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- ###########################################################################
- ############### Logistic regression ###########
- setwd("E:/R (Data Science)/excel files")
- data <- read.csv("binary.csv")
- str(data)
- data$admit <- as.factor(data$admit)
- data$rank <- as.factor(data$rank)
- ############ training and testing data #############
- set.seed(123)
- id <- sample(2,nrow(data),replace = T,prob = c(.8,.2))
- train <- data[id == 1, ]
- test <- data[id ==2,]
- ##### Model ######
- model <-glm(admit ~ gpa+rank,train,family = "binomial")
- summary(model)
- # prediction training dataset
- p1<-predict(model,train,type="response")
- head(p1)
- head(train)
- # misclassification train
- pred1 <- ifelse(p1>.5,1,0)
- tab1 <- table(prediction=pred1,actual=train$admit)
- tab1
- sum(diag(tab1))/sum(tab1)
- # misclassification on test dataset
- p2 <-predict(model,test,type="response")
- head(p2)
- head(test)
- pred2 <- ifelse(p2>.5,1,0)
- tab2 <- table(predication =pred2,actual = test$admit)
- tab2
- sum(diag(tab2))/sum(tab2)
- ####################### K-Nearest Neighbour (KNN) #####################3
- data <- read.csv("Prostate_Cancer.csv")
- data <- data[,-1]
- str(data)
- t <-table(data$diagnosis_result)
- pie(t)
- # Normalisation
- normalise <- function(x){
- return( (x - min(x)) / (max(x)- min(x))) ### 0-1
- }
- data_n <- as.data.frame(lapply(data[2:9], normalise))
- ### training and test set
- train <- data_n[1:70,]
- test <- data_n[71:100,]
- ### train and test labels
- train_lab <- data[1:70,1]
- test_lab <- data[71:100,1]
- table(test_lab)
- ### Knn
- install.packages("class")
- library(class)
- k <- sqrt(nrow(data)) #### K is determined by square root of no . of rows
- model <- knn(train = train,test = test,cl = train_lab, k= 9)
- table(model)
- t1 <- table(Prediction =model, Actual=test_lab)
- sum(diag(t1))/sum(t1)
- ############################################################################
- ################# Multinomial Logistic regression ######################
- data <- read.csv("Cardiotocographic.csv")
- str(data)
- # factorise the NSP
- data$NSP <- as.factor(data$NSP)
- # train and test dataset
- id <- sample(2,nrow(data),replace = T,prob = c(.8,.2))
- train <- data[id ==1,]
- test <- data[id==2,]
- # Model Multinomial Logistic Regression
- install.packages("nnet")
- library(nnet)
- train$NSP <- relevel(train$NSP,ref = "1")
- model <- multinom(NSP ~.,train)
- # prediction of training data
- p <- predict(model,train)
- tab <- table(p,train$NSP)
- sum(diag(tab))/sum(tab)
- # prediction of testing set
- p1 <- predict(model,test)
- tab1 <- table(p1,test$NSP)
- sum(diag(tab1))/sum(tab1)
- n <- table(train$NSP)
- n/sum(n)
- tab/colSums(tab)
- n1 <- table(test$NSP)
- n1/sum(n1)
- tab1/colSums(tab1)
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