_smooth(method=”glm”, family=”binomial”, se=FALSE)