Accuracy improvement for logistic regression model

Question

I have achieved 68% accuracy with my logistic regression model. I want to increase the accuracy of the model. How can I apply stepwise regression in this code and how beneficial it would be for my model? What changes shall I make in my code to get more accuracy with my data set. I have attached my dataset below. Following is my code:

library(dplyr)
data1 <- read.csv("~/hj.csv", header=T)
train<- data1[1:116,]
VALUE<-as.numeric(rownames(train))
testset<- data1[1:116,]
mylogit <- glm(VALUE ~ POINT1 + POINT2 + POINT3 + POINT4 , data = data1, family ="binomial")
testset$predicted.value = predict(mylogit, newdata = testset, type="response")
for (i in 1: nrow(testset)){
  if(testset$predicted.value[i] <= 0.50)
    testset$outcome[i] <- 0 
  else testset$outcome[i] <- 1
}
print(testset)
tab = table(testset$VALUE, testset$outcome) %>% as.matrix.data.frame()
accuracy = sum(diag(tab))/sum(tab)
print(accuracy)
print(tab)
table(testset$VALUE, testset$outcome)

Following is my dataset: Link 1: http://www.filedropper.com/hj_2

Answer 1

Try:

mylogit <- glm(VALUE ~ POINT1 * POINT2 * POINT3 * POINT4, data = data1, family ="binomial")

with about 72% accuracy.

Answer 2

Try all possible combinations of interaction terms. Have you checked co-linearity of variables? Have you checked all variables interaction? Why do you stick to LR? Try Random Forest also. See what gives u best accuracy on k-fold validation.